System

Abstract

A system is provided.SOLUTION: A system comprising: means for periodically publishing digital information via the Internet; means for connecting users and suppliers; means for leveraging existing users to disseminate information; means for using generative artificial intelligence to discover new products; and means for using the generative artificial intelligence to develop new customers.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 online shopping, information is often distributed only to a limited number of users, making it difficult to acquire new customers and discover new products. In addition, the spread of product information is also dependent on a small number of users, so the spread of information is limited. This hinders the vitalization of distribution on online shopping sites, which is an issue. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes a means for periodically issuing digital information via the Internet, a means for connecting users with suppliers, a means for utilizing existing users to spread information, a means for discovering new products using generative AI, and a means for cultivating new customers using generative AI. This promotes the spread of information by existing users, and by efficiently discovering new products and new customers, it is possible to revitalize distribution on online shopping sites.

[0006] The "Internet" is a global network and communications infrastructure that enables computers and other devices to send and receive information.

[0007] "Digital information" refers to data or content stored, processed, or transmitted in electronic form, including text, images, audio, and video.

[0008] "User" refers to an individual or corporation that uses an online shopping site or digital information.

[0009] "Supplier" refers to a company or individual that provides goods or services and sells products through an online shopping site.

[0010] "Generative artificial intelligence" refers to algorithms and systems that analyze large amounts of data and automatically generate new information and predictions.

[0011] "Product discovery" refers to the act of finding new products that have potential demand in the market.

[0012] "Developing new customers" means finding new customers with whom you do not yet have business and building business relationships.

[0013] "Disseminating information" refers to the widespread dissemination of specific information to other people or groups. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence to discover new products and cultivate new customers. A specific example of this system is shown below.

[0036] Creation and publication of digital bulletin boards

[0037] server

[0038] First, the server collects past purchase history data and current market trend information. This data is input into a generation AI, which generates new product information and feature articles based on the purchase data and trend information. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[0039] Digital Newspaper Distribution

[0040] server

[0041] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0042] Information dissemination by customers

[0043] User

[0044] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[0045] Developing new customers

[0046] server

[0047] Generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. This data is obtained through the analysis of social media and access logs. New marketing measures are implemented targeting the potential customers identified from the analysis results. For example, advertising emails with exclusive coupons can be sent to these potential customers to encourage new registrations and purchases.

[0048] Specific examples

[0049] 1. Creation and publication of digital bulletin boards

[0050] Server: The AI ​​generator creates a digital bulletin board containing summer sales information and articles introducing popular products. For example, a special feature on "Recommended Products for August" will feature clothing and accessories that are in high demand as the seasons change.

[0051] 2. Digital Newspaper Distribution

[0052] Server: The digital bulletin board generated on August 1st will be sent to 10,000 existing customers at once. The delivery method will be email and push notification.

[0053] 3. Customers spread information

[0054] User: Views a digital newsletter received via email and shares a specific page on Facebook, commenting, "Perfect items for this summer!"

[0055] 4. Developing new customers

[0056] Server: By analyzing information spread on social media, 500 new potential customers are identified. By sending advertisements with exclusive coupons to these potential customers, 50 new customers will sign up.

[0057] As described above, the present invention utilizes digital bulletin boards to promote the dissemination of information by existing users and efficiently realize the discovery of new products and new customers, thereby revitalizing the distribution of online shopping sites.

[0058] The processing flow will be explained below.

[0059] Creation and publication of digital bulletin boards

[0060] server

[0061] Step 1:

[0062] Retrieve historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[0063] Step 2:

[0064] Use web scrapers to gather trending information and news articles. Use scraping tools to get the latest market trends and new product information.

[0065] Step 3:

[0066] Preprocessing acquired data and converting it into a specified format, filtering out unnecessary data, and extracting and organizing necessary information.

[0067] Step 4:

[0068] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[0069] Step 5:

[0070] The generated content is embedded into an HTML / CSS template. The text and images created by the generative AI are placed in the design template to create a digital bulletin board.

[0071] Step 6:

[0072] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[0073] Digital Newspaper Distribution

[0074] server

[0075] Step 1:

[0076] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[0077] Step 2:

[0078] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[0079] Step 3:

[0080] Use a template engine to embed the digital kawaraban into the email template. Use a template engine such as Jinja2 to dynamically generate the email body.

[0081] Step 4:

[0082] The generated email is sent to the mail server for mass distribution, and the digital bulletin is sent to the customer via the SMTP server.

[0083] Information dissemination by customers

[0084] User

[0085] Step 1:

[0086] The user opens the digital bulletin board received via email or app push notification. The user checks the notification in their email client or dedicated app.

[0087] Step 2:

[0088] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[0089] Step 3:

[0090] Click the "Share" button in the news bulletin board. The share button is implemented in JavaScript, and the SNS sharing dialog opens.

[0091] Step 4:

[0092] Share the information from the digital bulletin on social media. Post links and comments using your Facebook, Twitter, or other accounts.

[0093] Developing new customers

[0094] server

[0095] Step 1:

[0096] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[0097] Step 2:

[0098] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[0099] Step 3:

[0100] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[0101] Step 4:

[0102] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[0103] The above are the specific processing steps for carrying out the invention.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In conventional online shopping systems, the connection between users and suppliers is weak, making it difficult to efficiently discover new products and cultivate new customers. Furthermore, there is a lack of effective ways to utilize information dissemination by existing users, which means that product information remains limited. This creates the problem of hindering the vitalization of distribution on online shopping sites.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes means for automatically collecting purchase history data and market trend information and inputting it into a generating AI, means for generating product information and feature articles based on the purchase data and trend information using the generating AI, and means for creating digital information by embedding the generated content into design templates, which effectively connects users and suppliers, making it possible to spread information and develop new customers.

[0109] "Purchase history data" is data that includes detailed information about products that a user has purchased in the past.

[0110] "Market trend information" is data that indicates consumer interests and purchasing trends in the market.

[0111] "Generative AI" is software that has the ability to analyze data using machine learning algorithms and generate new information.

[0112] A "generative AI model" is an artificial intelligence model that generates new text and content based on diverse data.

[0113] A "design template" is a template that defines the layout and style for creating web pages and digital content.

[0114] "Digital information" refers generally to content that is electronically generated and distributed.

[0115] A "mail server" is a server that manages the sending and receiving of e-mail.

[0116] A "push notification server" is a server that sends notifications to mobile and web applications in real time.

[0117] "User" refers to a consumer who uses an online shopping site.

[0118] "Social media" refers to platforms where people share information online, such as Facebook and Twitter.

[0119] A "new customer list" is a list of potential new customers to target.

[0120] "Marketing strategies" refer to the planning and execution of advertising and promotions targeted at specific customer segments.

[0121] "Information diffusion" refers to the dissemination of information received by existing users to other users or networks.

[0122] This invention is a system for effectively connecting users and suppliers on online shopping sites, and in particular, it utilizes purchase history data and market trend information to periodically publish digital information, and uses generative artificial intelligence to discover new products and cultivate new customers. Specific implementation methods of this system are described below.

[0123] Creation and publication of digital bulletin boards

[0124] server:

[0125] The server first retrieves the user's purchase history data from a purchase history database. This database contains detailed information about the products the user has purchased in the past. In addition, it sends a request to an API (e.g., Google Trends) to obtain market trend information. The data collected in this way is input into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt sentence that can be used is as follows:

[0126] "Please create an article featuring new products for August based on the following data."

[0127] The generative AI model generates new product information and feature articles based on this data. This content is embedded in a design template (HTML / CSS) on the server and formatted as a digital bulletin board.

[0128] Scheduling and preparing for publication of the digital bulletin

[0129] server:

[0130] The server uses a scheduling function to set the publication date for the digital bulletin (for example, the beginning of the month or a specific day of the week). As the publication date approaches, the server automatically prepares for publication. Specifically, it retrieves the target customer list from the database and uses a mail server (for example, SendGrid) or push notification server (for example, Firebase Cloud Messaging) to simultaneously distribute the digital bulletin.

[0131] Information dissemination by customers

[0132] User:

[0133] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button that users can click to share the information on social media (e.g., Facebook or Twitter). This sharing function is expected to help the content of the digital bulletin reach many potential customers.

[0134] Developing new customers

[0135] server:

[0136] The server uses social media APIs and web analytics tools (e.g., Google Analytics) to collect and analyze responses to the spread of information. This data is input into a generative AI model, which creates a list of potential customers based on the responses and behavioral history of new users. Based on this list of new customers, the server implements new marketing initiatives. For example, it sends advertising emails with exclusive coupons to newly identified potential customers to encourage new registrations and purchases.

[0137] Specific examples

[0138] As an example, a digital bulletin is generated containing summer sale information and articles introducing popular products. For example, a "Featured August Products" section may include articles featuring clothing and accessories, which are in high demand as the seasons change. This digital bulletin is distributed simultaneously to 10,000 existing customers via email and push notifications on August 1st, the beginning of the month. In addition, when users share the digital bulletin they receive on social media, 500 new potential customers are identified, and advertising emails with exclusive coupons are sent to these customers. This will result in the registration of 50 new customers, revitalizing the distribution of the online shopping site.

[0139] The above is a specific embodiment for carrying out the present invention. This system realizes efficient product discovery and new customer acquisition, and can promote the revitalization of distribution on online shopping sites.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Specific processing steps from collecting purchase history data and market trend information to publishing a digital bulletin board

[0142] Step 1:

[0143] collection

[0144] Server: Sends SQL queries to the purchase history database to retrieve past purchase history data, and sends requests to the market trend information API to collect current market trend data.

[0145] Input: Purchase history database, trend information API request

[0146] Data processing: Execute SQL queries, send API requests

[0147] Output: Purchase history data, trend information

[0148] Step 2:

[0149] Data Entry and Content Generation

[0150] Server: The acquired purchase history data and market trend information are input into the generative AI model, which generates new product information and feature articles based on this data.

[0151] Input: Purchase history data, market trend information, prompt text

[0152] Data calculation: Input data into generative AI models, and generate content using the models

[0153] Output: Generated product information, featured articles

[0154] Specific operation: Send a request to the API of a generative AI model (e.g., OpenAI GPT-3), enter a prompt, and generate content.

[0155] Step 3:

[0156] Creating digital news bulletins

[0157] Server: The generated new product information and feature articles are embedded into HTML / CSS design templates, which creates digital bulletin boards.

[0158] Input: Generated product information, feature articles, design templates

[0159] Data processing: Inserting content into HTML / CSS templates

[0160] Output: Digital woodblock print

[0161] What it does: Uses a template engine to insert generated content into an HTML file and style it.

[0162] Step 4:

[0163] Scheduling and publication preparation

[0164] Server: The publication date of the digital bulletin is set using the scheduling function. As the publication date approaches, the target customer list is retrieved from the database and the digital bulletin is embedded in the email template.

[0165] Inputs: Digital bulletin, scheduling, customer list

[0166] Data processing: Inserting digital bulletin boards into email templates, acquiring customer lists

[0167] Output: Email template for delivery

[0168] Specific operations: Use a scheduler program to set a specific date and time, retrieve a customer list using an SQL query, and insert a digital bulletin board into a template.

[0169] Step 5:

[0170] Digital Newspaper Distribution

[0171] Server: Using a mail server or push notification server, email templates are sent to existing customers en masse.

[0172] Input: Email template for delivery, customer list

[0173] Data calculation: Sending email / push notifications

[0174] Output: Distributed digital bulletin board

[0175] Specific operation: Sends a request to a mail server (e.g., SendGrid) or push notification server (e.g., Firebase Cloud Messaging) and executes delivery.

[0176] Step 6:

[0177] Information dissemination by customers

[0178] User: Opens the digital bulletin they receive and spreads the information by clicking the share button on social media.

[0179] Input: Received digital bulletin board

[0180] Data processing: Sharing information on social media

[0181] Output: Information shared on social media

[0182] Specific actions: Click the share button in the digital news bulletin and share the information on the social media posting screen.

[0183] Step 7:

[0184] Developing new customers

[0185] Server: Collects and analyzes responses to the information spread using social media APIs and web analytics tools, and creates a list of new customers. Marketing strategies are implemented based on this list.

[0186] Input: Social media response data, access log data

[0187] Data calculation: Analysis of reaction data, creation of new customer list

[0188] Output: New customer list, marketing measures

[0189] What it does: Analyze data using a generative AI model and send promotional emails with exclusive coupons to newly identified potential customers.

[0190] These are the specific processing steps of the program for this system. At each step, appropriate data processing and calculations are performed based on the input data, and an output is generated as a result.

[0191] (Application example 1)

[0192] 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."

[0193] Current online shopping sites lack effective ways to connect users and suppliers. They also lack mechanisms for utilizing existing users to spread information, and methods for using generative artificial intelligence to discover new products and develop new customers. This makes it difficult to revitalize online shopping sites, and there are challenges in anticipating increased sales.

[0194] 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.

[0195] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information using existing users, means for discovering new products using a generating AI, means for acquiring new customers using the generating AI, means for publishing digital bulletins on specific days of the week, and means for embedding generated content into email templates and preparing for simultaneous distribution. This allows online shopping sites to effectively connect users with suppliers, discover new products, and acquire new customers. Furthermore, spreading information through existing users makes it easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0196] The "Internet" refers to a global network of computers that enables the collection, sharing, and communication of information.

[0197] "Periodic" means something that is repeated at regular intervals.

[0198] "Digital information" refers to information that is stored, processed, and transmitted electronically, and includes text, images, audio, and video.

[0199] "User" refers to a customer who uses a service such as an online shopping site.

[0200] "Provider" refers to a company or individual that provides goods or services to Users.

[0201] "Generative artificial intelligence" refers to technology that uses machine learning and deep learning to analyze data and generate new information like a human.

[0202] "New customers" refer to new customers with whom we have not previously conducted business or had contact.

[0203] "Publication" means the regular release of new information.

[0204] An "email template" refers to a template for creating email content according to a certain format.

[0205] "Simultaneous distribution" refers to sending the same information to a large number of users at the same time.

[0206] "Digital Kawaraban" refers to electronic newsletters and magazines generated by online shopping sites.

[0207] "Social media" refers to online platforms that allow people to share information and interact over the internet.

[0208] "Information diffusion" refers to spreading information to many people.

[0209] "New products" refer to products that have been newly added to the existing lineup.

[0210] "Market trends" refers to current market trends, trends, and changes in demand.

[0211] "Limited coupon" refers to a discount coupon that can only be used under certain conditions.

[0212] "Push notifications" refers to the function that forces notifications from applications to be displayed on devices such as smartphones and tablets.

[0213] "Potential Customer" refers to a person who has the potential to become a customer in the future.

[0214] A system embodying the invention includes the following components:

[0215] 1. A means of publishing digital information periodically via the Internet

[0216] The server collects market trend information and user purchasing history data, and uses a generative AI model to generate new product information and feature articles. This generated content is embedded in HTML / CSS design templates and published periodically as a digital bulletin board.

[0217] 2. A means of connecting users and suppliers

[0218] The server uses a generative AI model to analyze users' purchasing patterns and market trends, and selects individual recommended products, providing users with appropriate product information from suppliers.

[0219] 3. Using existing users to spread information

[0220] Digital bulletin boards have share buttons that users can click to easily spread information across social media and other online platforms, allowing a single piece of information to reach many potential customers.

[0221] 4. A means of discovering new products using generative artificial intelligence

[0222] The server inputs market trend information and purchase history data into a generative AI model to discover new products. In this process, large amounts of data are analyzed using machine learning algorithms to narrow down interesting product candidates.

[0223] 5. Means of acquiring new customers using the generative artificial intelligence

[0224] When information spreads on social media, the server analyzes the behavioral history of new customers. This is done by analyzing access logs and SNS data to generate a new list of potential customers. Limited coupons can be distributed to these customers to promote new customers.

[0225] 6. How to publish digital bulletins on specific days of the week

[0226] The server has a schedule management function, for example, to publish a new digital bulletin every Monday. This schedule management is to ensure regular information transmission.

[0227] 7. A way to embed generated content into email templates and prepare them for mass distribution

[0228] The newly generated digital bulletin board is embedded in an email template and prepared for mass distribution. The server sends the email to multiple users simultaneously via an SMTP server.

[0229] Hardware and software used

[0230] Hardware: Cloud server, user devices (smartphones, PCs)

[0231] Software: Generative AI models (e.g., GPT-4), HTML / CSS design templates, SMTP server, SNS integration API

[0232] Specific examples of processing

[0233] The user list includes email addresses and purchase histories of existing customers. Based on this, a generative AI model generates new product information and articles, which are then sent out as digital bulletins every Monday. For example, a bulletin titled "Recommended Fashion for This Fall" might feature new seasonal products.

[0234] Example prompt for a generative AI model:

[0235] "User ID: 1

[0236] Purchase history: ['Autumn jacket', 'Business shoes']

[0237] Market trends: ['Autumn / Winter Fashion', 'New Material Jackets']

[0238] This system allows online shopping sites to effectively connect users with suppliers, discover new products, and develop new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] The server collects user purchasing history and market trend data. The input is the existing user database and market trend database. The server temporarily stores this data in memory to input it into the generative AI model. The output is a dataset to be passed to the generative AI model.

[0242] Step 2:

[0243] The server feeds the dataset into the generative AI model to generate product information and feature articles. The input is collected user purchasing history and market trend data. The generative AI model analyzes the data using machine learning algorithms to generate interesting product information and feature articles. The output is the content of the generated digital bulletin board.

[0244] Step 3:

[0245] The server embeds the generated content into an HTML / CSS design template to create a digital tile-ban. The input is the generated content and the design template. An HTML engine is used to embed the content into the template. The output is an HTML file of the completed digital tile-ban.

[0246] Step 4:

[0247] The server embeds the digital kawaraban in the email template and prepares for mass distribution. The input is the completed HTML file of the digital kawaraban and the email template. The server embeds the digital kawaraban in the email template and prepares to send the email via the SMTP server. The output is the email data that has been prepared for sending.

[0248] Step 5:

[0249] The server distributes digital bulletins on specific days of the week. The input is email data that has been prepared for sending and an existing user list. Using the schedule management function, emails are sent en masse via an SMTP server, for example, every Monday. The output is the digital bulletin distributed to users.

[0250] Step 6:

[0251] Users can share the digital bulletin they receive on social media or other online platforms. The input is the digital bulletin they received and the operation of the share button. When a user clicks the share button, the information can be easily spread through the social media sharing API. The output is the shared link and the extent to which it has been shared.

[0252] Step 7:

[0253] The server analyzes the information spread on social media and analyzes the behavioral history of new users. The input is the access log of the spread link and social media data. This data is analyzed using an analysis engine to generate a list of new customers. The output is a list of newly identified potential customers.

[0254] Step 8:

[0255] The server distributes limited coupons to a new customer list. The input is a newly generated potential customer list and coupon information. An advertising email with a limited coupon is sent to the new customers using an SMTP server. The output is the email with the coupon delivered to the new customer.

[0256] Step 9:

[0257] A user receives a coupon and purchases a product on an online shopping site. The input is the received email with the coupon and the purchase process on the online shopping site. The user uses the coupon to purchase the product on the online shopping site at a discounted price. The output is the completed purchase transaction.

[0258] Through these steps, the system effectively connects users with suppliers, discovers new products, and develops new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0259] 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.

[0260] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence and an emotion engine to discover new products and cultivate new customers. Specific examples and processing steps are shown below.

[0261] Creation and publication of digital bulletin boards

[0262] server

[0263] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[0264] Digital Newspaper Distribution

[0265] server

[0266] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0267] Information dissemination by customers

[0268] User

[0269] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[0270] Developing new customers

[0271] server

[0272] The generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. Furthermore, the emotion engine recognizes the user's emotions and analyzes their feedback to optimize marketing measures.

[0273] Use of emotion engine

[0274] server

[0275] The emotion engine monitors the user's emotional state in real time and analyzes the emotional data. For example, when a user browses a digital newspaper, their facial expressions and reactions are captured via a camera or microphone, and the data is used to determine the user's current emotions. This emotional data is then combined with the results of analysis by generative AI to provide customized product recommendations based on the user's interests.

[0276] Specific examples

[0277] 1. Creation and publication of digital bulletin boards

[0278] Server: Generative AI and an emotion engine create a digital bulletin board containing information about autumn sales and articles introducing popular products. Based on the user's past purchasing history and current emotional data, seasonal products are featured as "September's Recommended Products."

[0279] 2. Digital Newspaper Distribution

[0280] Server: The digital bulletin board generated on September 1st is sent simultaneously to 5,000 existing customers via email and push notification. In addition, the timing of delivery is optimized based on individual sentiment data.

[0281] 3. Customers spread information

[0282] User: Views the digital bulletin received via email and evaluates the products. When sharing a specific page on Instagram, the user adds the comment "This fall's trend! Recommended products featured."

[0283] 4. Developing new customers

[0284] Server: Analyze engagement with the information shared on social media and identify 300 new potential customers. Based on their emotional data, deliver more personalized, exclusive offers, and 50 new sign-ups.

[0285] By incorporating an emotion engine, feedback based on the user's emotional state can be obtained, which can be used to optimize the content and distribution timing of digital bulletins, thereby improving user satisfaction and enabling effective marketing measures.

[0286] The processing flow will be explained below.

[0287] Creation and publication of digital bulletin boards

[0288] server

[0289] Step 1:

[0290] Obtain historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[0291] Step 2:

[0292] Use web scraping tools to gather the latest market trend information and news articles. Scrape and retrieve the required data from the specified website.

[0293] Step 3:

[0294] Preprocess and format the acquired data, filter out unnecessary data, and extract and organize the necessary information.

[0295] Step 4:

[0296] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[0297] Step 5:

[0298] The generated content is embedded into HTML / CSS templates, and text and images generated by AI are embedded in the template engine to create a digital bulletin board.

[0299] Step 6:

[0300] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[0301] Digital Newspaper Distribution

[0302] server

[0303] Step 1:

[0304] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[0305] Step 2:

[0306] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[0307] Step 3:

[0308] Embed the digital bulletin in an email template using a template engine, combining the dynamically generated email body with the digital bulletin.

[0309] Step 4:

[0310] The generated email is sent to the mail server for mass distribution, and then sent to customers via the SMTP server.

[0311] Information dissemination by customers

[0312] User

[0313] Step 1:

[0314] Open the digital bulletin received by email or push notification. Check the notification in your email client or dedicated app.

[0315] Step 2:

[0316] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[0317] Step 3:

[0318] Click the "Share" button in the news bulletin board. The JavaScript-implemented share button will open the social media sharing dialog.

[0319] Step 4:

[0320] Share the information from the digital bulletin on social media, such as Facebook or Twitter, by posting links and comments.

[0321] Developing new customers

[0322] server

[0323] Step 1:

[0324] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[0325] Step 2:

[0326] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[0327] Step 3:

[0328] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[0329] Step 4:

[0330] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[0331] Use of emotion engine

[0332] server

[0333] Step 1:

[0334] The emotion engine recognizes the user's emotional state, capturing facial expressions and voice data through a camera and microphone.

[0335] Step 2:

[0336] The acquired data is analyzed in real time to determine the user's emotional state, and an emotion analysis algorithm is used to classify the emotion into categories such as positive, negative, or neutral.

[0337] Step 3:

[0338] Emotional data is stored and analyzed cumulatively. Emotional data from viewing digital bulletin boards is added to user profiles.

[0339] Step 4:

[0340] Incorporate sentiment data into generative AI and marketing strategies to generate more personalized content and offers based on sentiment data.

[0341] As a specific example, the generative AI and emotion engine will create a digital bulletin containing information about autumn sales, and while monitoring the emotional data of each user, send it to customers at the optimal timing. If a customer who receives the digital bulletin responds positively, the content and timing of the next distribution will be further optimized based on that reaction.

[0342] In this way, the present invention utilizes digital bulletin boards and incorporates user emotional data to discover new products, cultivate new customers, and achieve effective marketing.

[0343] Example 2

[0344] 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."

[0345] Modern online shopping sites require efficient methods to connect users with suppliers and to discover new products and customers. However, existing systems lack the means to provide personalized recommendations that take into account not only users' past purchase history and market trends, but also their emotional feedback. Furthermore, they have yet to effectively leverage existing customers to naturally spread information and attract new customers.

[0346] 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.

[0347] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information by utilizing existing users, means for discovering new products using generative AI, means for cultivating new customers using the generative AI, means for collecting past purchase history data and market trend information and inputting the collected data into a generative AI model and an emotion engine to generate new product information and feature articles, means for embedding the generated digital information into HTML / CSS templates and publishing them at specified times, and means for collecting user emotion feedback using the emotion engine and using the collected data to customize product proposals for the digital information. This enables personalized proposals based on the user's purchase history and emotions, and enables natural information dissemination through existing users and effective acquisition of new customers.

[0348] "Means of publishing digital information periodically via the Internet" refers to the function of a server publishing content generated using HTML / CSS templates at specified times.

[0349] "Means to connect users and suppliers" refers to the function of utilizing generative AI models and emotion engines to provide appropriate product information based on users' purchasing history and market trend information.

[0350] "Means of spreading information by utilizing existing users" refers to the function that allows existing users to use the share buttons within the digital bulletin board to spread information on social media and other networks.

[0351] "Means of discovering new products using generative AI" refers to the function of a generative AI model that analyzes past purchase history data and market trend information to automatically suggest new products.

[0352] "Means of developing new customers using generative AI" refers to the function in which a generative AI model analyzes shared information and the behavioral history of new users, creates a list of potential customers, and acquires new customers.

[0353] "Means for collecting past purchase history data and market trend information" refers to the function by which the server obtains purchase history data from the database and collects market trend information using an API.

[0354] "Means of inputting data into a generative AI model and emotion engine to generate new product information and feature articles" refers to the function of inputting collected data into a generative AI model along with specific prompt statements, and evaluating the generated content using an emotion engine.

[0355] "A means of embedding the generated digital information into HTML / CSS templates and publishing them at specified times" refers to a scheduling function that embeds content generated by a generative AI model into HTML / CSS templates and automatically publishes them on a regular basis.

[0356] "Means of using an emotion engine to collect user emotional feedback and utilize it for customized product recommendations based on digital information" refers to a function that acquires emotional data from users' facial expressions, voice, etc., and works in conjunction with a generative AI model to make personalized product recommendations.

[0357] This invention is a system for effectively connecting users and suppliers on online shopping sites. Specifically, it utilizes past purchase history data, current market trend information, and emotion data to generate new product information and feature articles, which are then published as digital information. An embodiment of this system is described in detail below.

[0358] 1. Data Collection and Analysis

[0359] server

[0360] The server first retrieves past purchase history data from the database using SQL queries. It also collects current market trend information through APIs, allowing it to understand user purchasing habits and market trends.

[0361] Specific actions

[0362] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[0363] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[0364] 2. Digital Information Generation

[0365] server

[0366] The server inputs the collected data into a generative AI model (such as the GPT series), prompts the model to generate new product information and feature articles, and uses an emotion engine to analyze user emotional feedback and evaluate the quality of the generated content.

[0367] Prompt Sentence Examples

[0368] "Generate articles featuring recommended products for the next month based on users' past purchase history and market trends."

[0369] 3. Publication of digital information

[0370] server

[0371] The generated content is embedded in an HTML / CSS design template. The server schedules the publication of this digital bulletin at specified times. For example, a cron job can be set up to automatically publish the bulletin at 9:00 a.m. on the first of every month.

[0372] Specific actions

[0373] Scheduling: "cron job setting: 0 9 1 php publish_kawaraban.php"

[0374] 4. Digital Information Distribution

[0375] server

[0376] As the publication date approaches, the server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for simultaneous distribution using the email server and push notification server. It can also distribute the newsletter at the optimal time based on each user's emotional data.

[0377] Specific actions

[0378] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[0379] Embed content in email template: Embed the content of the bulletin board in "mail_body.html".

[0380] 5. Information Spread

[0381] User

[0382] Users receive email or app push notifications, open the digital bulletin board, and use the "share" button within the bulletin board to spread the information on social media and other networks. The shared link is assigned a unique tracking ID, allowing the behavior of new users to be tracked.

[0383] Specific actions

[0384] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[0385] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[0386] 6. Finding new customers

[0387] server

[0388] The server analyzes the engagement of the information that has been shared and uses an emotion engine to analyze the emotional data of new users, creating a list of potential customers based on this data and preparing targeted advertisements and personalized offers.

[0389] Specific actions

[0390] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[0391] 7. Personalized product recommendations

[0392] server

[0393] The server then uses the generative AI model again to make personalized product suggestions based on the user's emotional data collected by the emotion engine, thereby enabling the provision of products that match the user's interests and improving customer satisfaction.

[0394] Specific actions

[0395] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[0396] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[0397] Through the above steps, this invention realizes personalized proposals based on the user's purchasing history and emotions, aiming to spread information naturally through existing users and effectively acquire new customers.

[0398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0399] Step 1:

[0400] The server collects past purchase history data from a database. It uses an SQL query to extract order history made within a specific period. The input is the database connection information and query conditions, and the output is a list of purchase history data. The server stores the data retrieved by the query in its internal memory.

[0401] Specific behavior:

[0402] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[0403] Data retrieved from the database is stored in memory

[0404] Step 2:

[0405] The server collects market trend information through APIs. It sends API requests to obtain information on popular products in the current market. The input is the API endpoint and query parameters, and the output is market trend information data. The server analyzes the obtained data and extracts the required information.

[0406] Specific behavior:

[0407] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[0408] Analyzing acquired data and extracting necessary information

[0409] Step 3:

[0410] The server inputs the collected purchase history data and market trend information into the generative AI model, which uses prompt statements to generate new product information and feature articles. The inputs are purchase history data, market trend information, and prompt statements, and the output is the generated content.

[0411] Specific behavior:

[0412] Prompt: "Generate an article highlighting recommended products for the next month based on the user's past purchase history and market trends."

[0413] Get content generated from generative AI models

[0414] Step 4:

[0415] The server uses an emotion engine to collect user emotion feedback and evaluate the quality of the generated content. The input is the generated content and user emotion data, and the output is the emotion analysis result. The server uses this feedback to regenerate optimized content.

[0416] Specific behavior:

[0417] Collect facial and voice data

[0418] Data analysis using emotion engine

[0419] Regenerating content using feedback results

[0420] Step 5:

[0421] The server embeds the generated content into an HTML / CSS template to create a digital bulletin board. This digital bulletin board is scheduled for publication based on a specified timing. The input is the generated content and HTML / CSS template, and the output is the digital bulletin board.

[0422] Specific behavior:

[0423] Embedding content into templates

[0424] Publication schedule settings: "cron job settings: 0 9 1 php publish_kawaraban.php"

[0425] Step 6:

[0426] As the publication date approaches, the server retrieves the target customer list from the database. It embeds the digital bulletin in an email template and sends it out all at once using the email server and push notification server. The input is the database connection information and email template, and the output is the target customer list and the email to be sent.

[0427] Specific behavior:

[0428] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[0429] Embed content in email template: Embed the contents of the bulletin board in "mail_body.html"

[0430] Add to email delivery queue: "INSERT INTO email_queue (recipient, content) VALUES ('user@example.com', 'mail_body.html');"

[0431] Step 7:

[0432] Users receive an email or push notification from the app, open the digital bulletin board, and use the "share" button provided within the bulletin board to spread information on social media, etc. The input is the click of the share button and the user's selected social media, and the output is the shared link.

[0433] Specific behavior:

[0434] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[0435] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[0436] Step 8:

[0437] The server analyzes the engagement of the disseminated information and creates a new potential customer list. It uses an emotion engine to analyze the user's emotion data and prepare targeted advertisements and personalized offers. The input is the shared log and emotion data, and the output is a potential customer list and advertising offers.

[0438] Specific behavior:

[0439] Engagement data analysis: SELECT COUNT() FROM share_logs WHERE platform='Instagram';

[0440] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[0441] Step 9:

[0442] The server uses the emotion engine to collect emotional data and then uses the generative AI model to make personalized product recommendations. The input is emotional data and purchase history data, and the output is personalized product recommendations.

[0443] Specific behavior:

[0444] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[0445] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[0446] (Application example 2)

[0447] 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."

[0448] Traditional online shopping sites have limited means to effectively connect users and suppliers, making it difficult to acquire new customers and improve customer satisfaction. Furthermore, personalized marketing strategies are difficult due to a lack of product recommendations that reflect user sentiment and real-time feedback.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically issuing digital information via the Internet, means for connecting users and suppliers, and means for disseminating information by utilizing existing users. This makes it possible to discover new products and develop new customers using generative AI and an emotion engine. Furthermore, by including means for analyzing user emotion data using smart devices and making personalized product recommendations, and means for periodically delivering digital information to customers, it is possible to improve user satisfaction and realize effective marketing measures.

[0450] The "Internet" is a system for exchanging information through computer networks and a technology that forms a widespread communications network.

[0451] "Periodic" means something that is repeated at regular intervals.

[0452] "Digital information" means information in a form that is stored or transmitted electronically.

[0453] "User" refers to an individual or organization that uses the system or service.

[0454] "Supplier" means an individual or entity that provides goods or services.

[0455] "Generative artificial intelligence" refers to a system that uses machine learning and deep learning techniques to derive new results from data.

[0456] A "smart device" is a device that can connect to the Internet and is an electronic device with advanced functionality.

[0457] "Emotional data" refers to information about the user's emotional state, and is obtained by analyzing facial expressions and voice.

[0458] "Personalization" means customizing to suit the characteristics and preferences of individual users.

[0459] "Product recommendation" is the act of presenting appropriate products or services to users.

[0460] "Distribution" refers to the act of sending information or data to a specific recipient.

[0461] The present invention is a system for effectively connecting users and suppliers, periodically issuing digital information and utilizing generative AI and emotion engines to discover new products and develop new customers. The following describes in detail the embodiments of the present invention.

[0462] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is embedded into a design template using HTML / CSS, and the resulting digital bulletin board is created. The system is configured to publish this digital bulletin board at a specified time (for example, the beginning of the month or a specific day of the week).

[0463] Next, as the regular publication date approaches, the server automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0464] The system also includes a means for analyzing the user's emotional data using smart devices. This analysis of emotional data utilizes the camera and microphone on the user's smartphone. This allows emotions to be captured from the user's facial expressions and voice, and then analyzed by the emotion engine. For example, if the user smiles at the camera, the system detects the emotion "fun" and recommends relaxation-related products based on that. If the user's facial expression is tired, the system suggests energy drinks and fitness-related products.

[0465] By combining the analyzed emotional data with purchase history data, personalized product recommendations are made. When a user shows interest in a product, emotional feedback is provided on the spot, and the system can improve the accuracy of product recommendations in real time based on this feedback.

[0466] As a concrete example,

[0467] When a user smiles into the camera, the system detects the emotion "happy" and recommends relaxation-related products based on that emotion.

[0468] On the other hand, if the person looks tired, we will suggest energy drinks or fitness-related products.

[0469] Also, an example of a prompt for the generation AI is:

[0470] Prompt: When the user smiles, suggest three relaxation-related products.

[0471] There is.

[0472] In this way, the server periodically publishes digital information via the Internet, providing a means to connect users and suppliers, and it is also possible to discover new products and develop new customers using generative artificial intelligence and emotion engines.

[0473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0474] Step 1:

[0475] The server collects historical purchase data and current market trend information. This information is extracted from a database. The input is user purchase data, and the output is a dataset for analysis. Specifically, the server uses SQL queries to retrieve the required information from the database.

[0476] Step 2:

[0477] The server inputs the collected data into the generative AI model to generate new product information and feature articles. The input is the collected purchase history data and market trend information, and the output is the generated content. Specifically, the server prompts the generative AI model to generate text and images.

[0478] Step 3:

[0479] The generated content is embedded in an HTML / CSS template and configured as a digital tile print. The input is the generated content, and the output is the digital tile print. Specifically, the server uses a template engine to format the content into a web page.

[0480] Step 4:

[0481] As the regular publication date approaches, the server retrieves the list of customers to be distributed from the database and embeds the digital bulletin in the email template. The input is the customer list and the digital bulletin, and the output is an email ready to be distributed. Specifically, the server generates an email for each customer on the list.

[0482] Step 5:

[0483] The server uses a mail server and a push notification server to efficiently deliver the digital bulletin to customers. The input is the prepared email, and the output is the digital bulletin that arrives in the customer's inbox. Specifically, the server sends the email using the SMTP protocol.

[0484] Step 6:

[0485] A user's smart device (e.g., a smartphone) uses a camera and microphone to capture the user's facial expressions and voice and collect emotional data. The input is the user's facial expressions and voice, and the output is emotional data. Specifically, the device collects data using OpenCV and a voice recognition library.

[0486] Step 7:

[0487] The device analyzes the collected emotion data with an emotion engine to determine the user's current emotion. The input is the captured emotion data, and the output is the analyzed emotional state. Specifically, the device uses a deep learning model to classify the emotion.

[0488] Step 8:

[0489] The server then makes personalized product recommendations based on the analyzed emotional data. The input is the analyzed emotional state and purchase history data, and the output is a list of recommended products. Specifically, the server selects products using a recommendation algorithm.

[0490] Step 9:

[0491] If the user is interested in a product, they will receive emotional feedback on the spot, which will improve the system's recommendation accuracy. The input is the user's additional feedback, and the output is an improved recommendation model. Specifically, the server updates the model in real time.

[0492] Step 10:

[0493] The server analyzes the information from the spread digital bulletin board and analyzes the behavioral history and emotional data of new users. The input is the spread information and the behavioral history of new users, and the output is a list of potential customers. Specifically, the server uses big data analysis tools.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 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.

[0500] 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).

[0501] 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.

[0502] 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.

[0503] 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).

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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."

[0510] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence to discover new products and cultivate new customers. A specific example of this system is shown below.

[0511] Creation and publication of digital bulletin boards

[0512] server

[0513] First, the server collects past purchase history data and current market trend information. This data is input into a generation AI, which generates new product information and feature articles based on the purchase data and trend information. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[0514] Digital Newspaper Distribution

[0515] server

[0516] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0517] Information dissemination by customers

[0518] User

[0519] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[0520] Developing new customers

[0521] server

[0522] Generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. This data is obtained through the analysis of social media and access logs. New marketing measures are implemented targeting the potential customers identified from the analysis results. For example, advertising emails with exclusive coupons can be sent to these potential customers to encourage new registrations and purchases.

[0523] Specific examples

[0524] 1. Creation and publication of digital bulletin boards

[0525] Server: The AI ​​generator creates a digital bulletin board containing summer sales information and articles introducing popular products. For example, a special feature on "Recommended Products for August" will feature clothing and accessories that are in high demand as the seasons change.

[0526] 2. Digital Newspaper Distribution

[0527] Server: The digital bulletin board generated on August 1st will be sent to 10,000 existing customers at once. The delivery method will be email and push notification.

[0528] 3. Customers spread information

[0529] User: Views a digital newsletter received via email and shares a specific page on Facebook, commenting, "Perfect items for this summer!"

[0530] 4. Developing new customers

[0531] Server: By analyzing information spread on social media, 500 new potential customers are identified. By sending advertisements with exclusive coupons to these potential customers, 50 new customers will sign up.

[0532] As described above, the present invention utilizes digital bulletin boards to promote the dissemination of information by existing users and efficiently realize the discovery of new products and new customers, thereby revitalizing the distribution of online shopping sites.

[0533] The processing flow will be explained below.

[0534] Creation and publication of digital bulletin boards

[0535] server

[0536] Step 1:

[0537] Retrieve historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[0538] Step 2:

[0539] Use web scrapers to gather trending information and news articles. Use scraping tools to get the latest market trends and new product information.

[0540] Step 3:

[0541] Preprocessing acquired data and converting it into a specified format, filtering out unnecessary data, and extracting and organizing necessary information.

[0542] Step 4:

[0543] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[0544] Step 5:

[0545] The generated content is embedded into an HTML / CSS template. The text and images created by the generative AI are placed in the design template to create a digital bulletin board.

[0546] Step 6:

[0547] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[0548] Digital Newspaper Distribution

[0549] server

[0550] Step 1:

[0551] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[0552] Step 2:

[0553] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[0554] Step 3:

[0555] Use a template engine to embed the digital kawaraban into the email template. Use a template engine such as Jinja2 to dynamically generate the email body.

[0556] Step 4:

[0557] The generated email is sent to the mail server for mass distribution, and the digital bulletin is sent to the customer via the SMTP server.

[0558] Information dissemination by customers

[0559] User

[0560] Step 1:

[0561] The user opens the digital bulletin board received via email or app push notification. The user checks the notification in their email client or dedicated app.

[0562] Step 2:

[0563] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[0564] Step 3:

[0565] Click the "Share" button in the news bulletin board. The share button is implemented in JavaScript, and the SNS sharing dialog opens.

[0566] Step 4:

[0567] Share the information from the digital bulletin on social media. Post links and comments using your Facebook, Twitter, or other accounts.

[0568] Developing new customers

[0569] server

[0570] Step 1:

[0571] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[0572] Step 2:

[0573] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[0574] Step 3:

[0575] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[0576] Step 4:

[0577] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[0578] The above are the specific processing steps for carrying out the invention.

[0579] Example 1

[0580] 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."

[0581] In conventional online shopping systems, the connection between users and suppliers is weak, making it difficult to efficiently discover new products and cultivate new customers. Furthermore, there is a lack of effective ways to utilize information dissemination by existing users, which means that product information remains limited. This creates the problem of hindering the vitalization of distribution on online shopping sites.

[0582] 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.

[0583] In this invention, the server includes means for automatically collecting purchase history data and market trend information and inputting it into a generating AI, means for generating product information and feature articles based on the purchase data and trend information using the generating AI, and means for creating digital information by embedding the generated content into design templates, which effectively connects users and suppliers, making it possible to spread information and develop new customers.

[0584] "Purchase history data" is data that includes detailed information about products that a user has purchased in the past.

[0585] "Market trend information" is data that indicates consumer interests and purchasing trends in the market.

[0586] "Generative AI" is software that has the ability to analyze data using machine learning algorithms and generate new information.

[0587] A "generative AI model" is an artificial intelligence model that generates new text and content based on diverse data.

[0588] A "design template" is a template that defines the layout and style for creating web pages and digital content.

[0589] "Digital information" refers generally to content that is electronically generated and distributed.

[0590] A "mail server" is a server that manages the sending and receiving of e-mail.

[0591] A "push notification server" is a server that sends notifications to mobile and web applications in real time.

[0592] "User" refers to a consumer who uses an online shopping site.

[0593] "Social media" refers to platforms where people share information online, such as Facebook and Twitter.

[0594] A "new customer list" is a list of potential new customers to target.

[0595] "Marketing strategies" refer to the planning and execution of advertising and promotions targeted at specific customer segments.

[0596] "Information diffusion" refers to the dissemination of information received by existing users to other users or networks.

[0597] This invention is a system for effectively connecting users and suppliers on online shopping sites, and in particular, it utilizes purchase history data and market trend information to periodically publish digital information, and uses generative artificial intelligence to discover new products and cultivate new customers. Specific implementation methods of this system are described below.

[0598] Creation and publication of digital bulletin boards

[0599] server:

[0600] The server first retrieves the user's purchase history data from a purchase history database. This database contains detailed information about the products the user has purchased in the past. In addition, it sends a request to an API (e.g., Google Trends) to obtain market trend information. The data collected in this way is input into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt sentence that can be used is as follows:

[0601] "Please create an article featuring new products for August based on the following data."

[0602] The generative AI model generates new product information and feature articles based on this data. This content is embedded in a design template (HTML / CSS) on the server and formatted as a digital bulletin board.

[0603] Scheduling and preparing for publication of the digital bulletin

[0604] server:

[0605] The server uses a scheduling function to set the publication date for the digital bulletin (for example, the beginning of the month or a specific day of the week). As the publication date approaches, the server automatically prepares for publication. Specifically, it retrieves the target customer list from the database and uses a mail server (for example, SendGrid) or push notification server (for example, Firebase Cloud Messaging) to simultaneously distribute the digital bulletin.

[0606] Information dissemination by customers

[0607] User:

[0608] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button that users can click to share the information on social media (e.g., Facebook or Twitter). This sharing function is expected to help the content of the digital bulletin reach many potential customers.

[0609] Developing new customers

[0610] server:

[0611] The server uses social media APIs and web analytics tools (e.g., Google Analytics) to collect and analyze responses to the spread of information. This data is input into a generative AI model, which creates a list of potential customers based on the responses and behavioral history of new users. Based on this list of new customers, the server implements new marketing initiatives. For example, it sends advertising emails with exclusive coupons to newly identified potential customers to encourage new registrations and purchases.

[0612] Specific examples

[0613] As an example, a digital bulletin is generated containing summer sale information and articles introducing popular products. For example, a "Featured August Products" section may include articles featuring clothing and accessories, which are in high demand as the seasons change. This digital bulletin is distributed simultaneously to 10,000 existing customers via email and push notifications on August 1st, the beginning of the month. In addition, when users share the digital bulletin they receive on social media, 500 new potential customers are identified, and advertising emails with exclusive coupons are sent to these customers. This will result in the registration of 50 new customers, revitalizing the distribution of the online shopping site.

[0614] The above is a specific embodiment for carrying out the present invention. This system realizes efficient product discovery and new customer acquisition, and can promote the revitalization of distribution on online shopping sites.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Specific processing steps from collecting purchase history data and market trend information to publishing a digital bulletin board

[0617] Step 1:

[0618] collection

[0619] Server: Sends SQL queries to the purchase history database to retrieve past purchase history data, and sends requests to the market trend information API to collect current market trend data.

[0620] Input: Purchase history database, trend information API request

[0621] Data processing: Execute SQL queries, send API requests

[0622] Output: Purchase history data, trend information

[0623] Step 2:

[0624] Data Entry and Content Generation

[0625] Server: The acquired purchase history data and market trend information are input into the generative AI model, which generates new product information and feature articles based on this data.

[0626] Input: Purchase history data, market trend information, prompt text

[0627] Data calculation: Input data into generative AI models, and generate content using the models

[0628] Output: Generated product information, featured articles

[0629] Specific operation: Send a request to the API of a generative AI model (e.g., OpenAI GPT-3), enter a prompt, and generate content.

[0630] Step 3:

[0631] Creating digital news bulletins

[0632] Server: The generated new product information and feature articles are embedded into HTML / CSS design templates, which creates digital bulletin boards.

[0633] Input: Generated product information, feature articles, design templates

[0634] Data processing: Inserting content into HTML / CSS templates

[0635] Output: Digital woodblock print

[0636] What it does: Uses a template engine to insert generated content into an HTML file and style it.

[0637] Step 4:

[0638] Scheduling and publication preparation

[0639] Server: The publication date of the digital bulletin is set using the scheduling function. As the publication date approaches, the target customer list is retrieved from the database and the digital bulletin is embedded in the email template.

[0640] Inputs: Digital bulletin, scheduling, customer list

[0641] Data processing: Inserting digital bulletin boards into email templates, acquiring customer lists

[0642] Output: Email template for delivery

[0643] Specific operations: Use a scheduler program to set a specific date and time, retrieve a customer list using an SQL query, and insert a digital bulletin board into a template.

[0644] Step 5:

[0645] Digital Newspaper Distribution

[0646] Server: Using a mail server or push notification server, email templates are sent to existing customers en masse.

[0647] Input: Email template for delivery, customer list

[0648] Data calculation: Sending email / push notifications

[0649] Output: Distributed digital bulletin board

[0650] Specific operation: Sends a request to a mail server (e.g., SendGrid) or push notification server (e.g., Firebase Cloud Messaging) and executes delivery.

[0651] Step 6:

[0652] Information dissemination by customers

[0653] User: Opens the digital bulletin they receive and spreads the information by clicking the share button on social media.

[0654] Input: Received digital bulletin board

[0655] Data processing: Sharing information on social media

[0656] Output: Information shared on social media

[0657] Specific actions: Click the share button in the digital news bulletin and share the information on the social media posting screen.

[0658] Step 7:

[0659] Developing new customers

[0660] Server: Collects and analyzes responses to the information spread using social media APIs and web analytics tools, and creates a list of new customers. Marketing strategies are implemented based on this list.

[0661] Input: Social media response data, access log data

[0662] Data calculation: Analysis of reaction data, creation of new customer list

[0663] Output: New customer list, marketing measures

[0664] What it does: Analyze data using a generative AI model and send promotional emails with exclusive coupons to newly identified potential customers.

[0665] These are the specific processing steps of the program for this system. At each step, appropriate data processing and calculations are performed based on the input data, and an output is generated as a result.

[0666] (Application example 1)

[0667] 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."

[0668] Current online shopping sites lack effective ways to connect users and suppliers. They also lack mechanisms for utilizing existing users to spread information, and methods for using generative artificial intelligence to discover new products and develop new customers. This makes it difficult to revitalize online shopping sites, and there are challenges in anticipating increased sales.

[0669] 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.

[0670] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information using existing users, means for discovering new products using a generating AI, means for acquiring new customers using the generating AI, means for publishing digital bulletins on specific days of the week, and means for embedding generated content into email templates and preparing for simultaneous distribution. This allows online shopping sites to effectively connect users with suppliers, discover new products, and acquire new customers. Furthermore, spreading information through existing users makes it easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0671] The "Internet" refers to a global network of computers that enables the collection, sharing, and communication of information.

[0672] "Periodic" means something that is repeated at regular intervals.

[0673] "Digital information" refers to information that is stored, processed, and transmitted electronically, and includes text, images, audio, and video.

[0674] "User" refers to a customer who uses a service such as an online shopping site.

[0675] "Provider" refers to a company or individual that provides goods or services to Users.

[0676] "Generative artificial intelligence" refers to technology that uses machine learning and deep learning to analyze data and generate new information like a human.

[0677] "New customers" refer to new customers with whom we have not previously conducted business or had contact.

[0678] "Publication" means the regular release of new information.

[0679] An "email template" refers to a template for creating email content according to a certain format.

[0680] "Simultaneous distribution" refers to sending the same information to a large number of users at the same time.

[0681] "Digital Kawaraban" refers to electronic newsletters and magazines generated by online shopping sites.

[0682] "Social media" refers to online platforms that allow people to share information and interact over the internet.

[0683] "Information diffusion" refers to spreading information to many people.

[0684] "New products" refer to products that have been newly added to the existing lineup.

[0685] "Market trends" refers to current market trends, trends, and changes in demand.

[0686] "Limited coupon" refers to a discount coupon that can only be used under certain conditions.

[0687] "Push notifications" refers to the function that forces notifications from applications to be displayed on devices such as smartphones and tablets.

[0688] "Potential Customer" refers to a person who has the potential to become a customer in the future.

[0689] A system embodying the invention includes the following components:

[0690] 1. A means of publishing digital information periodically via the Internet

[0691] The server collects market trend information and user purchasing history data, and uses a generative AI model to generate new product information and feature articles. This generated content is embedded in HTML / CSS design templates and published periodically as a digital bulletin board.

[0692] 2. A means of connecting users and suppliers

[0693] The server uses a generative AI model to analyze users' purchasing patterns and market trends, and selects individual recommended products, providing users with appropriate product information from suppliers.

[0694] 3. Using existing users to spread information

[0695] Digital bulletin boards have share buttons that users can click to easily spread information across social media and other online platforms, allowing a single piece of information to reach many potential customers.

[0696] 4. A means of discovering new products using generative artificial intelligence

[0697] The server inputs market trend information and purchase history data into a generative AI model to discover new products. In this process, large amounts of data are analyzed using machine learning algorithms to narrow down interesting product candidates.

[0698] 5. Means of acquiring new customers using the generative artificial intelligence

[0699] When information spreads on social media, the server analyzes the behavioral history of new customers. This is done by analyzing access logs and SNS data to generate a new list of potential customers. Limited coupons can be distributed to these customers to promote new customers.

[0700] 6. How to publish digital bulletins on specific days of the week

[0701] The server has a schedule management function, for example, to publish a new digital bulletin every Monday. This schedule management is to ensure regular information transmission.

[0702] 7. A way to embed generated content into email templates and prepare them for mass distribution

[0703] The newly generated digital bulletin board is embedded in an email template and prepared for mass distribution. The server sends the email to multiple users simultaneously via an SMTP server.

[0704] Hardware and software used

[0705] Hardware: Cloud server, user devices (smartphones, PCs)

[0706] Software: Generative AI models (e.g., GPT-4), HTML / CSS design templates, SMTP server, SNS integration API

[0707] Specific examples of processing

[0708] The user list includes email addresses and purchase histories of existing customers. Based on this, a generative AI model generates new product information and articles, which are then sent out as digital bulletins every Monday. For example, a bulletin titled "Recommended Fashion for This Fall" might feature new seasonal products.

[0709] Example prompt for a generative AI model:

[0710] "User ID: 1

[0711] Purchase history: ['Autumn jacket', 'Business shoes']

[0712] Market trends: ['Autumn / Winter Fashion', 'New Material Jackets']

[0713] This system allows online shopping sites to effectively connect users with suppliers, discover new products, and develop new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0715] Step 1:

[0716] The server collects user purchasing history and market trend data. The input is the existing user database and market trend database. The server temporarily stores this data in memory to input it into the generative AI model. The output is a dataset to be passed to the generative AI model.

[0717] Step 2:

[0718] The server feeds the dataset into the generative AI model to generate product information and feature articles. The input is collected user purchasing history and market trend data. The generative AI model analyzes the data using machine learning algorithms to generate interesting product information and feature articles. The output is the content of the generated digital bulletin board.

[0719] Step 3:

[0720] The server embeds the generated content into an HTML / CSS design template to create a digital tile-ban. The input is the generated content and the design template. An HTML engine is used to embed the content into the template. The output is an HTML file of the completed digital tile-ban.

[0721] Step 4:

[0722] The server embeds the digital kawaraban in the email template and prepares for mass distribution. The input is the completed HTML file of the digital kawaraban and the email template. The server embeds the digital kawaraban in the email template and prepares to send the email via the SMTP server. The output is the email data that has been prepared for sending.

[0723] Step 5:

[0724] The server distributes digital bulletins on specific days of the week. The input is email data that has been prepared for sending and an existing user list. Using the schedule management function, emails are sent en masse via an SMTP server, for example, every Monday. The output is the digital bulletin distributed to users.

[0725] Step 6:

[0726] Users can share the digital bulletin they receive on social media or other online platforms. The input is the digital bulletin they received and the operation of the share button. When a user clicks the share button, the information can be easily spread through the social media sharing API. The output is the shared link and the extent to which it has been shared.

[0727] Step 7:

[0728] The server analyzes the information spread on social media and analyzes the behavioral history of new users. The input is the access log of the spread link and social media data. This data is analyzed using an analysis engine to generate a list of new customers. The output is a list of newly identified potential customers.

[0729] Step 8:

[0730] The server distributes limited coupons to a new customer list. The input is a newly generated potential customer list and coupon information. An advertising email with a limited coupon is sent to the new customers using an SMTP server. The output is the email with the coupon delivered to the new customer.

[0731] Step 9:

[0732] A user receives a coupon and purchases a product on an online shopping site. The input is the received email with the coupon and the purchase process on the online shopping site. The user uses the coupon to purchase the product on the online shopping site at a discounted price. The output is the completed purchase transaction.

[0733] Through these steps, the system effectively connects users with suppliers, discovers new products, and develops new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[0734] 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.

[0735] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence and an emotion engine to discover new products and cultivate new customers. Specific examples and processing steps are shown below.

[0736] Creation and publication of digital bulletin boards

[0737] server

[0738] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[0739] Digital Newspaper Distribution

[0740] server

[0741] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0742] Information dissemination by customers

[0743] User

[0744] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[0745] Developing new customers

[0746] server

[0747] The generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. Furthermore, the emotion engine recognizes the user's emotions and analyzes their feedback to optimize marketing measures.

[0748] Use of emotion engine

[0749] server

[0750] The emotion engine monitors the user's emotional state in real time and analyzes the emotional data. For example, when a user browses a digital newspaper, their facial expressions and reactions are captured via a camera or microphone, and the data is used to determine the user's current emotions. This emotional data is then combined with the results of analysis by generative AI to provide customized product recommendations based on the user's interests.

[0751] Specific examples

[0752] 1. Creation and publication of digital bulletin boards

[0753] Server: Generative AI and an emotion engine create a digital bulletin board containing information about autumn sales and articles introducing popular products. Based on the user's past purchasing history and current emotional data, seasonal products are featured as "September's Recommended Products."

[0754] 2. Digital Newspaper Distribution

[0755] Server: The digital bulletin board generated on September 1st is sent simultaneously to 5,000 existing customers via email and push notification. In addition, the timing of delivery is optimized based on individual sentiment data.

[0756] 3. Customers spread information

[0757] User: Views the digital bulletin received via email and evaluates the products. When sharing a specific page on Instagram, the user adds the comment "This fall's trend! Recommended products featured."

[0758] 4. Developing new customers

[0759] Server: Analyze engagement with the information shared on social media and identify 300 new potential customers. Based on their emotional data, deliver more personalized, exclusive offers, and 50 new sign-ups.

[0760] By incorporating an emotion engine, feedback based on the user's emotional state can be obtained, which can be used to optimize the content and distribution timing of digital bulletins, thereby improving user satisfaction and enabling effective marketing measures.

[0761] The processing flow will be explained below.

[0762] Creation and publication of digital bulletin boards

[0763] server

[0764] Step 1:

[0765] Obtain historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[0766] Step 2:

[0767] Use web scraping tools to gather the latest market trend information and news articles. Scrape and retrieve the required data from the specified website.

[0768] Step 3:

[0769] Preprocess and format the acquired data, filter out unnecessary data, and extract and organize the necessary information.

[0770] Step 4:

[0771] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[0772] Step 5:

[0773] The generated content is embedded into HTML / CSS templates, and text and images generated by AI are embedded in the template engine to create a digital bulletin board.

[0774] Step 6:

[0775] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[0776] Digital Newspaper Distribution

[0777] server

[0778] Step 1:

[0779] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[0780] Step 2:

[0781] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[0782] Step 3:

[0783] Embed the digital bulletin in an email template using a template engine, combining the dynamically generated email body with the digital bulletin.

[0784] Step 4:

[0785] The generated email is sent to the mail server for mass distribution, and then sent to customers via the SMTP server.

[0786] Information dissemination by customers

[0787] User

[0788] Step 1:

[0789] Open the digital bulletin received by email or push notification. Check the notification in your email client or dedicated app.

[0790] Step 2:

[0791] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[0792] Step 3:

[0793] Click the "Share" button in the news bulletin board. The JavaScript-implemented share button will open the social media sharing dialog.

[0794] Step 4:

[0795] Share the information from the digital bulletin on social media, such as Facebook or Twitter, by posting links and comments.

[0796] Developing new customers

[0797] server

[0798] Step 1:

[0799] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[0800] Step 2:

[0801] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[0802] Step 3:

[0803] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[0804] Step 4:

[0805] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[0806] Use of emotion engine

[0807] server

[0808] Step 1:

[0809] The emotion engine recognizes the user's emotional state, capturing facial expressions and voice data through a camera and microphone.

[0810] Step 2:

[0811] The acquired data is analyzed in real time to determine the user's emotional state, and an emotion analysis algorithm is used to classify the emotion into categories such as positive, negative, or neutral.

[0812] Step 3:

[0813] Emotional data is stored and analyzed cumulatively. Emotional data from viewing digital bulletin boards is added to user profiles.

[0814] Step 4:

[0815] Incorporate sentiment data into generative AI and marketing strategies to generate more personalized content and offers based on sentiment data.

[0816] As a specific example, the generative AI and emotion engine will create a digital bulletin containing information about autumn sales, and while monitoring the emotional data of each user, send it to customers at the optimal timing. If a customer who receives the digital bulletin responds positively, the content and timing of the next distribution will be further optimized based on that reaction.

[0817] In this way, the present invention utilizes digital bulletin boards and incorporates user emotional data to discover new products, cultivate new customers, and achieve effective marketing.

[0818] Example 2

[0819] 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."

[0820] Modern online shopping sites require efficient methods to connect users with suppliers and to discover new products and customers. However, existing systems lack the means to provide personalized recommendations that take into account not only users' past purchase history and market trends, but also their emotional feedback. Furthermore, they have yet to effectively leverage existing customers to naturally spread information and attract new customers.

[0821] 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.

[0822] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information by utilizing existing users, means for discovering new products using generative AI, means for cultivating new customers using the generative AI, means for collecting past purchase history data and market trend information and inputting the collected data into a generative AI model and an emotion engine to generate new product information and feature articles, means for embedding the generated digital information into HTML / CSS templates and publishing them at specified times, and means for collecting user emotion feedback using the emotion engine and using the collected data to customize product proposals for the digital information. This enables personalized proposals based on the user's purchase history and emotions, and enables natural information dissemination through existing users and effective acquisition of new customers.

[0823] "Means of publishing digital information periodically via the Internet" refers to the function of a server publishing content generated using HTML / CSS templates at specified times.

[0824] "Means to connect users and suppliers" refers to the function of utilizing generative AI models and emotion engines to provide appropriate product information based on users' purchasing history and market trend information.

[0825] "Means of spreading information by utilizing existing users" refers to the function that allows existing users to use the share buttons within the digital bulletin board to spread information on social media and other networks.

[0826] "Means of discovering new products using generative AI" refers to the function of a generative AI model that analyzes past purchase history data and market trend information to automatically suggest new products.

[0827] "Means of developing new customers using generative AI" refers to the function in which a generative AI model analyzes shared information and the behavioral history of new users, creates a list of potential customers, and acquires new customers.

[0828] "Means for collecting past purchase history data and market trend information" refers to the function by which the server obtains purchase history data from the database and collects market trend information using an API.

[0829] "Means of inputting data into a generative AI model and emotion engine to generate new product information and feature articles" refers to the function of inputting collected data into a generative AI model along with specific prompt statements, and evaluating the generated content using an emotion engine.

[0830] "A means of embedding the generated digital information into HTML / CSS templates and publishing them at specified times" refers to a scheduling function that embeds content generated by a generative AI model into HTML / CSS templates and automatically publishes them on a regular basis.

[0831] "Means of using an emotion engine to collect user emotional feedback and utilize it for customized product recommendations based on digital information" refers to a function that acquires emotional data from users' facial expressions, voice, etc., and works in conjunction with a generative AI model to make personalized product recommendations.

[0832] This invention is a system for effectively connecting users and suppliers on online shopping sites. Specifically, it utilizes past purchase history data, current market trend information, and emotion data to generate new product information and feature articles, which are then published as digital information. An embodiment of this system is described in detail below.

[0833] 1. Data Collection and Analysis

[0834] server

[0835] The server first retrieves past purchase history data from the database using SQL queries. It also collects current market trend information through APIs, allowing it to understand user purchasing habits and market trends.

[0836] Specific actions

[0837] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[0838] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[0839] 2. Digital Information Generation

[0840] server

[0841] The server inputs the collected data into a generative AI model (such as the GPT series), prompts the model to generate new product information and feature articles, and uses an emotion engine to analyze user emotional feedback and evaluate the quality of the generated content.

[0842] Prompt Sentence Examples

[0843] "Generate articles featuring recommended products for the next month based on users' past purchase history and market trends."

[0844] 3. Publication of digital information

[0845] server

[0846] The generated content is embedded in an HTML / CSS design template. The server schedules the publication of this digital bulletin at specified times. For example, a cron job can be set up to automatically publish the bulletin at 9:00 a.m. on the first of every month.

[0847] Specific actions

[0848] Scheduling: "cron job setting: 0 9 1 php publish_kawaraban.php"

[0849] 4. Digital Information Distribution

[0850] server

[0851] As the publication date approaches, the server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for simultaneous distribution using the email server and push notification server. It can also distribute the newsletter at the optimal time based on each user's emotional data.

[0852] Specific actions

[0853] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[0854] Embed content in email template: Embed the content of the bulletin board in "mail_body.html".

[0855] 5. Information Spread

[0856] User

[0857] Users receive email or app push notifications, open the digital bulletin board, and use the "share" button within the bulletin board to spread the information on social media and other networks. The shared link is assigned a unique tracking ID, allowing the behavior of new users to be tracked.

[0858] Specific actions

[0859] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[0860] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[0861] 6. Finding new customers

[0862] server

[0863] The server analyzes the engagement of the information that has been shared and uses an emotion engine to analyze the emotional data of new users, creating a list of potential customers based on this data and preparing targeted advertisements and personalized offers.

[0864] Specific actions

[0865] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[0866] 7. Personalized product recommendations

[0867] server

[0868] The server then uses the generative AI model again to make personalized product suggestions based on the user's emotional data collected by the emotion engine, thereby enabling the provision of products that match the user's interests and improving customer satisfaction.

[0869] Specific actions

[0870] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[0871] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[0872] Through the above steps, this invention realizes personalized proposals based on the user's purchasing history and emotions, aiming to spread information naturally through existing users and effectively acquire new customers.

[0873] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0874] Step 1:

[0875] The server collects past purchase history data from a database. It uses an SQL query to extract order history made within a specific period. The input is the database connection information and query conditions, and the output is a list of purchase history data. The server stores the data retrieved by the query in its internal memory.

[0876] Specific behavior:

[0877] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[0878] Data retrieved from the database is stored in memory

[0879] Step 2:

[0880] The server collects market trend information through APIs. It sends API requests to obtain information on popular products in the current market. The input is the API endpoint and query parameters, and the output is market trend information data. The server analyzes the obtained data and extracts the required information.

[0881] Specific behavior:

[0882] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[0883] Analyzing acquired data and extracting necessary information

[0884] Step 3:

[0885] The server inputs the collected purchase history data and market trend information into the generative AI model, which uses prompt statements to generate new product information and feature articles. The inputs are purchase history data, market trend information, and prompt statements, and the output is the generated content.

[0886] Specific behavior:

[0887] Prompt: "Generate an article highlighting recommended products for the next month based on the user's past purchase history and market trends."

[0888] Get content generated from generative AI models

[0889] Step 4:

[0890] The server uses an emotion engine to collect user emotion feedback and evaluate the quality of the generated content. The input is the generated content and user emotion data, and the output is the emotion analysis result. The server uses this feedback to regenerate optimized content.

[0891] Specific behavior:

[0892] Collect facial and voice data

[0893] Data analysis using emotion engine

[0894] Regenerating content using feedback results

[0895] Step 5:

[0896] The server embeds the generated content into an HTML / CSS template to create a digital bulletin board. This digital bulletin board is scheduled for publication based on a specified timing. The input is the generated content and HTML / CSS template, and the output is the digital bulletin board.

[0897] Specific behavior:

[0898] Embedding content into templates

[0899] Publication schedule settings: "cron job settings: 0 9 1 php publish_kawaraban.php"

[0900] Step 6:

[0901] As the publication date approaches, the server retrieves the target customer list from the database. It embeds the digital bulletin in an email template and sends it out all at once using the email server and push notification server. The input is the database connection information and email template, and the output is the target customer list and the email to be sent.

[0902] Specific behavior:

[0903] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[0904] Embed content in email template: Embed the contents of the bulletin board in "mail_body.html"

[0905] Add to email delivery queue: "INSERT INTO email_queue (recipient, content) VALUES ('user@example.com', 'mail_body.html');"

[0906] Step 7:

[0907] Users receive an email or push notification from the app, open the digital bulletin board, and use the "share" button provided within the bulletin board to spread information on social media, etc. The input is the click of the share button and the user's selected social media, and the output is the shared link.

[0908] Specific behavior:

[0909] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[0910] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[0911] Step 8:

[0912] The server analyzes the engagement of the disseminated information and creates a new potential customer list. It uses an emotion engine to analyze the user's emotion data and prepare targeted advertisements and personalized offers. The input is the shared log and emotion data, and the output is a potential customer list and advertising offers.

[0913] Specific behavior:

[0914] Engagement data analysis: SELECT COUNT() FROM share_logs WHERE platform='Instagram';

[0915] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[0916] Step 9:

[0917] The server uses the emotion engine to collect emotional data and then uses the generative AI model to make personalized product recommendations. The input is emotional data and purchase history data, and the output is personalized product recommendations.

[0918] Specific behavior:

[0919] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[0920] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[0921] (Application example 2)

[0922] 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."

[0923] Traditional online shopping sites have limited means to effectively connect users and suppliers, making it difficult to acquire new customers and improve customer satisfaction. Furthermore, personalized marketing strategies are difficult due to a lack of product recommendations that reflect user sentiment and real-time feedback.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically issuing digital information via the Internet, means for connecting users and suppliers, and means for disseminating information by utilizing existing users. This makes it possible to discover new products and develop new customers using generative AI and an emotion engine. Furthermore, by including means for analyzing user emotion data using smart devices and making personalized product recommendations, and means for periodically delivering digital information to customers, it is possible to improve user satisfaction and realize effective marketing measures.

[0925] The "Internet" is a system for exchanging information through computer networks and a technology that forms a widespread communications network.

[0926] "Periodic" means something that is repeated at regular intervals.

[0927] "Digital information" means information in a form that is stored or transmitted electronically.

[0928] "User" refers to an individual or organization that uses the system or service.

[0929] "Supplier" means an individual or entity that provides goods or services.

[0930] "Generative artificial intelligence" refers to a system that uses machine learning and deep learning techniques to derive new results from data.

[0931] A "smart device" is a device that can connect to the Internet and is an electronic device with advanced functionality.

[0932] "Emotional data" refers to information about the user's emotional state, and is obtained by analyzing facial expressions and voice.

[0933] "Personalization" means customizing to suit the characteristics and preferences of individual users.

[0934] "Product recommendation" is the act of presenting appropriate products or services to users.

[0935] "Distribution" refers to the act of sending information or data to a specific recipient.

[0936] The present invention is a system for effectively connecting users and suppliers, periodically issuing digital information and utilizing generative AI and emotion engines to discover new products and develop new customers. The following describes in detail the embodiments of the present invention.

[0937] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is embedded into a design template using HTML / CSS, and the resulting digital bulletin board is created. The system is configured to publish this digital bulletin board at a specified time (for example, the beginning of the month or a specific day of the week).

[0938] Next, as the regular publication date approaches, the server automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0939] The system also includes a means for analyzing the user's emotional data using smart devices. This analysis of emotional data utilizes the camera and microphone on the user's smartphone. This allows emotions to be captured from the user's facial expressions and voice, and then analyzed by the emotion engine. For example, if the user smiles at the camera, the system detects the emotion "fun" and recommends relaxation-related products based on that. If the user's facial expression is tired, the system suggests energy drinks and fitness-related products.

[0940] By combining the analyzed emotional data with purchase history data, personalized product recommendations are made. When a user shows interest in a product, emotional feedback is provided on the spot, and the system can improve the accuracy of product recommendations in real time based on this feedback.

[0941] As a concrete example,

[0942] When a user smiles into the camera, the system detects the emotion "happy" and recommends relaxation-related products based on that emotion.

[0943] On the other hand, if the person looks tired, we will suggest energy drinks or fitness-related products.

[0944] Also, an example of a prompt for the generation AI is:

[0945] Prompt: When the user smiles, suggest three relaxation-related products.

[0946] There is.

[0947] In this way, the server periodically publishes digital information via the Internet, providing a means to connect users and suppliers, and it is also possible to discover new products and develop new customers using generative artificial intelligence and emotion engines.

[0948] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0949] Step 1:

[0950] The server collects historical purchase data and current market trend information. This information is extracted from a database. The input is user purchase data, and the output is a dataset for analysis. Specifically, the server uses SQL queries to retrieve the required information from the database.

[0951] Step 2:

[0952] The server inputs the collected data into the generative AI model to generate new product information and feature articles. The input is the collected purchase history data and market trend information, and the output is the generated content. Specifically, the server prompts the generative AI model to generate text and images.

[0953] Step 3:

[0954] The generated content is embedded in an HTML / CSS template and configured as a digital tile print. The input is the generated content, and the output is the digital tile print. Specifically, the server uses a template engine to format the content into a web page.

[0955] Step 4:

[0956] As the regular publication date approaches, the server retrieves the list of customers to be distributed from the database and embeds the digital bulletin in the email template. The input is the customer list and the digital bulletin, and the output is an email ready to be distributed. Specifically, the server generates an email for each customer on the list.

[0957] Step 5:

[0958] The server uses a mail server and a push notification server to efficiently deliver the digital bulletin to customers. The input is the prepared email, and the output is the digital bulletin that arrives in the customer's inbox. Specifically, the server sends the email using the SMTP protocol.

[0959] Step 6:

[0960] A user's smart device (e.g., a smartphone) uses a camera and microphone to capture the user's facial expressions and voice and collect emotional data. The input is the user's facial expressions and voice, and the output is emotional data. Specifically, the device collects data using OpenCV and a voice recognition library.

[0961] Step 7:

[0962] The device analyzes the collected emotion data with an emotion engine to determine the user's current emotion. The input is the captured emotion data, and the output is the analyzed emotional state. Specifically, the device uses a deep learning model to classify the emotion.

[0963] Step 8:

[0964] The server then makes personalized product recommendations based on the analyzed emotional data. The input is the analyzed emotional state and purchase history data, and the output is a list of recommended products. Specifically, the server selects products using a recommendation algorithm.

[0965] Step 9:

[0966] If the user is interested in a product, they will receive emotional feedback on the spot, which will improve the system's recommendation accuracy. The input is the user's additional feedback, and the output is an improved recommendation model. Specifically, the server updates the model in real time.

[0967] Step 10:

[0968] The server analyzes the information from the spread digital bulletin board and analyzes the behavioral history and emotional data of new users. The input is the spread information and the behavioral history of new users, and the output is a list of potential customers. Specifically, the server uses big data analysis tools.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] [Third embodiment]

[0973] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0974] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0975] 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).

[0976] 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.

[0977] 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.

[0978] 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).

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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."

[0985] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence to discover new products and cultivate new customers. A specific example of this system is shown below.

[0986] Creation and publication of digital bulletin boards

[0987] server

[0988] First, the server collects past purchase history data and current market trend information. This data is input into a generation AI, which generates new product information and feature articles based on the purchase data and trend information. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[0989] Digital Newspaper Distribution

[0990] server

[0991] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[0992] Information dissemination by customers

[0993] User

[0994] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[0995] Developing new customers

[0996] server

[0997] Generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. This data is obtained through the analysis of social media and access logs. New marketing measures are implemented targeting the potential customers identified from the analysis results. For example, advertising emails with exclusive coupons can be sent to these potential customers to encourage new registrations and purchases.

[0998] Specific examples

[0999] 1. Creation and publication of digital bulletin boards

[1000] Server: The AI ​​generator creates a digital bulletin board containing summer sales information and articles introducing popular products. For example, a special feature on "Recommended Products for August" will feature clothing and accessories that are in high demand as the seasons change.

[1001] 2. Digital Newspaper Distribution

[1002] Server: The digital bulletin board generated on August 1st will be sent to 10,000 existing customers at once. The delivery method will be email and push notification.

[1003] 3. Customers spread information

[1004] User: Views a digital newsletter received via email and shares a specific page on Facebook, commenting, "Perfect items for this summer!"

[1005] 4. Developing new customers

[1006] Server: By analyzing information spread on social media, 500 new potential customers are identified. By sending advertisements with exclusive coupons to these potential customers, 50 new customers will sign up.

[1007] As described above, the present invention utilizes digital bulletin boards to promote the dissemination of information by existing users and efficiently realize the discovery of new products and new customers, thereby revitalizing the distribution of online shopping sites.

[1008] The processing flow will be explained below.

[1009] Creation and publication of digital bulletin boards

[1010] server

[1011] Step 1:

[1012] Retrieve historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[1013] Step 2:

[1014] Use web scrapers to gather trending information and news articles. Use scraping tools to get the latest market trends and new product information.

[1015] Step 3:

[1016] Preprocessing acquired data and converting it into a specified format, filtering out unnecessary data, and extracting and organizing necessary information.

[1017] Step 4:

[1018] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[1019] Step 5:

[1020] The generated content is embedded into an HTML / CSS template. The text and images created by the generative AI are placed in the design template to create a digital bulletin board.

[1021] Step 6:

[1022] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[1023] Digital Newspaper Distribution

[1024] server

[1025] Step 1:

[1026] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[1027] Step 2:

[1028] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[1029] Step 3:

[1030] Use a template engine to embed the digital kawaraban into the email template. Use a template engine such as Jinja2 to dynamically generate the email body.

[1031] Step 4:

[1032] The generated email is sent to the mail server for mass distribution, and the digital bulletin is sent to the customer via the SMTP server.

[1033] Information dissemination by customers

[1034] User

[1035] Step 1:

[1036] The user opens the digital bulletin board received via email or app push notification. The user checks the notification in their email client or dedicated app.

[1037] Step 2:

[1038] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[1039] Step 3:

[1040] Click the "Share" button in the news bulletin board. The share button is implemented in JavaScript, and the SNS sharing dialog opens.

[1041] Step 4:

[1042] Share the information from the digital bulletin on social media. Post links and comments using your Facebook, Twitter, or other accounts.

[1043] Developing new customers

[1044] server

[1045] Step 1:

[1046] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[1047] Step 2:

[1048] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[1049] Step 3:

[1050] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[1051] Step 4:

[1052] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[1053] The above are the specific processing steps for carrying out the invention.

[1054] Example 1

[1055] 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."

[1056] In conventional online shopping systems, the connection between users and suppliers is weak, making it difficult to efficiently discover new products and cultivate new customers. Furthermore, there is a lack of effective ways to utilize information dissemination by existing users, which means that product information remains limited. This creates the problem of hindering the vitalization of distribution on online shopping sites.

[1057] 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.

[1058] In this invention, the server includes means for automatically collecting purchase history data and market trend information and inputting it into a generating AI, means for generating product information and feature articles based on the purchase data and trend information using the generating AI, and means for creating digital information by embedding the generated content into design templates, which effectively connects users and suppliers, making it possible to spread information and develop new customers.

[1059] "Purchase history data" is data that includes detailed information about products that a user has purchased in the past.

[1060] "Market trend information" is data that indicates consumer interests and purchasing trends in the market.

[1061] "Generative AI" is software that has the ability to analyze data using machine learning algorithms and generate new information.

[1062] A "generative AI model" is an artificial intelligence model that generates new text and content based on diverse data.

[1063] A "design template" is a template that defines the layout and style for creating web pages and digital content.

[1064] "Digital information" refers generally to content that is electronically generated and distributed.

[1065] A "mail server" is a server that manages the sending and receiving of e-mail.

[1066] A "push notification server" is a server that sends notifications to mobile and web applications in real time.

[1067] "User" refers to a consumer who uses an online shopping site.

[1068] "Social media" refers to platforms where people share information online, such as Facebook and Twitter.

[1069] A "new customer list" is a list of potential new customers to target.

[1070] "Marketing strategies" refer to the planning and execution of advertising and promotions targeted at specific customer segments.

[1071] "Information diffusion" refers to the dissemination of information received by existing users to other users or networks.

[1072] This invention is a system for effectively connecting users and suppliers on online shopping sites, and in particular, it utilizes purchase history data and market trend information to periodically publish digital information, and uses generative artificial intelligence to discover new products and cultivate new customers. Specific implementation methods of this system are described below.

[1073] Creation and publication of digital bulletin boards

[1074] server:

[1075] The server first retrieves the user's purchase history data from a purchase history database. This database contains detailed information about the products the user has purchased in the past. In addition, it sends a request to an API (e.g., Google Trends) to obtain market trend information. The data collected in this way is input into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt sentence that can be used is as follows:

[1076] "Please create an article featuring new products for August based on the following data."

[1077] The generative AI model generates new product information and feature articles based on this data. This content is embedded in a design template (HTML / CSS) on the server and formatted as a digital bulletin board.

[1078] Scheduling and preparing for publication of the digital bulletin

[1079] server:

[1080] The server uses a scheduling function to set the publication date for the digital bulletin (for example, the beginning of the month or a specific day of the week). As the publication date approaches, the server automatically prepares for publication. Specifically, it retrieves the target customer list from the database and uses a mail server (for example, SendGrid) or push notification server (for example, Firebase Cloud Messaging) to simultaneously distribute the digital bulletin.

[1081] Information dissemination by customers

[1082] User:

[1083] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button that users can click to share the information on social media (e.g., Facebook or Twitter). This sharing function is expected to help the content of the digital bulletin reach many potential customers.

[1084] Developing new customers

[1085] server:

[1086] The server uses social media APIs and web analytics tools (e.g., Google Analytics) to collect and analyze responses to the spread of information. This data is input into a generative AI model, which creates a list of potential customers based on the responses and behavioral history of new users. Based on this list of new customers, the server implements new marketing initiatives. For example, it sends advertising emails with exclusive coupons to newly identified potential customers to encourage new registrations and purchases.

[1087] Specific examples

[1088] As an example, a digital bulletin is generated containing summer sale information and articles introducing popular products. For example, a "Featured August Products" section may include articles featuring clothing and accessories, which are in high demand as the seasons change. This digital bulletin is distributed simultaneously to 10,000 existing customers via email and push notifications on August 1st, the beginning of the month. In addition, when users share the digital bulletin they receive on social media, 500 new potential customers are identified, and advertising emails with exclusive coupons are sent to these customers. This will result in the registration of 50 new customers, revitalizing the distribution of the online shopping site.

[1089] The above is a specific embodiment for carrying out the present invention. This system realizes efficient product discovery and new customer acquisition, and can promote the revitalization of distribution on online shopping sites.

[1090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1091] Specific processing steps from collecting purchase history data and market trend information to publishing a digital bulletin board

[1092] Step 1:

[1093] collection

[1094] Server: Sends SQL queries to the purchase history database to retrieve past purchase history data, and sends requests to the market trend information API to collect current market trend data.

[1095] Input: Purchase history database, trend information API request

[1096] Data processing: Execute SQL queries, send API requests

[1097] Output: Purchase history data, trend information

[1098] Step 2:

[1099] Data Entry and Content Generation

[1100] Server: The acquired purchase history data and market trend information are input into the generative AI model, which generates new product information and feature articles based on this data.

[1101] Input: Purchase history data, market trend information, prompt text

[1102] Data calculation: Input data into generative AI models, and generate content using the models

[1103] Output: Generated product information, featured articles

[1104] Specific operation: Send a request to the API of a generative AI model (e.g., OpenAI GPT-3), enter a prompt, and generate content.

[1105] Step 3:

[1106] Creating digital news bulletins

[1107] Server: The generated new product information and feature articles are embedded into HTML / CSS design templates, which creates digital bulletin boards.

[1108] Input: Generated product information, feature articles, design templates

[1109] Data processing: Inserting content into HTML / CSS templates

[1110] Output: Digital woodblock print

[1111] What it does: Uses a template engine to insert generated content into an HTML file and style it.

[1112] Step 4:

[1113] Scheduling and publication preparation

[1114] Server: The publication date of the digital bulletin is set using the scheduling function. As the publication date approaches, the target customer list is retrieved from the database and the digital bulletin is embedded in the email template.

[1115] Inputs: Digital bulletin, scheduling, customer list

[1116] Data processing: Inserting digital bulletin boards into email templates, acquiring customer lists

[1117] Output: Email template for delivery

[1118] Specific operations: Use a scheduler program to set a specific date and time, retrieve a customer list using an SQL query, and insert a digital bulletin board into a template.

[1119] Step 5:

[1120] Digital Newspaper Distribution

[1121] Server: Using a mail server or push notification server, email templates are sent to existing customers en masse.

[1122] Input: Email template for delivery, customer list

[1123] Data calculation: Sending email / push notifications

[1124] Output: Distributed digital bulletin board

[1125] Specific operation: Sends a request to a mail server (e.g., SendGrid) or push notification server (e.g., Firebase Cloud Messaging) and executes delivery.

[1126] Step 6:

[1127] Information dissemination by customers

[1128] User: Opens the digital bulletin they receive and spreads the information by clicking the share button on social media.

[1129] Input: Received digital bulletin board

[1130] Data processing: Sharing information on social media

[1131] Output: Information shared on social media

[1132] Specific actions: Click the share button in the digital news bulletin and share the information on the social media posting screen.

[1133] Step 7:

[1134] Developing new customers

[1135] Server: Collects and analyzes responses to the information spread using social media APIs and web analytics tools, and creates a list of new customers. Marketing strategies are implemented based on this list.

[1136] Input: Social media response data, access log data

[1137] Data calculation: Analysis of reaction data, creation of new customer list

[1138] Output: New customer list, marketing measures

[1139] What it does: Analyze data using a generative AI model and send promotional emails with exclusive coupons to newly identified potential customers.

[1140] These are the specific processing steps of the program for this system. At each step, appropriate data processing and calculations are performed based on the input data, and an output is generated as a result.

[1141] (Application example 1)

[1142] 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."

[1143] Current online shopping sites lack effective ways to connect users and suppliers. They also lack mechanisms for utilizing existing users to spread information, and methods for using generative artificial intelligence to discover new products and develop new customers. This makes it difficult to revitalize online shopping sites, and there are challenges in anticipating increased sales.

[1144] 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.

[1145] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information using existing users, means for discovering new products using a generating AI, means for acquiring new customers using the generating AI, means for publishing digital bulletins on specific days of the week, and means for embedding generated content into email templates and preparing for simultaneous distribution. This allows online shopping sites to effectively connect users with suppliers, discover new products, and acquire new customers. Furthermore, spreading information through existing users makes it easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1146] The "Internet" refers to a global network of computers that enables the collection, sharing, and communication of information.

[1147] "Periodic" means something that is repeated at regular intervals.

[1148] "Digital information" refers to information that is stored, processed, and transmitted electronically, and includes text, images, audio, and video.

[1149] "User" refers to a customer who uses a service such as an online shopping site.

[1150] "Provider" refers to a company or individual that provides goods or services to Users.

[1151] "Generative artificial intelligence" refers to technology that uses machine learning and deep learning to analyze data and generate new information like a human.

[1152] "New customers" refer to new customers with whom we have not previously conducted business or had contact.

[1153] "Publication" means the regular release of new information.

[1154] An "email template" refers to a template for creating email content according to a certain format.

[1155] "Simultaneous distribution" refers to sending the same information to a large number of users at the same time.

[1156] "Digital Kawaraban" refers to electronic newsletters and magazines generated by online shopping sites.

[1157] "Social media" refers to online platforms that allow people to share information and interact over the internet.

[1158] "Information diffusion" refers to spreading information to many people.

[1159] "New products" refer to products that have been newly added to the existing lineup.

[1160] "Market trends" refers to current market trends, trends, and changes in demand.

[1161] "Limited coupon" refers to a discount coupon that can only be used under certain conditions.

[1162] "Push notifications" refers to the function that forces notifications from applications to be displayed on devices such as smartphones and tablets.

[1163] "Potential Customer" refers to a person who has the potential to become a customer in the future.

[1164] A system embodying the invention includes the following components:

[1165] 1. A means of publishing digital information periodically via the Internet

[1166] The server collects market trend information and user purchasing history data, and uses a generative AI model to generate new product information and feature articles. This generated content is embedded in HTML / CSS design templates and published periodically as a digital bulletin board.

[1167] 2. A means of connecting users and suppliers

[1168] The server uses a generative AI model to analyze users' purchasing patterns and market trends, and selects individual recommended products, providing users with appropriate product information from suppliers.

[1169] 3. Using existing users to spread information

[1170] Digital bulletin boards have share buttons that users can click to easily spread information across social media and other online platforms, allowing a single piece of information to reach many potential customers.

[1171] 4. A means of discovering new products using generative artificial intelligence

[1172] The server inputs market trend information and purchase history data into a generative AI model to discover new products. In this process, large amounts of data are analyzed using machine learning algorithms to narrow down interesting product candidates.

[1173] 5. Means of acquiring new customers using the generative artificial intelligence

[1174] When information spreads on social media, the server analyzes the behavioral history of new customers. This is done by analyzing access logs and SNS data to generate a new list of potential customers. Limited coupons can be distributed to these customers to promote new customers.

[1175] 6. How to publish digital bulletins on specific days of the week

[1176] The server has a schedule management function, for example, to publish a new digital bulletin every Monday. This schedule management is to ensure regular information transmission.

[1177] 7. A way to embed generated content into email templates and prepare them for mass distribution

[1178] The newly generated digital bulletin board is embedded in an email template and prepared for mass distribution. The server sends the email to multiple users simultaneously via an SMTP server.

[1179] Hardware and software used

[1180] Hardware: Cloud server, user devices (smartphones, PCs)

[1181] Software: Generative AI models (e.g., GPT-4), HTML / CSS design templates, SMTP server, SNS integration API

[1182] Specific examples of processing

[1183] The user list includes email addresses and purchase histories of existing customers. Based on this, a generative AI model generates new product information and articles, which are then sent out as digital bulletins every Monday. For example, a bulletin titled "Recommended Fashion for This Fall" might feature new seasonal products.

[1184] Example prompt for a generative AI model:

[1185] "User ID: 1

[1186] Purchase history: ['Autumn jacket', 'Business shoes']

[1187] Market trends: ['Autumn / Winter Fashion', 'New Material Jackets']

[1188] This system allows online shopping sites to effectively connect users with suppliers, discover new products, and develop new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1190] Step 1:

[1191] The server collects user purchasing history and market trend data. The input is the existing user database and market trend database. The server temporarily stores this data in memory to input it into the generative AI model. The output is a dataset to be passed to the generative AI model.

[1192] Step 2:

[1193] The server feeds the dataset into the generative AI model to generate product information and feature articles. The input is collected user purchasing history and market trend data. The generative AI model analyzes the data using machine learning algorithms to generate interesting product information and feature articles. The output is the content of the generated digital bulletin board.

[1194] Step 3:

[1195] The server embeds the generated content into an HTML / CSS design template to create a digital tile-ban. The input is the generated content and the design template. An HTML engine is used to embed the content into the template. The output is an HTML file of the completed digital tile-ban.

[1196] Step 4:

[1197] The server embeds the digital kawaraban in the email template and prepares for mass distribution. The input is the completed HTML file of the digital kawaraban and the email template. The server embeds the digital kawaraban in the email template and prepares to send the email via the SMTP server. The output is the email data that has been prepared for sending.

[1198] Step 5:

[1199] The server distributes digital bulletins on specific days of the week. The input is email data that has been prepared for sending and an existing user list. Using the schedule management function, emails are sent en masse via an SMTP server, for example, every Monday. The output is the digital bulletin distributed to users.

[1200] Step 6:

[1201] Users can share the digital bulletin they receive on social media or other online platforms. The input is the digital bulletin they received and the operation of the share button. When a user clicks the share button, the information can be easily spread through the social media sharing API. The output is the shared link and the extent to which it has been shared.

[1202] Step 7:

[1203] The server analyzes the information spread on social media and analyzes the behavioral history of new users. The input is the access log of the spread link and social media data. This data is analyzed using an analysis engine to generate a list of new customers. The output is a list of newly identified potential customers.

[1204] Step 8:

[1205] The server distributes limited coupons to a new customer list. The input is a newly generated potential customer list and coupon information. An advertising email with a limited coupon is sent to the new customers using an SMTP server. The output is the email with the coupon delivered to the new customer.

[1206] Step 9:

[1207] A user receives a coupon and purchases a product on an online shopping site. The input is the received email with the coupon and the purchase process on the online shopping site. The user uses the coupon to purchase the product on the online shopping site at a discounted price. The output is the completed purchase transaction.

[1208] Through these steps, the system effectively connects users with suppliers, discovers new products, and develops new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1209] 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.

[1210] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence and an emotion engine to discover new products and cultivate new customers. Specific examples and processing steps are shown below.

[1211] Creation and publication of digital bulletin boards

[1212] server

[1213] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[1214] Digital Newspaper Distribution

[1215] server

[1216] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[1217] Information dissemination by customers

[1218] User

[1219] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[1220] Developing new customers

[1221] server

[1222] The generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. Furthermore, the emotion engine recognizes the user's emotions and analyzes their feedback to optimize marketing measures.

[1223] Use of emotion engine

[1224] server

[1225] The emotion engine monitors the user's emotional state in real time and analyzes the emotional data. For example, when a user browses a digital newspaper, their facial expressions and reactions are captured via a camera or microphone, and the data is used to determine the user's current emotions. This emotional data is then combined with the results of analysis by generative AI to provide customized product recommendations based on the user's interests.

[1226] Specific examples

[1227] 1. Creation and publication of digital bulletin boards

[1228] Server: Generative AI and an emotion engine create a digital bulletin board containing information about autumn sales and articles introducing popular products. Based on the user's past purchasing history and current emotional data, seasonal products are featured as "September's Recommended Products."

[1229] 2. Digital Newspaper Distribution

[1230] Server: The digital bulletin board generated on September 1st is sent simultaneously to 5,000 existing customers via email and push notification. In addition, the timing of delivery is optimized based on individual sentiment data.

[1231] 3. Customers spread information

[1232] User: Views the digital bulletin received via email and evaluates the products. When sharing a specific page on Instagram, the user adds the comment "This fall's trend! Recommended products featured."

[1233] 4. Developing new customers

[1234] Server: Analyze engagement with the information shared on social media and identify 300 new potential customers. Based on their emotional data, deliver more personalized, exclusive offers, and 50 new sign-ups.

[1235] By incorporating an emotion engine, feedback based on the user's emotional state can be obtained, which can be used to optimize the content and distribution timing of digital bulletins, thereby improving user satisfaction and enabling effective marketing measures.

[1236] The processing flow will be explained below.

[1237] Creation and publication of digital bulletin boards

[1238] server

[1239] Step 1:

[1240] Obtain historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[1241] Step 2:

[1242] Use web scraping tools to gather the latest market trend information and news articles. Scrape and retrieve the required data from the specified website.

[1243] Step 3:

[1244] Preprocess and format the acquired data, filter out unnecessary data, and extract and organize the necessary information.

[1245] Step 4:

[1246] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[1247] Step 5:

[1248] The generated content is embedded into HTML / CSS templates, and text and images generated by AI are embedded in the template engine to create a digital bulletin board.

[1249] Step 6:

[1250] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[1251] Digital Newspaper Distribution

[1252] server

[1253] Step 1:

[1254] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[1255] Step 2:

[1256] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[1257] Step 3:

[1258] Embed the digital bulletin in an email template using a template engine, combining the dynamically generated email body with the digital bulletin.

[1259] Step 4:

[1260] The generated email is sent to the mail server for mass distribution, and then sent to customers via the SMTP server.

[1261] Information dissemination by customers

[1262] User

[1263] Step 1:

[1264] Open the digital bulletin received by email or push notification. Check the notification in your email client or dedicated app.

[1265] Step 2:

[1266] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[1267] Step 3:

[1268] Click the "Share" button in the news bulletin board. The JavaScript-implemented share button will open the social media sharing dialog.

[1269] Step 4:

[1270] Share the information from the digital bulletin on social media, such as Facebook or Twitter, by posting links and comments.

[1271] Developing new customers

[1272] server

[1273] Step 1:

[1274] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[1275] Step 2:

[1276] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[1277] Step 3:

[1278] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[1279] Step 4:

[1280] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[1281] Use of emotion engine

[1282] server

[1283] Step 1:

[1284] The emotion engine recognizes the user's emotional state, capturing facial expressions and voice data through a camera and microphone.

[1285] Step 2:

[1286] The acquired data is analyzed in real time to determine the user's emotional state, and an emotion analysis algorithm is used to classify the emotion into categories such as positive, negative, or neutral.

[1287] Step 3:

[1288] Emotional data is stored and analyzed cumulatively. Emotional data from viewing digital bulletin boards is added to user profiles.

[1289] Step 4:

[1290] Incorporate sentiment data into generative AI and marketing strategies to generate more personalized content and offers based on sentiment data.

[1291] As a specific example, the generative AI and emotion engine will create a digital bulletin containing information about autumn sales, and while monitoring the emotional data of each user, send it to customers at the optimal timing. If a customer who receives the digital bulletin responds positively, the content and timing of the next distribution will be further optimized based on that reaction.

[1292] In this way, the present invention utilizes digital bulletin boards and incorporates user emotional data to discover new products, cultivate new customers, and achieve effective marketing.

[1293] Example 2

[1294] 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."

[1295] Modern online shopping sites require efficient methods to connect users with suppliers and to discover new products and customers. However, existing systems lack the means to provide personalized recommendations that take into account not only users' past purchase history and market trends, but also their emotional feedback. Furthermore, they have yet to effectively leverage existing customers to naturally spread information and attract new customers.

[1296] 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.

[1297] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information by utilizing existing users, means for discovering new products using generative AI, means for cultivating new customers using the generative AI, means for collecting past purchase history data and market trend information and inputting the collected data into a generative AI model and an emotion engine to generate new product information and feature articles, means for embedding the generated digital information into HTML / CSS templates and publishing them at specified times, and means for collecting user emotion feedback using the emotion engine and using the collected data to customize product proposals for the digital information. This enables personalized proposals based on the user's purchase history and emotions, and enables natural information dissemination through existing users and effective acquisition of new customers.

[1298] "Means of publishing digital information periodically via the Internet" refers to the function of a server publishing content generated using HTML / CSS templates at specified times.

[1299] "Means to connect users and suppliers" refers to the function of utilizing generative AI models and emotion engines to provide appropriate product information based on users' purchasing history and market trend information.

[1300] "Means of spreading information by utilizing existing users" refers to the function that allows existing users to use the share buttons within the digital bulletin board to spread information on social media and other networks.

[1301] "Means of discovering new products using generative AI" refers to the function of a generative AI model that analyzes past purchase history data and market trend information to automatically suggest new products.

[1302] "Means of developing new customers using generative AI" refers to the function in which a generative AI model analyzes shared information and the behavioral history of new users, creates a list of potential customers, and acquires new customers.

[1303] "Means for collecting past purchase history data and market trend information" refers to the function by which the server obtains purchase history data from the database and collects market trend information using an API.

[1304] "Means of inputting data into a generative AI model and emotion engine to generate new product information and feature articles" refers to the function of inputting collected data into a generative AI model along with specific prompt statements, and evaluating the generated content using an emotion engine.

[1305] "A means of embedding the generated digital information into HTML / CSS templates and publishing them at specified times" refers to a scheduling function that embeds content generated by a generative AI model into HTML / CSS templates and automatically publishes them on a regular basis.

[1306] "Means of using an emotion engine to collect user emotional feedback and utilize it for customized product recommendations based on digital information" refers to a function that acquires emotional data from users' facial expressions, voice, etc., and works in conjunction with a generative AI model to make personalized product recommendations.

[1307] This invention is a system for effectively connecting users and suppliers on online shopping sites. Specifically, it utilizes past purchase history data, current market trend information, and emotion data to generate new product information and feature articles, which are then published as digital information. An embodiment of this system is described in detail below.

[1308] 1. Data Collection and Analysis

[1309] server

[1310] The server first retrieves past purchase history data from the database using SQL queries. It also collects current market trend information through APIs, allowing it to understand user purchasing habits and market trends.

[1311] Specific actions

[1312] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[1313] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[1314] 2. Digital Information Generation

[1315] server

[1316] The server inputs the collected data into a generative AI model (such as the GPT series), prompts the model to generate new product information and feature articles, and uses an emotion engine to analyze user emotional feedback and evaluate the quality of the generated content.

[1317] Prompt Sentence Examples

[1318] "Generate articles featuring recommended products for the next month based on users' past purchase history and market trends."

[1319] 3. Publication of digital information

[1320] server

[1321] The generated content is embedded in an HTML / CSS design template. The server schedules the publication of this digital bulletin at specified times. For example, a cron job can be set up to automatically publish the bulletin at 9:00 a.m. on the first of every month.

[1322] Specific actions

[1323] Scheduling: "cron job setting: 0 9 1 php publish_kawaraban.php"

[1324] 4. Digital Information Distribution

[1325] server

[1326] As the publication date approaches, the server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for simultaneous distribution using the email server and push notification server. It can also distribute the newsletter at the optimal time based on each user's emotional data.

[1327] Specific actions

[1328] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[1329] Embed content in email template: Embed the content of the bulletin board in "mail_body.html".

[1330] 5. Information Spread

[1331] User

[1332] Users receive email or app push notifications, open the digital bulletin board, and use the "share" button within the bulletin board to spread the information on social media and other networks. The shared link is assigned a unique tracking ID, allowing the behavior of new users to be tracked.

[1333] Specific actions

[1334] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[1335] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[1336] 6. Finding new customers

[1337] server

[1338] The server analyzes the engagement of the information that has been shared and uses an emotion engine to analyze the emotional data of new users, creating a list of potential customers based on this data and preparing targeted advertisements and personalized offers.

[1339] Specific actions

[1340] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[1341] 7. Personalized product recommendations

[1342] server

[1343] The server then uses the generative AI model again to make personalized product suggestions based on the user's emotional data collected by the emotion engine, thereby enabling the provision of products that match the user's interests and improving customer satisfaction.

[1344] Specific actions

[1345] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[1346] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[1347] Through the above steps, this invention realizes personalized proposals based on the user's purchasing history and emotions, aiming to spread information naturally through existing users and effectively acquire new customers.

[1348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1349] Step 1:

[1350] The server collects past purchase history data from a database. It uses an SQL query to extract order history made within a specific period. The input is the database connection information and query conditions, and the output is a list of purchase history data. The server stores the data retrieved by the query in its internal memory.

[1351] Specific behavior:

[1352] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[1353] Data retrieved from the database is stored in memory

[1354] Step 2:

[1355] The server collects market trend information through APIs. It sends API requests to obtain information on popular products in the current market. The input is the API endpoint and query parameters, and the output is market trend information data. The server analyzes the obtained data and extracts the required information.

[1356] Specific behavior:

[1357] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[1358] Analyzing acquired data and extracting necessary information

[1359] Step 3:

[1360] The server inputs the collected purchase history data and market trend information into the generative AI model, which uses prompt statements to generate new product information and feature articles. The inputs are purchase history data, market trend information, and prompt statements, and the output is the generated content.

[1361] Specific behavior:

[1362] Prompt: "Generate an article highlighting recommended products for the next month based on the user's past purchase history and market trends."

[1363] Get content generated from generative AI models

[1364] Step 4:

[1365] The server uses an emotion engine to collect user emotion feedback and evaluate the quality of the generated content. The input is the generated content and user emotion data, and the output is the emotion analysis result. The server uses this feedback to regenerate optimized content.

[1366] Specific behavior:

[1367] Collect facial and voice data

[1368] Data analysis using emotion engine

[1369] Regenerating content using feedback results

[1370] Step 5:

[1371] The server embeds the generated content into an HTML / CSS template to create a digital bulletin board. This digital bulletin board is scheduled for publication based on a specified timing. The input is the generated content and HTML / CSS template, and the output is the digital bulletin board.

[1372] Specific behavior:

[1373] Embedding content into templates

[1374] Publication schedule settings: "cron job settings: 0 9 1 php publish_kawaraban.php"

[1375] Step 6:

[1376] As the publication date approaches, the server retrieves the target customer list from the database. It embeds the digital bulletin in an email template and sends it out all at once using the email server and push notification server. The input is the database connection information and email template, and the output is the target customer list and the email to be sent.

[1377] Specific behavior:

[1378] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[1379] Embed content in email template: Embed the contents of the bulletin board in "mail_body.html"

[1380] Add to email delivery queue: "INSERT INTO email_queue (recipient, content) VALUES ('user@example.com', 'mail_body.html');"

[1381] Step 7:

[1382] Users receive an email or push notification from the app, open the digital bulletin board, and use the "share" button provided within the bulletin board to spread information on social media, etc. The input is the click of the share button and the user's selected social media, and the output is the shared link.

[1383] Specific behavior:

[1384] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[1385] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[1386] Step 8:

[1387] The server analyzes the engagement of the disseminated information and creates a new potential customer list. It uses an emotion engine to analyze the user's emotion data and prepare targeted advertisements and personalized offers. The input is the shared log and emotion data, and the output is a potential customer list and advertising offers.

[1388] Specific behavior:

[1389] Engagement data analysis: SELECT COUNT() FROM share_logs WHERE platform='Instagram';

[1390] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[1391] Step 9:

[1392] The server uses the emotion engine to collect emotional data and then uses the generative AI model to make personalized product recommendations. The input is emotional data and purchase history data, and the output is personalized product recommendations.

[1393] Specific behavior:

[1394] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[1395] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[1396] (Application example 2)

[1397] 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."

[1398] Traditional online shopping sites have limited means to effectively connect users and suppliers, making it difficult to acquire new customers and improve customer satisfaction. Furthermore, personalized marketing strategies are difficult due to a lack of product recommendations that reflect user sentiment and real-time feedback.

[1399] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically issuing digital information via the Internet, means for connecting users and suppliers, and means for disseminating information by utilizing existing users. This makes it possible to discover new products and develop new customers using generative AI and an emotion engine. Furthermore, by including means for analyzing user emotion data using smart devices and making personalized product recommendations, and means for periodically delivering digital information to customers, it is possible to improve user satisfaction and realize effective marketing measures.

[1400] The "Internet" is a system for exchanging information through computer networks and a technology that forms a widespread communications network.

[1401] "Periodic" means something that is repeated at regular intervals.

[1402] "Digital information" means information in a form that is stored or transmitted electronically.

[1403] "User" refers to an individual or organization that uses the system or service.

[1404] "Supplier" means an individual or entity that provides goods or services.

[1405] "Generative artificial intelligence" refers to a system that uses machine learning and deep learning techniques to derive new results from data.

[1406] A "smart device" is a device that can connect to the Internet and is an electronic device with advanced functionality.

[1407] "Emotional data" refers to information about the user's emotional state, and is obtained by analyzing facial expressions and voice.

[1408] "Personalization" means customizing to suit the characteristics and preferences of individual users.

[1409] "Product recommendation" is the act of presenting appropriate products or services to users.

[1410] "Distribution" refers to the act of sending information or data to a specific recipient.

[1411] The present invention is a system for effectively connecting users and suppliers, periodically issuing digital information and utilizing generative AI and emotion engines to discover new products and develop new customers. The following describes in detail the embodiments of the present invention.

[1412] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is embedded into a design template using HTML / CSS, and the resulting digital bulletin board is created. The system is configured to publish this digital bulletin board at a specified time (for example, the beginning of the month or a specific day of the week).

[1413] Next, as the regular publication date approaches, the server automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[1414] The system also includes a means for analyzing the user's emotional data using smart devices. This analysis of emotional data utilizes the camera and microphone on the user's smartphone. This allows emotions to be captured from the user's facial expressions and voice, and then analyzed by the emotion engine. For example, if the user smiles at the camera, the system detects the emotion "fun" and recommends relaxation-related products based on that. If the user's facial expression is tired, the system suggests energy drinks and fitness-related products.

[1415] By combining the analyzed emotional data with purchase history data, personalized product recommendations are made. When a user shows interest in a product, emotional feedback is provided on the spot, and the system can improve the accuracy of product recommendations in real time based on this feedback.

[1416] As a concrete example,

[1417] When a user smiles into the camera, the system detects the emotion "happy" and recommends relaxation-related products based on that emotion.

[1418] On the other hand, if the person looks tired, we will suggest energy drinks or fitness-related products.

[1419] Also, an example of a prompt for the generation AI is:

[1420] Prompt: When the user smiles, suggest three relaxation-related products.

[1421] There is.

[1422] In this way, the server periodically publishes digital information via the Internet, providing a means to connect users and suppliers, and it is also possible to discover new products and develop new customers using generative artificial intelligence and emotion engines.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] The server collects historical purchase data and current market trend information. This information is extracted from a database. The input is user purchase data, and the output is a dataset for analysis. Specifically, the server uses SQL queries to retrieve the required information from the database.

[1426] Step 2:

[1427] The server inputs the collected data into the generative AI model to generate new product information and feature articles. The input is the collected purchase history data and market trend information, and the output is the generated content. Specifically, the server prompts the generative AI model to generate text and images.

[1428] Step 3:

[1429] The generated content is embedded in an HTML / CSS template and configured as a digital tile print. The input is the generated content, and the output is the digital tile print. Specifically, the server uses a template engine to format the content into a web page.

[1430] Step 4:

[1431] As the regular publication date approaches, the server retrieves the list of customers to be distributed from the database and embeds the digital bulletin in the email template. The input is the customer list and the digital bulletin, and the output is an email ready to be distributed. Specifically, the server generates an email for each customer on the list.

[1432] Step 5:

[1433] The server uses a mail server and a push notification server to efficiently deliver the digital bulletin to customers. The input is the prepared email, and the output is the digital bulletin that arrives in the customer's inbox. Specifically, the server sends the email using the SMTP protocol.

[1434] Step 6:

[1435] A user's smart device (e.g., a smartphone) uses a camera and microphone to capture the user's facial expressions and voice and collect emotional data. The input is the user's facial expressions and voice, and the output is emotional data. Specifically, the device collects data using OpenCV and a voice recognition library.

[1436] Step 7:

[1437] The device analyzes the collected emotion data with an emotion engine to determine the user's current emotion. The input is the captured emotion data, and the output is the analyzed emotional state. Specifically, the device uses a deep learning model to classify the emotion.

[1438] Step 8:

[1439] The server then makes personalized product recommendations based on the analyzed emotional data. The input is the analyzed emotional state and purchase history data, and the output is a list of recommended products. Specifically, the server selects products using a recommendation algorithm.

[1440] Step 9:

[1441] If the user is interested in a product, they will receive emotional feedback on the spot, which will improve the system's recommendation accuracy. The input is the user's additional feedback, and the output is an improved recommendation model. Specifically, the server updates the model in real time.

[1442] Step 10:

[1443] The server analyzes the information from the spread digital bulletin board and analyzes the behavioral history and emotional data of new users. The input is the spread information and the behavioral history of new users, and the output is a list of potential customers. Specifically, the server uses big data analysis tools.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] [Fourth embodiment]

[1448] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1449] 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.

[1450] 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).

[1451] 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.

[1452] 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.

[1453] 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).

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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.

[1460] 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."

[1461] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence to discover new products and cultivate new customers. A specific example of this system is shown below.

[1462] Creation and publication of digital bulletin boards

[1463] server

[1464] First, the server collects past purchase history data and current market trend information. This data is input into a generation AI, which generates new product information and feature articles based on the purchase data and trend information. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[1465] Digital Newspaper Distribution

[1466] server

[1467] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[1468] Information dissemination by customers

[1469] User

[1470] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[1471] Developing new customers

[1472] server

[1473] Generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. This data is obtained through the analysis of social media and access logs. New marketing measures are implemented targeting the potential customers identified from the analysis results. For example, advertising emails with exclusive coupons can be sent to these potential customers to encourage new registrations and purchases.

[1474] Specific examples

[1475] 1. Creation and publication of digital bulletin boards

[1476] Server: The AI ​​generator creates a digital bulletin board containing summer sales information and articles introducing popular products. For example, a special feature on "Recommended Products for August" will feature clothing and accessories that are in high demand as the seasons change.

[1477] 2. Digital Newspaper Distribution

[1478] Server: The digital bulletin board generated on August 1st will be sent to 10,000 existing customers at once. The delivery method will be email and push notification.

[1479] 3. Customers spread information

[1480] User: Views a digital newsletter received via email and shares a specific page on Facebook, commenting, "Perfect items for this summer!"

[1481] 4. Developing new customers

[1482] Server: By analyzing information spread on social media, 500 new potential customers are identified. By sending advertisements with exclusive coupons to these potential customers, 50 new customers will sign up.

[1483] As described above, the present invention utilizes digital bulletin boards to promote the dissemination of information by existing users and efficiently realize the discovery of new products and new customers, thereby revitalizing the distribution of online shopping sites.

[1484] The processing flow will be explained below.

[1485] Creation and publication of digital bulletin boards

[1486] server

[1487] Step 1:

[1488] Retrieve historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[1489] Step 2:

[1490] Use web scrapers to gather trending information and news articles. Use scraping tools to get the latest market trends and new product information.

[1491] Step 3:

[1492] Preprocessing acquired data and converting it into a specified format, filtering out unnecessary data, and extracting and organizing necessary information.

[1493] Step 4:

[1494] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[1495] Step 5:

[1496] The generated content is embedded into an HTML / CSS template. The text and images created by the generative AI are placed in the design template to create a digital bulletin board.

[1497] Step 6:

[1498] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[1499] Digital Newspaper Distribution

[1500] server

[1501] Step 1:

[1502] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[1503] Step 2:

[1504] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[1505] Step 3:

[1506] Use a template engine to embed the digital kawaraban into the email template. Use a template engine such as Jinja2 to dynamically generate the email body.

[1507] Step 4:

[1508] The generated email is sent to the mail server for mass distribution, and the digital bulletin is sent to the customer via the SMTP server.

[1509] Information dissemination by customers

[1510] User

[1511] Step 1:

[1512] The user opens the digital bulletin board received via email or app push notification. The user checks the notification in their email client or dedicated app.

[1513] Step 2:

[1514] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[1515] Step 3:

[1516] Click the "Share" button in the news bulletin board. The share button is implemented in JavaScript, and the SNS sharing dialog opens.

[1517] Step 4:

[1518] Share the information from the digital bulletin on social media. Post links and comments using your Facebook, Twitter, or other accounts.

[1519] Developing new customers

[1520] server

[1521] Step 1:

[1522] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[1523] Step 2:

[1524] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[1525] Step 3:

[1526] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[1527] Step 4:

[1528] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[1529] The above are the specific processing steps for carrying out the invention.

[1530] Example 1

[1531] 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."

[1532] In conventional online shopping systems, the connection between users and suppliers is weak, making it difficult to efficiently discover new products and cultivate new customers. Furthermore, there is a lack of effective ways to utilize information dissemination by existing users, which means that product information remains limited. This creates the problem of hindering the vitalization of distribution on online shopping sites.

[1533] 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.

[1534] In this invention, the server includes means for automatically collecting purchase history data and market trend information and inputting it into a generating AI, means for generating product information and feature articles based on the purchase data and trend information using the generating AI, and means for creating digital information by embedding the generated content into design templates, which effectively connects users and suppliers, making it possible to spread information and develop new customers.

[1535] "Purchase history data" is data that includes detailed information about products that a user has purchased in the past.

[1536] "Market trend information" is data that indicates consumer interests and purchasing trends in the market.

[1537] "Generative AI" is software that has the ability to analyze data using machine learning algorithms and generate new information.

[1538] A "generative AI model" is an artificial intelligence model that generates new text and content based on diverse data.

[1539] A "design template" is a template that defines the layout and style for creating web pages and digital content.

[1540] "Digital information" refers generally to content that is electronically generated and distributed.

[1541] A "mail server" is a server that manages the sending and receiving of e-mail.

[1542] A "push notification server" is a server that sends notifications to mobile and web applications in real time.

[1543] "User" refers to a consumer who uses an online shopping site.

[1544] "Social media" refers to platforms where people share information online, such as Facebook and Twitter.

[1545] A "new customer list" is a list of potential new customers to target.

[1546] "Marketing strategies" refer to the planning and execution of advertising and promotions targeted at specific customer segments.

[1547] "Information diffusion" refers to the dissemination of information received by existing users to other users or networks.

[1548] This invention is a system for effectively connecting users and suppliers on online shopping sites, and in particular, it utilizes purchase history data and market trend information to periodically publish digital information, and uses generative artificial intelligence to discover new products and cultivate new customers. Specific implementation methods of this system are described below.

[1549] Creation and publication of digital bulletin boards

[1550] server:

[1551] The server first retrieves the user's purchase history data from a purchase history database. This database contains detailed information about the products the user has purchased in the past. In addition, it sends a request to an API (e.g., Google Trends) to obtain market trend information. The data collected in this way is input into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt sentence that can be used is as follows:

[1552] "Please create an article featuring new products for August based on the following data."

[1553] The generative AI model generates new product information and feature articles based on this data. This content is embedded in a design template (HTML / CSS) on the server and formatted as a digital bulletin board.

[1554] Scheduling and preparing for publication of the digital bulletin

[1555] server:

[1556] The server uses a scheduling function to set the publication date for the digital bulletin (for example, the beginning of the month or a specific day of the week). As the publication date approaches, the server automatically prepares for publication. Specifically, it retrieves the target customer list from the database and uses a mail server (for example, SendGrid) or push notification server (for example, Firebase Cloud Messaging) to simultaneously distribute the digital bulletin.

[1557] Information dissemination by customers

[1558] User:

[1559] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button that users can click to share the information on social media (e.g., Facebook or Twitter). This sharing function is expected to help the content of the digital bulletin reach many potential customers.

[1560] Developing new customers

[1561] server:

[1562] The server uses social media APIs and web analytics tools (e.g., Google Analytics) to collect and analyze responses to the spread of information. This data is input into a generative AI model, which creates a list of potential customers based on the responses and behavioral history of new users. Based on this list of new customers, the server implements new marketing initiatives. For example, it sends advertising emails with exclusive coupons to newly identified potential customers to encourage new registrations and purchases.

[1563] Specific examples

[1564] As an example, a digital bulletin is generated containing summer sale information and articles introducing popular products. For example, a "Featured August Products" section may include articles featuring clothing and accessories, which are in high demand as the seasons change. This digital bulletin is distributed simultaneously to 10,000 existing customers via email and push notifications on August 1st, the beginning of the month. In addition, when users share the digital bulletin they receive on social media, 500 new potential customers are identified, and advertising emails with exclusive coupons are sent to these customers. This will result in the registration of 50 new customers, revitalizing the distribution of the online shopping site.

[1565] The above is a specific embodiment for carrying out the present invention. This system realizes efficient product discovery and new customer acquisition, and can promote the revitalization of distribution on online shopping sites.

[1566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1567] Specific processing steps from collecting purchase history data and market trend information to publishing a digital bulletin board

[1568] Step 1:

[1569] collection

[1570] Server: Sends SQL queries to the purchase history database to retrieve past purchase history data, and sends requests to the market trend information API to collect current market trend data.

[1571] Input: Purchase history database, trend information API request

[1572] Data processing: Execute SQL queries, send API requests

[1573] Output: Purchase history data, trend information

[1574] Step 2:

[1575] Data Entry and Content Generation

[1576] Server: The acquired purchase history data and market trend information are input into the generative AI model, which generates new product information and feature articles based on this data.

[1577] Input: Purchase history data, market trend information, prompt text

[1578] Data calculation: Input data into generative AI models, and generate content using the models

[1579] Output: Generated product information, featured articles

[1580] Specific operation: Send a request to the API of a generative AI model (e.g., OpenAI GPT-3), enter a prompt, and generate content.

[1581] Step 3:

[1582] Creating digital news bulletins

[1583] Server: The generated new product information and feature articles are embedded into HTML / CSS design templates, which creates digital bulletin boards.

[1584] Input: Generated product information, feature articles, design templates

[1585] Data processing: Inserting content into HTML / CSS templates

[1586] Output: Digital woodblock print

[1587] What it does: Uses a template engine to insert generated content into an HTML file and style it.

[1588] Step 4:

[1589] Scheduling and publication preparation

[1590] Server: The publication date of the digital bulletin is set using the scheduling function. As the publication date approaches, the target customer list is retrieved from the database and the digital bulletin is embedded in the email template.

[1591] Inputs: Digital bulletin, scheduling, customer list

[1592] Data processing: Inserting digital bulletin boards into email templates, acquiring customer lists

[1593] Output: Email template for delivery

[1594] Specific operations: Use a scheduler program to set a specific date and time, retrieve a customer list using an SQL query, and insert a digital bulletin board into a template.

[1595] Step 5:

[1596] Digital Newspaper Distribution

[1597] Server: Using a mail server or push notification server, email templates are sent to existing customers en masse.

[1598] Input: Email template for delivery, customer list

[1599] Data calculation: Sending email / push notifications

[1600] Output: Distributed digital bulletin board

[1601] Specific operation: Sends a request to a mail server (e.g., SendGrid) or push notification server (e.g., Firebase Cloud Messaging) and executes delivery.

[1602] Step 6:

[1603] Information dissemination by customers

[1604] User: Opens the digital bulletin they receive and spreads the information by clicking the share button on social media.

[1605] Input: Received digital bulletin board

[1606] Data processing: Sharing information on social media

[1607] Output: Information shared on social media

[1608] Specific actions: Click the share button in the digital news bulletin and share the information on the social media posting screen.

[1609] Step 7:

[1610] Developing new customers

[1611] Server: Collects and analyzes responses to the information spread using social media APIs and web analytics tools, and creates a list of new customers. Marketing strategies are implemented based on this list.

[1612] Input: Social media response data, access log data

[1613] Data calculation: Analysis of reaction data, creation of new customer list

[1614] Output: New customer list, marketing measures

[1615] What it does: Analyze data using a generative AI model and send promotional emails with exclusive coupons to newly identified potential customers.

[1616] These are the specific processing steps of the program for this system. At each step, appropriate data processing and calculations are performed based on the input data, and an output is generated as a result.

[1617] (Application example 1)

[1618] 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."

[1619] Current online shopping sites lack effective ways to connect users and suppliers. They also lack mechanisms for utilizing existing users to spread information, and methods for using generative artificial intelligence to discover new products and develop new customers. This makes it difficult to revitalize online shopping sites, and there are challenges in anticipating increased sales.

[1620] 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.

[1621] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information using existing users, means for discovering new products using a generating AI, means for acquiring new customers using the generating AI, means for publishing digital bulletins on specific days of the week, and means for embedding generated content into email templates and preparing for simultaneous distribution. This allows online shopping sites to effectively connect users with suppliers, discover new products, and acquire new customers. Furthermore, spreading information through existing users makes it easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1622] The "Internet" refers to a global network of computers that enables the collection, sharing, and communication of information.

[1623] "Periodic" means something that is repeated at regular intervals.

[1624] "Digital information" refers to information that is stored, processed, and transmitted electronically, and includes text, images, audio, and video.

[1625] "User" refers to a customer who uses a service such as an online shopping site.

[1626] "Provider" refers to a company or individual that provides goods or services to Users.

[1627] "Generative artificial intelligence" refers to technology that uses machine learning and deep learning to analyze data and generate new information like a human.

[1628] "New customers" refer to new customers with whom we have not previously conducted business or had contact.

[1629] "Publication" means the regular release of new information.

[1630] An "email template" refers to a template for creating email content according to a certain format.

[1631] "Simultaneous distribution" refers to sending the same information to a large number of users at the same time.

[1632] "Digital Kawaraban" refers to electronic newsletters and magazines generated by online shopping sites.

[1633] "Social media" refers to online platforms that allow people to share information and interact over the internet.

[1634] "Information diffusion" refers to spreading information to many people.

[1635] "New products" refer to products that have been newly added to the existing lineup.

[1636] "Market trends" refers to current market trends, trends, and changes in demand.

[1637] "Limited coupon" refers to a discount coupon that can only be used under certain conditions.

[1638] "Push notifications" refers to the function that forces notifications from applications to be displayed on devices such as smartphones and tablets.

[1639] "Potential Customer" refers to a person who has the potential to become a customer in the future.

[1640] A system embodying the invention includes the following components:

[1641] 1. A means of publishing digital information periodically via the Internet

[1642] The server collects market trend information and user purchasing history data, and uses a generative AI model to generate new product information and feature articles. This generated content is embedded in HTML / CSS design templates and published periodically as a digital bulletin board.

[1643] 2. A means of connecting users and suppliers

[1644] The server uses a generative AI model to analyze users' purchasing patterns and market trends, and selects individual recommended products, providing users with appropriate product information from suppliers.

[1645] 3. Using existing users to spread information

[1646] Digital bulletin boards have share buttons that users can click to easily spread information across social media and other online platforms, allowing a single piece of information to reach many potential customers.

[1647] 4. A means of discovering new products using generative artificial intelligence

[1648] The server inputs market trend information and purchase history data into a generative AI model to discover new products. In this process, large amounts of data are analyzed using machine learning algorithms to narrow down interesting product candidates.

[1649] 5. Means of acquiring new customers using the generative artificial intelligence

[1650] When information spreads on social media, the server analyzes the behavioral history of new customers. This is done by analyzing access logs and SNS data to generate a new list of potential customers. Limited coupons can be distributed to these customers to promote new customers.

[1651] 6. How to publish digital bulletins on specific days of the week

[1652] The server has a schedule management function, for example, to publish a new digital bulletin every Monday. This schedule management is to ensure regular information transmission.

[1653] 7. A way to embed generated content into email templates and prepare them for mass distribution

[1654] The newly generated digital bulletin board is embedded in an email template and prepared for mass distribution. The server sends the email to multiple users simultaneously via an SMTP server.

[1655] Hardware and software used

[1656] Hardware: Cloud server, user devices (smartphones, PCs)

[1657] Software: Generative AI models (e.g., GPT-4), HTML / CSS design templates, SMTP server, SNS integration API

[1658] Specific examples of processing

[1659] The user list includes email addresses and purchase histories of existing customers. Based on this, a generative AI model generates new product information and articles, which are then sent out as digital bulletins every Monday. For example, a bulletin titled "Recommended Fashion for This Fall" might feature new seasonal products.

[1660] Example prompt for a generative AI model:

[1661] "User ID: 1

[1662] Purchase history: ['Autumn jacket', 'Business shoes']

[1663] Market trends: ['Autumn / Winter Fashion', 'New Material Jackets']

[1664] This system allows online shopping sites to effectively connect users with suppliers, discover new products, and develop new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1666] Step 1:

[1667] The server collects user purchasing history and market trend data. The input is the existing user database and market trend database. The server temporarily stores this data in memory to input it into the generative AI model. The output is a dataset to be passed to the generative AI model.

[1668] Step 2:

[1669] The server feeds the dataset into the generative AI model to generate product information and feature articles. The input is collected user purchasing history and market trend data. The generative AI model analyzes the data using machine learning algorithms to generate interesting product information and feature articles. The output is the content of the generated digital bulletin board.

[1670] Step 3:

[1671] The server embeds the generated content into an HTML / CSS design template to create a digital tile-ban. The input is the generated content and the design template. An HTML engine is used to embed the content into the template. The output is an HTML file of the completed digital tile-ban.

[1672] Step 4:

[1673] The server embeds the digital kawaraban in the email template and prepares for mass distribution. The input is the completed HTML file of the digital kawaraban and the email template. The server embeds the digital kawaraban in the email template and prepares to send the email via the SMTP server. The output is the email data that has been prepared for sending.

[1674] Step 5:

[1675] The server distributes digital bulletins on specific days of the week. The input is email data that has been prepared for sending and an existing user list. Using the schedule management function, emails are sent en masse via an SMTP server, for example, every Monday. The output is the digital bulletin distributed to users.

[1676] Step 6:

[1677] Users can share the digital bulletin they receive on social media or other online platforms. The input is the digital bulletin they received and the operation of the share button. When a user clicks the share button, the information can be easily spread through the social media sharing API. The output is the shared link and the extent to which it has been shared.

[1678] Step 7:

[1679] The server analyzes the information spread on social media and analyzes the behavioral history of new users. The input is the access log of the spread link and social media data. This data is analyzed using an analysis engine to generate a list of new customers. The output is a list of newly identified potential customers.

[1680] Step 8:

[1681] The server distributes limited coupons to a new customer list. The input is a newly generated potential customer list and coupon information. An advertising email with a limited coupon is sent to the new customers using an SMTP server. The output is the email with the coupon delivered to the new customer.

[1682] Step 9:

[1683] A user receives a coupon and purchases a product on an online shopping site. The input is the received email with the coupon and the purchase process on the online shopping site. The user uses the coupon to purchase the product on the online shopping site at a discounted price. The output is the completed purchase transaction.

[1684] Through these steps, the system effectively connects users with suppliers, discovers new products, and develops new customers. In addition, by spreading information through existing users, it becomes easier to approach potential customers, revitalizing the site's distribution and increasing sales.

[1685] 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.

[1686] The present invention is a system for effectively connecting users and suppliers on online shopping sites, periodically publishing digital information and utilizing generative artificial intelligence and an emotion engine to discover new products and cultivate new customers. Specific examples and processing steps are shown below.

[1687] Creation and publication of digital bulletin boards

[1688] server

[1689] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is then embedded into a design template using HTML / CSS, resulting in the creation of a digital bulletin board. This digital bulletin board is set up in the system to be published at a specified time (for example, the beginning of the month or a specific day of the week).

[1690] Digital Newspaper Distribution

[1691] server

[1692] As the regular publication date approaches, the system automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[1693] Information dissemination by customers

[1694] User

[1695] Users open the digital bulletin board they receive via email or app push notification. The bulletin board has a "share" button, which users can click to spread the information on social media and other networks. This spread is expected to reach many potential customers.

[1696] Developing new customers

[1697] server

[1698] The generative AI analyzes the information that has been spread and creates a list of potential customers based on the reactions and behavioral history of new users. Furthermore, the emotion engine recognizes the user's emotions and analyzes their feedback to optimize marketing measures.

[1699] Use of emotion engine

[1700] server

[1701] The emotion engine monitors the user's emotional state in real time and analyzes the emotional data. For example, when a user browses a digital newspaper, their facial expressions and reactions are captured via a camera or microphone, and the data is used to determine the user's current emotions. This emotional data is then combined with the results of analysis by generative AI to provide customized product recommendations based on the user's interests.

[1702] Specific examples

[1703] 1. Creation and publication of digital bulletin boards

[1704] Server: Generative AI and an emotion engine create a digital bulletin board containing information about autumn sales and articles introducing popular products. Based on the user's past purchasing history and current emotional data, seasonal products are featured as "September's Recommended Products."

[1705] 2. Digital Newspaper Distribution

[1706] Server: The digital bulletin board generated on September 1st is sent simultaneously to 5,000 existing customers via email and push notification. In addition, the timing of delivery is optimized based on individual sentiment data.

[1707] 3. Customers spread information

[1708] User: Views the digital bulletin received via email and evaluates the products. When sharing a specific page on Instagram, the user adds the comment "This fall's trend! Recommended products featured."

[1709] 4. Developing new customers

[1710] Server: Analyze engagement with the information shared on social media and identify 300 new potential customers. Based on their emotional data, deliver more personalized, exclusive offers, and 50 new sign-ups.

[1711] By incorporating an emotion engine, feedback based on the user's emotional state can be obtained, which can be used to optimize the content and distribution timing of digital bulletins, thereby improving user satisfaction and enabling effective marketing measures.

[1712] The processing flow will be explained below.

[1713] Creation and publication of digital bulletin boards

[1714] server

[1715] Step 1:

[1716] Obtain historical purchase data from the database. Use an SQL query to extract purchase data from the past year.

[1717] Step 2:

[1718] Use web scraping tools to gather the latest market trend information and news articles. Scrape and retrieve the required data from the specified website.

[1719] Step 3:

[1720] Preprocess and format the acquired data, filter out unnecessary data, and extract and organize the necessary information.

[1721] Step 4:

[1722] Preprocessed data is input into generative AI, which analyzes the data and automatically generates new product descriptions and feature articles based on purchasing habits and trends.

[1723] Step 5:

[1724] The generated content is embedded into HTML / CSS templates, and text and images generated by AI are embedded in the template engine to create a digital bulletin board.

[1725] Step 6:

[1726] Save the completed digital kawaraban in storage. Upload the HTML file to an FTP server or cloud storage for storage.

[1727] Digital Newspaper Distribution

[1728] server

[1729] Step 1:

[1730] When the publication date arrives, the scheduling system will automatically trigger a cron job or event scheduler to run on the set date.

[1731] Step 2:

[1732] Retrieve the target customer list from the database. Use an SQL query to extract the email addresses and push notification IDs of active members.

[1733] Step 3:

[1734] Embed the digital bulletin in an email template using a template engine, combining the dynamically generated email body with the digital bulletin.

[1735] Step 4:

[1736] The generated email is sent to the mail server for mass distribution, and then sent to customers via the SMTP server.

[1737] Information dissemination by customers

[1738] User

[1739] Step 1:

[1740] Open the digital bulletin received by email or push notification. Check the notification in your email client or dedicated app.

[1741] Step 2:

[1742] Click on the link in the email or app to view the digital bulletin. Use a web browser or the in-app display function to view the HTML content.

[1743] Step 3:

[1744] Click the "Share" button in the news bulletin board. The JavaScript-implemented share button will open the social media sharing dialog.

[1745] Step 4:

[1746] Share the information from the digital bulletin on social media, such as Facebook or Twitter, by posting links and comments.

[1747] Developing new customers

[1748] server

[1749] Step 1:

[1750] Use social media APIs to analyze information shared on social media, collecting clicks on shared links and engagement data.

[1751] Step 2:

[1752] Analyze access logs to understand the behavioral history of new users. Use data from web analytics tools (e.g., Google Analytics).

[1753] Step 3:

[1754] Leverage generative artificial intelligence to identify potential customers from analytics data, then apply machine learning models to cluster new users who have shown interest.

[1755] Step 4:

[1756] Launch new marketing campaigns based on your lead list. Send promotional emails and push notifications with exclusive coupons and special offers to identified leads.

[1757] Use of emotion engine

[1758] server

[1759] Step 1:

[1760] The emotion engine recognizes the user's emotional state, capturing facial expressions and voice data through a camera and microphone.

[1761] Step 2:

[1762] The acquired data is analyzed in real time to determine the user's emotional state, and an emotion analysis algorithm is used to classify the emotion into categories such as positive, negative, or neutral.

[1763] Step 3:

[1764] Emotional data is stored and analyzed cumulatively. Emotional data from viewing digital bulletin boards is added to user profiles.

[1765] Step 4:

[1766] Incorporate sentiment data into generative AI and marketing strategies to generate more personalized content and offers based on sentiment data.

[1767] As a specific example, the generative AI and emotion engine will create a digital bulletin containing information about autumn sales, and while monitoring the emotional data of each user, send it to customers at the optimal timing. If a customer who receives the digital bulletin responds positively, the content and timing of the next distribution will be further optimized based on that reaction.

[1768] In this way, the present invention utilizes digital bulletin boards and incorporates user emotional data to discover new products, cultivate new customers, and achieve effective marketing.

[1769] Example 2

[1770] 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."

[1771] Modern online shopping sites require efficient methods to connect users with suppliers and to discover new products and customers. However, existing systems lack the means to provide personalized recommendations that take into account not only users' past purchase history and market trends, but also their emotional feedback. Furthermore, they have yet to effectively leverage existing customers to naturally spread information and attract new customers.

[1772] 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.

[1773] In this invention, the server includes means for periodically publishing digital information via the Internet, means for connecting users with suppliers, means for disseminating information by utilizing existing users, means for discovering new products using generative AI, means for cultivating new customers using the generative AI, means for collecting past purchase history data and market trend information and inputting the collected data into a generative AI model and an emotion engine to generate new product information and feature articles, means for embedding the generated digital information into HTML / CSS templates and publishing them at specified times, and means for collecting user emotion feedback using the emotion engine and using the collected data to customize product proposals for the digital information. This enables personalized proposals based on the user's purchase history and emotions, and enables natural information dissemination through existing users and effective acquisition of new customers.

[1774] "Means of publishing digital information periodically via the Internet" refers to the function of a server publishing content generated using HTML / CSS templates at specified times.

[1775] "Means to connect users and suppliers" refers to the function of utilizing generative AI models and emotion engines to provide appropriate product information based on users' purchasing history and market trend information.

[1776] "Means of spreading information by utilizing existing users" refers to the function that allows existing users to use the share buttons within the digital bulletin board to spread information on social media and other networks.

[1777] "Means of discovering new products using generative AI" refers to the function of a generative AI model that analyzes past purchase history data and market trend information to automatically suggest new products.

[1778] "Means of developing new customers using generative AI" refers to the function in which a generative AI model analyzes shared information and the behavioral history of new users, creates a list of potential customers, and acquires new customers.

[1779] "Means for collecting past purchase history data and market trend information" refers to the function by which the server obtains purchase history data from the database and collects market trend information using an API.

[1780] "Means of inputting data into a generative AI model and emotion engine to generate new product information and feature articles" refers to the function of inputting collected data into a generative AI model along with specific prompt statements, and evaluating the generated content using an emotion engine.

[1781] "A means of embedding the generated digital information into HTML / CSS templates and publishing them at specified times" refers to a scheduling function that embeds content generated by a generative AI model into HTML / CSS templates and automatically publishes them on a regular basis.

[1782] "Means of using an emotion engine to collect user emotional feedback and utilize it for customized product recommendations based on digital information" refers to a function that acquires emotional data from users' facial expressions, voice, etc., and works in conjunction with a generative AI model to make personalized product recommendations.

[1783] This invention is a system for effectively connecting users and suppliers on online shopping sites. Specifically, it utilizes past purchase history data, current market trend information, and emotion data to generate new product information and feature articles, which are then published as digital information. An embodiment of this system is described in detail below.

[1784] 1. Data Collection and Analysis

[1785] server

[1786] The server first retrieves past purchase history data from the database using SQL queries. It also collects current market trend information through APIs, allowing it to understand user purchasing habits and market trends.

[1787] Specific actions

[1788] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[1789] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[1790] 2. Digital Information Generation

[1791] server

[1792] The server inputs the collected data into a generative AI model (such as the GPT series), prompts the model to generate new product information and feature articles, and uses an emotion engine to analyze user emotional feedback and evaluate the quality of the generated content.

[1793] Prompt Sentence Examples

[1794] "Generate articles featuring recommended products for the next month based on users' past purchase history and market trends."

[1795] 3. Publication of digital information

[1796] server

[1797] The generated content is embedded in an HTML / CSS design template. The server schedules the publication of this digital bulletin at specified times. For example, a cron job can be set up to automatically publish the bulletin at 9:00 a.m. on the first of every month.

[1798] Specific actions

[1799] Scheduling: "cron job setting: 0 9 1 php publish_kawaraban.php"

[1800] 4. Digital Information Distribution

[1801] server

[1802] As the publication date approaches, the server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for simultaneous distribution using the email server and push notification server. It can also distribute the newsletter at the optimal time based on each user's emotional data.

[1803] Specific actions

[1804] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[1805] Embed content in email template: Embed the content of the bulletin board in "mail_body.html".

[1806] 5. Information Spread

[1807] User

[1808] Users receive email or app push notifications, open the digital bulletin board, and use the "share" button within the bulletin board to spread the information on social media and other networks. The shared link is assigned a unique tracking ID, allowing the behavior of new users to be tracked.

[1809] Specific actions

[1810] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[1811] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[1812] 6. Finding new customers

[1813] server

[1814] The server analyzes the engagement of the information that has been shared and uses an emotion engine to analyze the emotional data of new users, creating a list of potential customers based on this data and preparing targeted advertisements and personalized offers.

[1815] Specific actions

[1816] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[1817] 7. Personalized product recommendations

[1818] server

[1819] The server then uses the generative AI model again to make personalized product suggestions based on the user's emotional data collected by the emotion engine, thereby enabling the provision of products that match the user's interests and improving customer satisfaction.

[1820] Specific actions

[1821] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[1822] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[1823] Through the above steps, this invention realizes personalized proposals based on the user's purchasing history and emotions, aiming to spread information naturally through existing users and effectively acquire new customers.

[1824] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1825] Step 1:

[1826] The server collects past purchase history data from a database. It uses an SQL query to extract order history made within a specific period. The input is the database connection information and query conditions, and the output is a list of purchase history data. The server stores the data retrieved by the query in its internal memory.

[1827] Specific behavior:

[1828] SQL query: "SELECT FROM order_history WHERE purchase_date BETWEEN '2022-01-01' AND '2023-01-01';"

[1829] Data retrieved from the database is stored in memory

[1830] Step 2:

[1831] The server collects market trend information through APIs. It sends API requests to obtain information on popular products in the current market. The input is the API endpoint and query parameters, and the output is market trend information data. The server analyzes the obtained data and extracts the required information.

[1832] Specific behavior:

[1833] API request: "GET https: / / market-trend-api.example.com / trends?category=electronics"

[1834] Analyzing acquired data and extracting necessary information

[1835] Step 3:

[1836] The server inputs the collected purchase history data and market trend information into the generative AI model, which uses prompt statements to generate new product information and feature articles. The inputs are purchase history data, market trend information, and prompt statements, and the output is the generated content.

[1837] Specific behavior:

[1838] Prompt: "Generate an article highlighting recommended products for the next month based on the user's past purchase history and market trends."

[1839] Get content generated from generative AI models

[1840] Step 4:

[1841] The server uses an emotion engine to collect user emotion feedback and evaluate the quality of the generated content. The input is the generated content and user emotion data, and the output is the emotion analysis result. The server uses this feedback to regenerate optimized content.

[1842] Specific behavior:

[1843] Collect facial and voice data

[1844] Data analysis using emotion engine

[1845] Regenerating content using feedback results

[1846] Step 5:

[1847] The server embeds the generated content into an HTML / CSS template to create a digital bulletin board. This digital bulletin board is scheduled for publication based on a specified timing. The input is the generated content and HTML / CSS template, and the output is the digital bulletin board.

[1848] Specific behavior:

[1849] Embedding content into templates

[1850] Publication schedule settings: "cron job settings: 0 9 1 php publish_kawaraban.php"

[1851] Step 6:

[1852] As the publication date approaches, the server retrieves the target customer list from the database. It embeds the digital bulletin in an email template and sends it out all at once using the email server and push notification server. The input is the database connection information and email template, and the output is the target customer list and the email to be sent.

[1853] Specific behavior:

[1854] SQL query: "SELECT email FROM customers WHERE subscription_status='active';"

[1855] Embed content in email template: Embed the contents of the bulletin board in "mail_body.html"

[1856] Add to email delivery queue: "INSERT INTO email_queue (recipient, content) VALUES ('user@example.com', 'mail_body.html');"

[1857] Step 7:

[1858] Users receive an email or push notification from the app, open the digital bulletin board, and use the "share" button provided within the bulletin board to spread information on social media, etc. The input is the click of the share button and the user's selected social media, and the output is the shared link.

[1859] Specific behavior:

[1860] Generate a shared link: "https: / / example.com / kawaraban?id=123&track_id=ABC456"

[1861] Record share link clicks: "INSERT INTO share_logs (user_id, share_time, platform) VALUES (123, '2023-10-01 09:10:00', 'Instagram');"

[1862] Step 8:

[1863] The server analyzes the engagement of the disseminated information and creates a new potential customer list. It uses an emotion engine to analyze the user's emotion data and prepare targeted advertisements and personalized offers. The input is the shared log and emotion data, and the output is a potential customer list and advertising offers.

[1864] Specific behavior:

[1865] Engagement data analysis: SELECT COUNT() FROM share_logs WHERE platform='Instagram';

[1866] Generate a list of potential customers: "INSERT INTO potential_customers (user_id, interest, engagement_score) VALUES (234, 'electronics', 87);"

[1867] Step 9:

[1868] The server uses the emotion engine to collect emotional data and then uses the generative AI model to make personalized product recommendations. The input is emotional data and purchase history data, and the output is personalized product recommendations.

[1869] Specific behavior:

[1870] Customized Proposal Generation: "Based on the emotional data of user ID 123, the generative AI generates personalized product suggestions."

[1871] Record offer: "INSERT INTO personalized_offers (user_id, product_id, offer_text) VALUES (123, 456, 'This month's featured product: latest smartphone');"

[1872] (Application example 2)

[1873] 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."

[1874] Traditional online shopping sites have limited means to effectively connect users and suppliers, making it difficult to acquire new customers and improve customer satisfaction. Furthermore, personalized marketing strategies are difficult due to a lack of product recommendations that reflect user sentiment and real-time feedback.

[1875] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for periodically issuing digital information via the Internet, means for connecting users and suppliers, and means for disseminating information by utilizing existing users. This makes it possible to discover new products and develop new customers using generative AI and an emotion engine. Furthermore, by including means for analyzing user emotion data using smart devices and making personalized product recommendations, and means for periodically delivering digital information to customers, it is possible to improve user satisfaction and realize effective marketing measures.

[1876] The "Internet" is a system for exchanging information through computer networks and a technology that forms a widespread communications network.

[1877] "Periodic" means something that is repeated at regular intervals.

[1878] "Digital information" means information in a form that is stored or transmitted electronically.

[1879] "User" refers to an individual or organization that uses the system or service.

[1880] "Supplier" means an individual or entity that provides goods or services.

[1881] "Generative artificial intelligence" refers to a system that uses machine learning and deep learning techniques to derive new results from data.

[1882] A "smart device" is a device that can connect to the Internet and is an electronic device with advanced functionality.

[1883] "Emotional data" refers to information about the user's emotional state, and is obtained by analyzing facial expressions and voice.

[1884] "Personalization" means customizing to suit the characteristics and preferences of individual users.

[1885] "Product recommendation" is the act of presenting appropriate products or services to users.

[1886] "Distribution" refers to the act of sending information or data to a specific recipient.

[1887] The present invention is a system for effectively connecting users and suppliers, periodically issuing digital information and utilizing generative AI and emotion engines to discover new products and develop new customers. The following describes in detail the embodiments of the present invention.

[1888] First, the server collects past purchase history data and current market trend information. This data is input into a generative AI and emotion engine, which generates new product information and feature articles based on the purchase data, trend information, and user emotional feedback. The generated content is embedded into a design template using HTML / CSS, and the resulting digital bulletin board is created. The system is configured to publish this digital bulletin board at a specified time (for example, the beginning of the month or a specific day of the week).

[1889] Next, as the regular publication date approaches, the server automatically begins preparations for publication. The server retrieves the target customer list from the database, embeds the digital bulletin in an email template, and prepares for mass distribution. This includes procedures for efficiently distributing the digital bulletin to customers using an email server and push notification server.

[1890] The system also includes a means for analyzing the user's emotional data using smart devices. This analysis of emotional data utilizes the camera and microphone on the user's smartphone. This allows emotions to be captured from the user's facial expressions and voice, and then analyzed by the emotion engine. For example, if the user smiles at the camera, the system detects the emotion "fun" and recommends relaxation-related products based on that. If the user's facial expression is tired, the system suggests energy drinks and fitness-related products.

[1891] By combining the analyzed emotional data with purchase history data, personalized product recommendations are made. When a user shows interest in a product, emotional feedback is provided on the spot, and the system can improve the accuracy of product recommendations in real time based on this feedback.

[1892] As a concrete example,

[1893] When a user smiles into the camera, the system detects the emotion "happy" and recommends relaxation-related products based on that emotion.

[1894] On the other hand, if the person looks tired, we will suggest energy drinks or fitness-related products.

[1895] Also, an example of a prompt for the generation AI is:

[1896] Prompt: When the user smiles, suggest three relaxation-related products.

[1897] There is.

[1898] In this way, the server periodically publishes digital information via the Internet, providing a means to connect users and suppliers, and it is also possible to discover new products and develop new customers using generative artificial intelligence and emotion engines.

[1899] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1900] Step 1:

[1901] The server collects historical purchase data and current market trend information. This information is extracted from a database. The input is user purchase data, and the output is a dataset for analysis. Specifically, the server uses SQL queries to retrieve the required information from the database.

[1902] Step 2:

[1903] The server inputs the collected data into the generative AI model to generate new product information and feature articles. The input is the collected purchase history data and market trend information, and the output is the generated content. Specifically, the server prompts the generative AI model to generate text and images.

[1904] Step 3:

[1905] The generated content is embedded in an HTML / CSS template and configured as a digital tile print. The input is the generated content, and the output is the digital tile print. Specifically, the server uses a template engine to format the content into a web page.

[1906] Step 4:

[1907] As the regular publication date approaches, the server retrieves the list of customers to be distributed from the database and embeds the digital bulletin in the email template. The input is the customer list and the digital bulletin, and the output is an email ready to be distributed. Specifically, the server generates an email for each customer on the list.

[1908] Step 5:

[1909] The server uses a mail server and a push notification server to efficiently deliver the digital bulletin to customers. The input is the prepared email, and the output is the digital bulletin that arrives in the customer's inbox. Specifically, the server sends the email using the SMTP protocol.

[1910] Step 6:

[1911] A user's smart device (e.g., a smartphone) uses a camera and microphone to capture the user's facial expressions and voice and collect emotional data. The input is the user's facial expressions and voice, and the output is emotional data. Specifically, the device collects data using OpenCV and a voice recognition library.

[1912] Step 7:

[1913] The device analyzes the collected emotion data with an emotion engine to determine the user's current emotion. The input is the captured emotion data, and the output is the analyzed emotional state. Specifically, the device uses a deep learning model to classify the emotion.

[1914] Step 8:

[1915] The server then makes personalized product recommendations based on the analyzed emotional data. The input is the analyzed emotional state and purchase history data, and the output is a list of recommended products. Specifically, the server selects products using a recommendation algorithm.

[1916] Step 9:

[1917] If the user is interested in a product, they will receive emotional feedback on the spot, which will improve the system's recommendation accuracy. The input is the user's additional feedback, and the output is an improved recommendation model. Specifically, the server updates the model in real time.

[1918] Step 10:

[1919] The server analyzes the information from the spread digital bulletin board and analyzes the behavioral history and emotional data of new users. The input is the spread information and the behavioral history of new users, and the output is a list of potential customers. Specifically, the server uses big data analysis tools.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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).

[1927] 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.

[1928] 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."

[1929] 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.

[1930] 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).

[1931] 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.

[1932] 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.

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] The following is further disclosed regarding the above embodiment.

[1942] (Claim 1)

[1943] means for periodically publishing digital information via the Internet;

[1944] a means for connecting users and suppliers;

[1945] A means of spreading information by utilizing existing users,

[1946] A means of discovering new products using generative artificial intelligence;

[1947] A means for acquiring new customers using the generative artificial intelligence;

[1948] A system including:

[1949] (Claim 2)

[1950] 10. The system of claim 1, further comprising means for a user to share the received digital information on social media.

[1951] (Claim 3)

[1952] 2. The system according to claim 1, further comprising means for analyzing the spread information and analyzing the behavioral history of new users.

[1953] "Example 1"

[1954] (Claim 1)

[1955] A means for automatically collecting purchase history data and market trend information and inputting the same into the generating artificial intelligence;

[1956] A means for generating product information and feature articles based on purchase data and trend information using generative artificial intelligence;

[1957] a means for creating digital information by embedding the generated content into a design template;

[1958] A means for automatically setting the publication date of the created digital information using a scheduling function and preparing for publication;

[1959] means for simultaneously distributing the digital information via a mail server or a push notification server;

[1960] means for the user to share the received digital information on social media;

[1961] A means of collecting and analyzing information spread on social media to create new customer lists,

[1962] a means for implementing marketing measures based on the new customer list;

[1963] A system including:

[1964] (Claim 2)

[1965] The system of claim 1, further comprising means for the user to share the received digital information on social media.

[1966] (Claim 3)

[1967] 2. The system according to claim 1, further comprising means for analyzing the spread information and analyzing the behavioral history of new users.

[1968] "Application Example 1"

[1969] (Claim 1)

[1970] means for periodically publishing digital information via the Internet;

[1971] a means for connecting users and suppliers;

[1972] A means of spreading information by utilizing existing users,

[1973] A means of discovering new products using generative artificial intelligence;

[1974] A means for acquiring new customers using the generative artificial intelligence;

[1975] A means of publishing digital bulletins on specific days of the week,

[1976] A way to embed the generated content into email templates and prepare for mass distribution,

[1977] A system including:

[1978] (Claim 2)

[1979] ...

Claims

1. means for periodically publishing digital information via the Internet; a means for connecting users and suppliers; A means of spreading information by utilizing existing users, A means of discovering new products using generative artificial intelligence; A means for acquiring new customers using the generative artificial intelligence; A system including:

2. 10. The system of claim 1, further comprising means for a user to share the received digital information on social media.

3. The system according to claim 1, further comprising means for analyzing the spread information and analyzing the behavioral history of new users.

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