system

Abstract

This invention provides an information processing device that collects keyword data from multiple web search engines, analyzes it, and identifies consumer interests. [Solution] A data processing device for acquiring data, comprising: means for collecting keyword data from multiple search engines on the web; means for analyzing the collected keyword data and identifying search frequency and trends over a specific period; means having a generating artificial intelligence that generates project proposals and creates catchphrases based on the analysis results; means for notifying the user's terminal of the generated project proposals and catchphrases via a network; means for providing an interface for registering relevant product information on an online sales platform based on the notification content; and means for collecting sales data of registered products and providing feedback for improving sales.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an online shopping platform, it is difficult to quickly and effectively reflect consumers' interests in a special feature plan, which may result in missing sales opportunities. Also, there is a problem that there is a lack of means for identifying trends and efficiently promoting products by utilizing keyword data from various search engines. Furthermore, there are also technical problems of quickly analyzing sales data and providing feedback for improving sales.

Means for Solving the Problems

[0005] This invention solves the above problem by providing an information processing device equipped with the function of collecting keyword data from multiple search engines on the web, analyzing it, and identifying consumer interests. Furthermore, it has built a system that generates project proposals and catchphrases based on the analysis results using a generation AI and notifies the user's terminal. In addition, it provides an interface for registering relevant product information on an online sales platform and provides feedback for improving sales by collecting and analyzing sales data of registered products in real time.

[0006] An "information processing device" is an electronic device used for a series of information processing tasks, such as data collection, analysis, generation, notification, and management.

[0007] A "search engine" is a software system that searches for information on the internet based on keywords and provides the results.

[0008] "Keyword data" refers to information about specific words and phrases obtained based on users' search behavior.

[0009] "Analysis" is the process of processing acquired data to extract useful information and conducting analysis in accordance with a specific purpose.

[0010] A "project proposal" is a plan or idea formulated based on a specific theme with the aim of promoting the sale of a product.

[0011] "Generative artificial intelligence" is an artificial intelligence technology that learns from large amounts of data and uses the knowledge gained to create new, creative content.

[0012] A "catchphrase" is a short advertising text or phrase used to clearly convey the features of a product or service.

[0013] A "terminal" is an electronic device such as a computer or smartphone that is connected to a network and operated by a user.

[0014] An "online sales platform" is a website or application that allows the buying and selling of goods and services via the internet.

[0015] "Interface" is a technical term that refers to the means or connection point by which different systems or devices exchange information with each other.

[0016] "Sales data" refers to information about the sale of goods and services, including sales figures, sales volume, and purchase history.

[0017] "Real-time" is a term that describes a state in which processing is performed immediately and results are provided with virtually no delay.

[0018] "Feedback" refers to evaluation and criticism information provided to a system or user, intended to be used for future improvements. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a system that obtains insights into consumer behavior on online shopping platforms through the collection and analysis of keyword data obtained from multiple search engines. This system is centered around an information processing device (server) and can be implemented in the following forms.

[0041] First, the server periodically collects trend and frequently occurring keyword data via the internet using APIs or scraping from various search engines. Then, after preprocessing the collected data, such as removing noise and standardizing it, the server analyzes it using natural language processing to identify topics of consumer interest. This allows for accurate understanding of changing trends over time.

[0042] Based on the generated data, the AI ​​on the server automatically creates effective sales promotion plans and slogans, referencing past sales performance and success stories. This enables the rapid development of marketing strategies. This information is immediately notified to the user's terminal via the network. Through the terminal's interface, the user can review the notified plan and modify or approve it as needed.

[0043] Next, the user selects products that match the decided project plan and lists those products on the online sales platform via the terminal. The terminal is equipped with an appropriate input interface to simplify product information entry and allow for the rapid creation of special feature pages.

[0044] Subsequently, the server monitors sales data for the listed products in real time and analyzes sales performance. Based on the data obtained, the server generates feedback to improve sales and notifies the terminal of this information. This feedback loop allows users to improve their product sales strategies and optimize sales.

[0045] For example, if the server analyzes the trending keyword "summer festival" and generates a project proposal such as "Cool Yukata Special," users will then list related yukata and summer festival goods on an online platform based on that proposal. The server tracks the sales data of these products and notifies users as needed about the effectiveness of the campaign and the need for additional promotion.

[0046] Thus, the system of the present invention provides an effective means of quickly reflecting consumer interests in product strategies and increasing sales.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server uses search engine APIs via the internet to collect keyword data. The data collected includes terms that were frequently searched or trending within a specific period.

[0050] Step 2:

[0051] The server preprocesses the collected keyword data, including removing duplicates and noise, and normalizing the keywords. This ensures the reliability and integrity of the data.

[0052] Step 3:

[0053] The server applies natural language processing algorithms to identify consumer interests using pre-processed data. This allows it to identify trending themes that are relevant to the time period and region.

[0054] Step 4:

[0055] The AI ​​on the server generates project proposals and taglines based on analyzed trends. Based on past sales performance, it predicts anticipated consumer reactions and creates optimized content.

[0056] Step 5:

[0057] The server notifies the user's terminal of the generated project proposal and tagline. The terminal receives this information and provides an interface for displaying it to the user.

[0058] Step 6:

[0059] The user reviews the notification on their device and makes corrections as needed. They then send the corrected information to the server as feedback.

[0060] Step 7:

[0061] Users select products based on information received from the server and list them on the online sales platform. An interface for efficiently registering product information is provided on the terminal.

[0062] Step 8:

[0063] The server collects and constantly monitors sales data for listed products in real time. It analyzes sales volume, sales amount, and consumer purchasing trends.

[0064] Step 9:

[0065] The server analyzes sales trends based on the collected sales data, generates feedback for the user regarding the success rate of promotions and areas for improvement, and notifies the user's device.

[0066] Step 10:

[0067] Users can review feedback via their devices and use it to inform future planning and sales strategies, thereby enabling continuous improvement.

[0068] Through these steps, the system enables efficient product sales tailored to consumer interests.

[0069] (Example 1)

[0070] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] In the online marketplace, quickly understanding consumer interests and developing effective product strategies is essential for increasing sales. However, existing systems have limited data acquisition and analysis capabilities, making it difficult to conduct effective marketing activities in a short period. In particular, there has been a lack of systems that can capture rapidly changing consumer trends and utilize them for sales promotion. This creates a challenge in that it increases the risk of missing potential sales opportunities.

[0072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0073] In this invention, the server includes means for communicating via a network and acquiring string data from multiple information provision platforms on the web; means for normalizing the acquired string data, removing unnecessary elements, and analyzing it to identify data trends over a certain period of time; and means equipped with artificial intelligence that automatically generates project proposals and attractive expressions related to a specific theme based on the analysis. This makes it possible to quickly reflect consumer interests in product strategies, optimize marketing effectiveness, and increase sales.

[0074] An "information processing device" is an electronic device used to acquire, analyze, and process data, and can access external databases and systems via a network.

[0075] "String data" refers to a set of characters and numbers used to represent specific information, and is usually represented in text format.

[0076] "Normalization" is a process that unifies the format and structure of data, and is used to improve efficiency when performing data analysis.

[0077] "Unnecessary elements" are data or information that hinder the extraction of useful information during analysis or processing, and are therefore subject to filtering.

[0078] "Analysis" is the process of examining collected data from multiple perspectives and extracting useful information and insights from it.

[0079] "Artificial intelligence" is a form of computer system that has the function of automatically learning and making decisions based on data, and plays a role in streamlining generation and analysis.

[0080] A "project proposal" is a plan or proposal that outlines a concept for achieving a specific goal, and it is an important element in marketing and strategy planning.

[0081] "Expression" refers to a concrete form, such as text or images, used to convey a specific intention or meaning, and is used in communication.

[0082] A "communication network" is a collection of electrical or optical lines and related equipment for sending and receiving information, and is a means of transmitting data quickly and efficiently.

[0083] An "operation screen" is the interface used by users when operating a system or device, and serves as a point of contact for inputting instructions and confirming information.

[0084] An "e-commerce platform" is a foundational system for buying and selling goods and services online, serving as a platform that connects consumers and sellers.

[0085] "Sales information" refers to data related to the transaction of goods or services, including sales figures, sales quantities, and buyer demographics.

[0086] A "guideline" is something that indicates the direction or policy for achieving a specific goal, and serves as a standard for actions and decision-making.

[0087] This invention provides an information processing system for gaining insights into consumer behavior in online markets. This system is primarily server-based. The server communicates via a network and retrieves string data from multiple information provision platforms on the web. Specifically, it can periodically collect keyword data using APIs from search engines such as Google® and Bing.

[0088] The server normalizes the collected string data, removes unnecessary elements, and then analyzes it. This utilizes data preprocessing techniques using programming languages ​​such as Python and R. The preprocessed data is then analyzed by an automated artificial intelligence (AI) and used as evidence to explore market trends and consumer interests. A commonly used generative AI model (e.g., GPT-3®) can be employed as the AI ​​model.

[0089] The AI-generated project proposals and taglines are related to a specific theme and are sent to the device in an appealing format. The device user reviews these and makes adjustments or approvals as needed. This information supports product registration on e-commerce platforms, allowing for quick listing through a simplified interface.

[0090] For example, a server might acquire the trending keyword "summer festival," and an automatically generated artificial intelligence might suggest a "Cool Yukata Special." Based on this plan, users would then list related products, such as yukata or summer festival-related goods, on an e-commerce platform. By developing such strategies, companies can conduct timely marketing activities.

[0091] An example of the prompt text mentioned above would be: "Generate a catchy slogan and promotional ideas for the online store based on this month's trending keywords. Keywords: summer festival, yukata, merchandise." Based on this prompt text, the AI ​​model automatically generates effective expressions to appeal to consumers. In this way, the system quickly reflects consumer interest in product strategies and provides an effective means to improve sales.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The server communicates with APIs from multiple information provision platforms and periodically retrieves keyword data. This input consists of search queries and trend data, and the data is returned in JSON format. Specifically, it sends requests to search engine APIs and saves the returned responses to a database. The output is a collection of raw data.

[0095] Step 2:

[0096] The server normalizes the collected raw data. The input is the raw data obtained in step 1, and the normalization process removes special characters and unnecessary elements. Specifically, it uses regular expressions to clean the data and format it into the required format. The output is normalized, clean data.

[0097] Step 3:

[0098] The server analyzes the normalized data. This input is the clean data obtained in step 2, and natural language processing techniques are used to analyze trends and consumer interests over a specific period. Specifically, this includes identifying themes using topic modeling and aggregating keyword frequencies. The output is an analysis that provides insights into consumer behavior.

[0099] Step 4:

[0100] The server automatically generates project proposals and taglines using a generative AI model. The input is the analysis results obtained in step 3, and the server creates prompt sentences based on these results. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves strategic expressions suggested by the model. The output is the generated project proposal and tagline.

[0101] Step 5:

[0102] The server notifies the terminal of the generated project proposal and catchphrase via the network. This input is the content generated in step 4 and is converted to a format suitable for the user's terminal. Specifically, it sends push notifications via email or a dedicated application to quickly deliver information to the user. The output is the notification that the user receives.

[0103] Step 6:

[0104] The user reviews the project proposal notified via their device and registers the relevant product information on the e-commerce platform. This input is the project proposal received in step 5, and the user enters the product information using the provided operation screen. Specific actions include manual product registration and information entry using templates. The output is the product data registered on the platform.

[0105] Step 7:

[0106] The server monitors sales information for registered products in real time. This input consists of product data and sales information registered in step 6, tracking sales volume and consumer response. Specifically, it retrieves data from the sales platform via an API and visualizes it on an analytics dashboard. The output is the sales analysis results.

[0107] Step 8:

[0108] The server generates feedback for sales improvement based on sales analysis results and notifies the user. The input is the analysis data obtained in step 7, and it creates guidelines for the next sales strategy. Specific actions include suggesting action items using a recommendation system. The output is a feedback notification and improvement suggestions provided to the user.

[0109] (Application Example 1)

[0110] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0111] In online sales, it is difficult to quickly grasp consumer interests and implement effective marketing strategies in a timely manner. Therefore, there is a need for a system that can develop appropriate sales strategies tailored to customer interests and optimize sales.

[0112] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0113] In this invention, the server includes means for collecting keyword information from multiple search mechanisms on a network, means for analyzing the collected keyword information and identifying trends during a specified period, and means for generating a plan related to a theme using generative AI and creating a catchy slogan. This makes it possible to quickly and effectively formulate and execute dynamic sales strategies based on consumer interests.

[0114] A "network" is a system in which multiple computers and devices are connected to exchange information and communications.

[0115] A "search engine" is a system used to search for information on the internet, and it finds relevant information based on keywords entered by the user.

[0116] "Keyword information" refers to the main terms that consumers use when searching the internet, and it is information that indicates trends and consumer interests.

[0117] "Generative AI" is a type of artificial intelligence technology that automatically generates new information and ideas based on learned data.

[0118] A "plan" is a set of detailed guidelines or policies formulated to achieve a specific objective.

[0119] A "catchphrase" is a short, memorable phrase used to attract consumers' attention in order to promote a product or service.

[0120] A "sales platform" is an online or offline platform for providing goods or services.

[0121] An "interface" is a connection or procedure that enables the exchange of information between a user and a system.

[0122] "Sales figures" refer to data that shows the sales volume or revenue of a product over a specific period.

[0123] "Opinions" are improvement measures or suggestions presented based on analysis.

[0124] "Trends" refer to changes or trends in fashion under specific periods or conditions.

[0125] To realize the system of this invention, the server acquires information via a network and collects keyword information from a search mechanism. The server uses this information to analyze the data using natural language processing technology, specifically TENSORFLOW®, and identifies trends. Based on the results of this analysis, the server uses generative AI to formulate a plan related to the theme and create a catchy slogan.

[0126] The server transmits the generated plan and catchphrase to the user's terminal via the network. Based on the received content, the terminal provides an interface for easily registering relevant product information on the online sales platform. This interface was developed using Flutter® and is designed to be intuitive and easy for the user to operate.

[0127] Furthermore, the server collects sales figures for registered products in real time and performs immediate analysis. The analysis results are provided to users as feedback for improving their sales strategies. This allows users to constantly develop and implement sales strategies optimized for consumer needs.

[0128] As a concrete example, consider a scenario where a server detects the trend "Halloween" and creates related plan proposals. In this case, the generation AI suggests a "Special Feature on Original Halloween Goods" and generates a related tagline: "Check out the items that will make your costume shine now!" This makes it easier for users to register Halloween-related products on the platform and allows for the rapid creation of a special feature page.

[0129] An example of a prompt for the generation AI model would be: "Analyze the latest keyword data and generate an attractive promotional tagline based on consumer insights into summer fashion."

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The server collects keyword information from multiple search mechanisms on the network. It uses authentication information for accessing network APIs as input. The output is a list of collected keywords. This process involves sending API requests and analyzing the retrieved responses to extract the target keywords.

[0133] Step 2:

[0134] The server analyzes the collected keyword information to identify search trends over a specific period. The input is the keyword list obtained in step 1. The output generates trend data as a result of the analysis. Using natural language processing techniques, it analyzes the frequency and time-series changes of keywords and performs specific actions to extract topics of consumer interest.

[0135] Step 3:

[0136] The server uses a generative AI to create a plan proposal and catchphrase based on the theme. The input is the trend data obtained in step 2. The output is the generated plan proposal and catchphrase. Prompt text is input to the generative AI, which then automatically generates an appropriate promotional message.

[0137] Step 4:

[0138] The server sends the generated plan and catchphrase to the terminal via the network. The input is the plan and catchphrase generated in step 3. The output is what is displayed on the user's terminal. This includes receiving the transmitted data on the terminal and allowing the user to confirm it.

[0139] Step 5:

[0140] The terminal provides an interface to assist in registering product information to the online sales platform based on the plan received from the server. The inputs are the plan and catchphrase received in step 4. The output is a registration support interface. Using Flutter, it provides forms and input fields for user-friendly UI operation.

[0141] Step 6:

[0142] The server collects sales figures for registered products in real time and generates suggestions for improving sales. The input is the latest sales data obtained from the sales platform. The output is feedback information based on the analysis results. It monitors sales data and uses analysis to develop strategies for improving sales.

[0143] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0144] This invention integrates an emotion engine that understands user emotions into a project proposal generation system based on the analysis of search keyword data, in order to enhance the effectiveness of product sales on online sales platforms. Specific embodiments of this system are described below.

[0145] First, the server collects keyword data from multiple search engines. This can be done using API-based data acquisition or web scraping techniques. The collected data is preprocessed on the server and then provided for analysis as a cleaned dataset.

[0146] In the analysis process, the server uses natural language processing technology to analyze the data and identify trending keywords and frequently searched terms relevant to the time period. This allows for the development of featured themes for products and services.

[0147] Next, the AI ​​on the server generates a marketing campaign plan and tagline based on past data and the results of the current analysis. During this process, the emotion engine analyzes the user's past feedback and behavioral data, customizing the content to take the user's emotional state into account. This enables more personalized suggestions.

[0148] The generated project proposals and taglines are sent to the user's device via the network. The device receives the notification and provides an interface that allows the user to review the project proposals and taglines. Based on this information, the user can register products or prepare special feature pages.

[0149] The product information selected by the user is registered from the terminal to the online sales platform. Subsequently, the server collects and monitors sales data for the listed products in real time. In addition, the emotion engine collects emotion data through user interactions and customer reviews, and generates feedback based on consumer emotions.

[0150] For example, if the server analyzes a trending theme like "Spring Picnic," it can suggest a relaxation-themed feature that takes into account the user's past purchase history and feedback. The emotion engine detects positive emotional responses to the "Picnic" theme and creates a promotional message that reflects them.

[0151] In this way, this system comprehensively utilizes search data and sentiment data to support the planning and implementation of more effective product sales strategies.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The server uses search engine APIs and web scraping techniques to collect vast amounts of keyword data, including trending keywords and frequently occurring words. This data reflects the latest consumer interests.

[0155] Step 2:

[0156] The server cleans up the collected keyword data, removing unnecessary information. It also standardizes the data and prepares it for analysis. This improves the consistency and accuracy of the data.

[0157] Step 3:

[0158] The server applies natural language processing techniques to analyze keyword data. This analysis identifies trends and important themes that have attracted attention during a specific period.

[0159] Step 4:

[0160] The server's AI generates advertising and sales promotion plans based on the analysis results. It also references data from past success stories to formulate the most optimal content.

[0161] Step 5:

[0162] The emotion engine analyzes the user's past behavioral data and feedback to determine their emotional state. This information is used to personalize the generated project proposals and taglines.

[0163] Step 6:

[0164] The server sends the generated project proposal and tagline to the user's terminal via the network. The terminal displays an interface that allows the user to review the project content and make revisions as needed.

[0165] Step 7:

[0166] Users review the notified project proposal on their devices and revise it as needed. The revised project proposal is then sent back to the server as feedback.

[0167] Step 8:

[0168] Based on the project proposal approved or modified by the user, relevant product information is registered on the online sales platform via the terminal. The terminal interface provides support to simplify the registration process.

[0169] Step 9:

[0170] The server monitors sales data for listed products in real time. It also uses an emotion engine to collect customer reviews and feedback and analyze consumers' emotional states.

[0171] Step 10:

[0172] The server analyzes sales data and sentiment data to generate more effective feedback and improvement suggestions. This information is then communicated to the user's device and used to inform future sales strategies.

[0173] This entire process enables highly personalized product sales that incorporate consumer emotions and trends.

[0174] (Example 2)

[0175] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0176] In modern e-commerce, there is a demand for the rapid and accurate provision of product information that matches consumer needs. However, conventional systems build marketing campaigns using only the analysis of search keyword data, and do not fully utilize consumer sentiment or past behavior history. As a result, personalized marketing becomes difficult, and it is difficult to increase sales.

[0177] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting information through multiple data acquisition means, means for analyzing the collected information and identifying trends over a specific period, and means having artificial intelligence that generates a plan and creates a representation on a theme using the analysis results and sentiment analysis. This makes it possible to plan personalized marketing campaigns that take into account consumer emotions and behavior, thereby increasing sales.

[0179] "Data acquisition means" refers to a device or method used to collect information required by an information processing device, and has the function of efficiently acquiring information from various data sources.

[0180] "Analysis means" refers to an apparatus or method used to analyze collected information and identify specific trends or patterns.

[0181] "Artificial intelligence" refers to a computer system that can perform specific tasks through learning and reasoning, and that can make creative or logical decisions based on the data obtained in the process.

[0182] "Expression" refers to text or visual content generated based on analyzed information, intended to effectively convey a message in line with a specific theme or purpose.

[0183] A "communication network" is a network infrastructure used for sending and receiving information, providing the necessary connections for exchanging data between different terminals.

[0184] A "display device" is a device that allows a user to visually confirm information, and typically includes screens and monitors.

[0185] An "e-commerce platform" is an online marketplace where goods and services are bought and sold, serving as a link between sellers and buyers.

[0186] "Transaction data" refers to information related to purchase history and sales statistics recorded in e-commerce, and plays an important role in marketing and inventory management.

[0187] A "response" is a message or report that indicates conclusions or recommended actions derived from the data received by the system, and is provided to support the user's decision-making.

[0188] This invention is a system for maximizing the effectiveness of marketing activities on e-commerce platforms. Specific embodiments thereof are described below.

[0189] First, the server collects information from multiple data sources as a means of obtaining information. This process includes methods such as using search engine APIs to obtain data and utilizing web scraping techniques. The acquired information is organized on the server and then analyzed.

[0190] Next, the server uses natural language processing techniques to analyze the acquired data. This analysis can utilize libraries such as Python's NLTK or spaCy. This allows for the extraction of trends and patterns over a specific period, which can then be incorporated into marketing activity plans.

[0191] The AI ​​model on the server generates a plan related to the theme based on the analysis results, and simultaneously creates a representation. This process incorporates the analysis results of the user's past transaction data and sentiment data to help provide personalized marketing strategies.

[0192] The generated results are notified to the user's terminal via the communication network. The terminal, acting as a display device, presents this information to the user, allowing the user to evaluate the plan and its representation through an appropriate interface.

[0193] Users can register product information on the e-commerce platform based on the information they receive. The server then collects and analyzes transaction data for the registered products in real time. Based on the insights gained from this transaction data, it generates further responses, contributing to increased sales.

[0194] For example, if the server analyzes the theme "Spring Picnic," the generative AI model can propose a feature that emphasizes relaxation based on past consumer behavior and emotional data. It can also use emotional data to create expressions that highlight the positive image of a "picnic."

[0195] An example of a prompt is: "Analyze trending keywords related to spring picnics and generate a plan that takes user sentiment into account."

[0196] Thus, the system of the present invention aims to enhance competitiveness in e-commerce by comprehensively analyzing diverse information and providing personalized marketing strategies based on consumer emotions and behavior.

[0197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0198] Step 1:

[0199] The server first uses information acquisition methods to collect necessary data via search engine APIs and web scraping techniques. The input consists of a list of target keywords and related URLs, and the server extracts keyword data and related information from the web based on this. The output is a cleaned dataset, with duplicates and noise removed, which is then passed on to the next analysis step.

[0200] Step 2:

[0201] The server applies natural language processing techniques to the obtained dataset to perform trend analysis. At this stage, Python's NLTK and spaCy are used to tokenize the data, tag parts of speech, and analyze sentence structure. The input is the dataset generated in the previous step, and the output is a list of trending keywords and frequently searched terms for each period. This allows for the extraction of specific trends and themes.

[0202] Step 3:

[0203] The AI ​​model generation step on the server is where a marketing plan is generated based on the analysis results. The inputs here are a list of trending keywords and historical consumer data. The AI ​​model combines these to generate a plan and tagline tailored to the target market. The output is a personalized marketing strategy that takes user emotions into consideration.

[0204] Step 4:

[0205] The generated plan and tagline are transmitted from the server to the user's terminal via the communication network. The terminal receives this data and presents the information through an interface that the user can view. The input is data from the server, and the output is visualized information displayed through the user-operated interface.

[0206] Step 5:

[0207] The user registers product information on the e-commerce platform based on information received via their device. The user's input process enters the product name, description, price, etc., into the platform, completing the registration. The output is a publicly available product page.

[0208] Step 6:

[0209] The server instantly collects and analyzes transaction data for registered products and generates responses to improve sales. This process involves evaluating transaction statistics and performing sentiment analysis of customer reviews using a sentiment engine. Inputs are real-time sales data and customer feedback, and outputs are feedback reports that include improvement suggestions and revisions to marketing strategies.

[0210] (Application Example 2)

[0211] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0212] Online sales platforms require sophisticated product sales strategies that respond to the diverse needs and emotional responses of consumers. While traditional sales systems can grasp broad trends through search keyword analysis, they have struggled to implement targeted marketing based on the emotions of individual consumers. Therefore, a method is needed to develop more effective and personalized sales strategies.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server is an information processing device for acquiring data, and includes means for collecting keyword information from multiple data collection systems on the web, means having a generative artificial intelligence that generates project proposals related to a theme and creates promotional messages based on the analysis results, and means having an emotion analysis mechanism for analyzing user emotion data and generating personalized advertising content. This enables the efficient generation of advertising content that is tailored to the individual needs and emotions of consumers.

[0215] An "information processing device" is used to collect keyword information from multiple data collection systems on the web.

[0216] "Keyword information" refers to information that indicates a specific theme or trend, obtained through users' search behavior.

[0217] "Generative artificial intelligence" is an artificial intelligence that has the function of generating project proposals related to a theme and creating promotional messages based on analysis results.

[0218] A "communication network" is a network used to notify users of generated project proposals and promotional messages to their electronic devices.

[0219] An "interface" is a system that provides a point of contact with the user to register relevant product information in the online sales system based on the content of the notification.

[0220] "Sales data" refers to data that includes sales information for registered products and is used to provide feedback for improving sales.

[0221] A "sentiment analysis mechanism" is a system that analyzes users' emotional data and has the function of generating personalized advertising content.

[0222] The system for implementing this invention is centered around a server that functions as an information processing device. The server acquires keyword information from multiple data collection systems on the internet for data collection. Web scraping libraries such as the Google Keyword Planner API and Beautiful Soup are used for this purpose. The acquired keyword information is analyzed using natural language processing techniques to identify search frequency and trends within a specific time frame. Libraries such as Spacy and NLTK are used for this analysis.

[0223] Based on the analysis results, a generative artificial intelligence generates project proposals and promotional messages related to the theme. Here, a generative AI model such as OpenAI's GPT is used, combining collected data with past sales performance data to make optimal suggestions. During this process, user emotional data is collected through the user's electronic devices, and this data is analyzed by an emotional analysis mechanism. VADER and TextBlob are used specifically for emotional analysis.

[0224] A distinctive feature of this system is the advertising content that users are notified of. These notifications are sent via a communication network, and the user's device receives them, providing an interface for registering relevant product information in the online sales system. Through this process, advertising content optimized for the individual needs and emotions of consumers is generated.

[0225] For example, if the server analyzes trends related to "spring picnics," it will consider the user's past purchase history and sentiment data to generate a personalized message such as, "Why not refresh yourself with some new picnic gear?" An example of a prompt to the generating AI model would be, "We have determined that the user is interested in 'spring picnics' and has positive feelings towards them. Please generate advertising copy related to picnics."

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server retrieves keyword information from multiple data collection systems on the internet. It uses the Google Keyword Planner API and Beautiful Soup as input to collect the necessary keyword data. The collected data is then cleaned up and output with noise removed.

[0229] Step 2:

[0230] The server analyzes the collected keyword information using natural language processing techniques. Spacy and NLTK are used to tokenize, normalize, and extract nouns from the data. The input is the cleaned keyword information obtained in step 1, and the output is data that identifies trends and search frequencies within a specific time frame.

[0231] Step 3:

[0232] The server generates a project proposal and promotional message using a generative AI model based on the analysis results. During this process, it uses OpenAI's GPT to create prompt text, resulting in personalized advertising content. The input is the analysis data obtained in step 2, and the output is the generated project proposal and advertising message.

[0233] Step 4:

[0234] The user's device receives notifications from the server via the network and displays the generated project proposal and promotional message. The input is the advertising content generated in step 3, which is output as an interface that the user can view.

[0235] Step 5:

[0236] User emotion data is transmitted to the server via the terminal. This data is input from the user's terminal and analyzed by an emotion analysis mechanism on the server. Specifically, positive or negative emotions are evaluated using VADER and TextBlob, and the results are output.

[0237] Step 6:

[0238] The server optimizes product information for registration in the online sales system based on the user's emotional data and the generated project proposal. The input here is the emotional data from step 5 and the project proposal from step 3, and the output is optimized product registration information that takes these into account.

[0239] Step 7:

[0240] The user's device verifies the optimized product information and registers it in the online sales system as needed. The input is the optimized product information obtained in step 6, and the user's actions result in the product being registered on the online platform as the final output.

[0241] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0242] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0243] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0247] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0248] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0249] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0250] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0251] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0252] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0253] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0254] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0255] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0256] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0257] This invention is a system that obtains insights into consumer behavior on online shopping platforms through the collection and analysis of keyword data obtained from multiple search engines. This system is centered around an information processing device (server) and can be implemented in the following forms.

[0258] First, the server periodically collects trend and frequently occurring keyword data via the internet using APIs or scraping from various search engines. Then, after preprocessing the collected data, such as removing noise and standardizing it, the server analyzes it using natural language processing to identify topics of consumer interest. This allows for accurate understanding of changing trends over time.

[0259] Based on the generated data, the AI ​​on the server automatically creates effective sales promotion plans and slogans, referencing past sales performance and success stories. This enables the rapid development of marketing strategies. This information is immediately notified to the user's terminal via the network. Through the terminal's interface, the user can review the notified plan and modify or approve it as needed.

[0260] Next, the user selects products that match the decided project plan and lists those products on the online sales platform via the terminal. The terminal is equipped with an appropriate input interface to simplify product information entry and allow for the rapid creation of special feature pages.

[0261] Subsequently, the server monitors sales data for the listed products in real time and analyzes sales performance. Based on the data obtained, the server generates feedback to improve sales and notifies the terminal of this information. This feedback loop allows users to improve their product sales strategies and optimize sales.

[0262] For example, if the server analyzes the trending keyword "summer festival" and generates a project proposal such as "Cool Yukata Special," users will then list related yukata and summer festival goods on an online platform based on that proposal. The server tracks the sales data of these products and notifies users as needed about the effectiveness of the campaign and the need for additional promotion.

[0263] Thus, the system of the present invention provides an effective means of quickly reflecting consumer interests in product strategies and increasing sales.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The server uses search engine APIs via the internet to collect keyword data. The data collected includes terms that were frequently searched or trending within a specific period.

[0267] Step 2:

[0268] The server preprocesses the collected keyword data. This includes removing duplicates and noise, and normalizing the keywords. This ensures the reliability and integrity of the data.

[0269] Step 3:

[0270] The server applies natural language processing algorithms to identify consumer interests using pre-processed data. This allows it to identify trending themes that are relevant to the time period and region.

[0271] Step 4:

[0272] The AI ​​on the server generates project proposals and taglines based on analyzed trends. Based on past sales performance, it predicts anticipated consumer reactions and creates optimized content.

[0273] Step 5:

[0274] The server notifies the user's terminal of the generated project proposal and tagline. The terminal receives this information and provides an interface for displaying it to the user.

[0275] Step 6:

[0276] The user checks the notification content on the terminal and makes corrections if necessary. Then, the corrected information is sent to the server as feedback.

[0277] Step 7:

[0278] Based on the information received from the server, the user selects a product and offers it on the online sales platform. An interface for efficiently registering product information is provided on the terminal.

[0279] Step 8:

[0280] The server collects and constantly monitors the sales data of the offered products in real time. It analyzes the number of sales, sales amount, consumers' purchase trends, etc.

[0281] Step 9:

[0282] Based on the collected sales data, the server analyzes the sales trend, generates feedback on the promotion success rate and improvement points for the user, and notifies the terminal.

[0283] Step 10:

[0284] The user checks the feedback via the terminal and makes continuous improvements by applying it to the next planning and sales strategies.

[0285] Through the above steps, the system realizes efficient product sales according to consumers' interests.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In the online marketplace, quickly understanding consumer interests and developing effective product strategies is essential for increasing sales. However, existing systems have limited data acquisition and analysis capabilities, making it difficult to conduct effective marketing activities in a short period. In particular, there has been a lack of systems that can capture rapidly changing consumer trends and utilize them for sales promotion. This creates a challenge in that it increases the risk of missing potential sales opportunities.

[0289] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes means for communicating via a network and acquiring string data from multiple information provision platforms on the web; means for normalizing the acquired string data, removing unnecessary elements, and analyzing it to identify data trends over a certain period of time; and means equipped with artificial intelligence that automatically generates project proposals and attractive expressions related to a specific theme based on the analysis. This makes it possible to quickly reflect consumer interests in product strategies, optimize marketing effectiveness, and increase sales.

[0291] An "information processing device" is an electronic device used to acquire, analyze, and process data, and can access external databases and systems via a network.

[0292] "String data" refers to a set of characters and numbers used to represent specific information, and is usually represented in text format.

[0293] "Normalization" is a process that unifies the format and structure of data, and is used to improve efficiency when performing data analysis.

[0294] "Unnecessary elements" are data or information that hinder the extraction of useful information during analysis or processing, and are therefore subject to filtering.

[0295] "Analysis" is the process of examining collected data from multiple perspectives and extracting useful information and insights from it.

[0296] "Artificial intelligence" is a form of computer system that has the function of automatically learning and making decisions based on data, and plays a role in streamlining generation and analysis.

[0297] A "project proposal" is a plan or proposal that outlines a concept for achieving a specific goal, and it is an important element in marketing and strategy planning.

[0298] "Expression" refers to a concrete form, such as text or images, used to convey a specific intention or meaning, and is used in communication.

[0299] A "communication network" is a collection of electrical or optical lines and related equipment for sending and receiving information, and is a means of transmitting data quickly and efficiently.

[0300] An "operation screen" is the interface used by users when operating a system or device, and serves as a point of contact for inputting instructions and confirming information.

[0301] An "e-commerce platform" is a foundational system for buying and selling goods and services online, serving as a platform that connects consumers and sellers.

[0302] "Sales information" refers to data related to the transaction of goods or services, including sales figures, sales quantities, and buyer demographics.

[0303] A "guideline" is something that indicates the direction or policy for achieving a specific goal, and serves as a standard for actions and decision-making.

[0304] This invention provides an information processing system for obtaining insights into consumer behavior in the online market. This system is mainly centered around a server. The server communicates via a network and acquires string data from multiple information - providing platforms on the web. Specifically, it is possible to regularly collect keyword data by utilizing the APIs of search engines such as Google and Bing.

[0305] The server normalizes the collected string data, removes unnecessary elements, and analyzes it. For this, data pre - processing techniques using programming languages such as Python and R are utilized. The pre - processed data is analyzed by an automatically - generated artificial intelligence (AI) and used as evidence to explore market trends and consumer interests. As an AI model, a generally - used generative AI model (for example, GPT - 3) can be adopted.

[0306] The planning proposals and catchphrases generated by the AI are related to a specific theme and are transmitted to the terminal as attractive expressions. The user of the terminal checks these and makes adjustments or approvals as necessary. This information supports product registration on the e - commerce platform, and by using a simplified operation screen, the listing work can be carried out quickly.

[0307] As a specific example, when the server obtains a trend keyword "summer festival" and the automatically - generated artificial intelligence proposes a "special feature on cool kimonos", based on this planning proposal, the user lists related products, such as kimonos and summer - festival - related goods, on the e - commerce platform. By formulating such strategies, enterprises can conduct timely marketing activities.

[0308] An example of the prompt text mentioned above would be: "Generate a catchy slogan and promotional ideas for the online store based on this month's trending keywords. Keywords: summer festival, yukata, merchandise." Based on this prompt text, the AI ​​model automatically generates effective expressions to appeal to consumers. In this way, the system quickly reflects consumer interest in product strategies and provides an effective means to improve sales.

[0309] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0310] Step 1:

[0311] The server communicates with APIs from multiple information provision platforms and periodically retrieves keyword data. This input consists of search queries and trend data, and the data is returned in JSON format. Specifically, it sends requests to search engine APIs and saves the returned responses to a database. The output is a collection of raw data.

[0312] Step 2:

[0313] The server normalizes the collected raw data. The input is the raw data obtained in step 1, and the normalization process removes special characters and unnecessary elements. Specifically, it uses regular expressions to clean the data and format it into the required format. The output is normalized, clean data.

[0314] Step 3:

[0315] The server analyzes the normalized data. This input is the clean data obtained in step 2, and natural language processing techniques are used to analyze trends and consumer interests over a specific period. Specifically, this includes identifying themes using topic modeling and aggregating keyword frequencies. The output is an analysis that provides insights into consumer behavior.

[0316] Step 4:

[0317] The server automatically generates project proposals and taglines using a generative AI model. The input is the analysis results obtained in step 3, and the server creates prompt sentences based on these results. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves strategic expressions suggested by the model. The output is the generated project proposal and tagline.

[0318] Step 5:

[0319] The server notifies the terminal of the generated project proposal and catchphrase via the network. This input is the content generated in step 4 and is converted to a format suitable for the user's terminal. Specifically, it sends push notifications via email or a dedicated application to quickly deliver information to the user. The output is the notification that the user receives.

[0320] Step 6:

[0321] The user reviews the project proposal notified via their device and registers the relevant product information on the e-commerce platform. This input is the project proposal received in step 5, and the user enters the product information using the provided operation screen. Specific actions include manual product registration and information entry using templates. The output is the product data registered on the platform.

[0322] Step 7:

[0323] The server monitors sales information for registered products in real time. This input consists of product data and sales information registered in step 6, tracking sales volume and consumer response. Specifically, it retrieves data from the sales platform via an API and visualizes it on an analytics dashboard. The output is the sales analysis results.

[0324] Step 8:

[0325] The server generates feedback for sales improvement based on sales analysis results and notifies the user. The input is the analysis data obtained in step 7, and it creates guidelines for the next sales strategy. Specific actions include suggesting action items using a recommendation system. The output is a feedback notification and improvement suggestions provided to the user.

[0326] (Application Example 1)

[0327] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0328] In online sales, it is difficult to quickly grasp consumer interests and implement effective marketing strategies in a timely manner. Therefore, there is a need for a system that can develop appropriate sales strategies tailored to customer interests and optimize sales.

[0329] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0330] In this invention, the server includes means for collecting keyword information from multiple search mechanisms on a network, means for analyzing the collected keyword information and identifying trends during a specified period, and means for generating a plan related to a theme using generative AI and creating a catchy slogan. This makes it possible to quickly and effectively formulate and execute dynamic sales strategies based on consumer interests.

[0331] A "network" is a system in which multiple computers and devices are connected to exchange information and communications.

[0332] A "search engine" is a system used to search for information on the internet, and it finds relevant information based on keywords entered by the user.

[0333] "Keyword information" refers to the main terms that consumers use when searching the internet, and it is information that indicates trends and consumer interests.

[0334] "Generative AI" is a type of artificial intelligence technology that automatically generates new information and ideas based on learned data.

[0335] A "plan" is a set of detailed guidelines or policies formulated to achieve a specific objective.

[0336] A "catchphrase" is a short, memorable phrase used to attract consumers' attention in order to promote a product or service.

[0337] A "sales platform" is an online or offline platform for providing goods or services.

[0338] An "interface" is a connection or procedure that enables the exchange of information between a user and a system.

[0339] "Sales figures" refer to data that shows the sales volume or revenue of a product over a specific period.

[0340] "Opinions" are improvement measures or suggestions presented based on analysis.

[0341] "Trends" refer to changes or trends in fashion under specific periods or conditions.

[0342] To realize the system of this invention, the server acquires information via a network and collects keyword information from a search mechanism. The server uses this information to analyze the data using natural language processing technology, specifically TensorFlow, and identifies trends. Based on the results of this analysis, the server uses generative AI to formulate a plan related to the theme and create a catchy slogan.

[0343] The server transmits the generated plan and catchphrase to the user's terminal via the network. Based on the received content, the terminal provides an interface for easily registering relevant product information on the online sales platform. This interface was developed using Flutter and is designed to be intuitive and easy for the user to operate.

[0344] Furthermore, the server collects sales figures for registered products in real time and performs immediate analysis. The results of the analysis are provided to the user as feedback for improving sales strategies. This allows users to constantly develop and implement sales strategies optimized for consumer needs.

[0345] As a concrete example, consider a scenario where a server detects the trend "Halloween" and creates related plan proposals. In this case, the generation AI suggests a "Special Feature on Original Halloween Goods" and generates a related tagline: "Check out the items that will make your costume shine now!" This makes it easier for users to register Halloween-related products on the platform and allows for the rapid creation of a special feature page.

[0346] An example of a prompt for the generation AI model would be: "Analyze the latest keyword data and generate compelling promotional taglines based on consumer insights into summer fashion."

[0347] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0348] Step 1:

[0349] The server collects keyword information from multiple search mechanisms on the network. It uses authentication information for accessing network APIs as input. The output is a list of collected keywords. This process involves sending API requests and analyzing the retrieved responses to extract the target keywords.

[0350] Step 2:

[0351] The server analyzes the collected keyword information to identify search trends over a specific period. The input is the keyword list obtained in step 1. The output generates trend data as a result of the analysis. Using natural language processing techniques, it analyzes the frequency and time-series changes of keywords and performs specific actions to extract topics of consumer interest.

[0352] Step 3:

[0353] The server uses a generative AI to create a plan proposal and catchphrase based on the theme. The input is the trend data obtained in step 2. The output is the generated plan proposal and catchphrase. Prompt text is input to the generative AI, which then automatically generates an appropriate promotional message.

[0354] Step 4:

[0355] The server sends the generated plan and catchphrase to the terminal via the network. The input is the plan and catchphrase generated in step 3. The output is what is displayed on the user's terminal. This includes receiving the transmitted data on the terminal and allowing the user to confirm it.

[0356] Step 5:

[0357] The terminal provides an interface to assist in registering product information to the online sales platform based on the plan received from the server. The inputs are the plan and catchphrase received in step 4. The output is a registration support interface. Using Flutter, it provides forms and input fields for user-friendly UI operation.

[0358] Step 6:

[0359] The server collects sales figures for registered products in real time and generates suggestions for improving sales. The input is the latest sales data obtained from the sales platform. The output is feedback information based on the analysis results. It monitors sales data and uses analysis to develop strategies for improving sales.

[0360] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0361] This invention integrates an emotion engine that understands user sentiment into a project proposal generation system based on the analysis of search keyword data, in order to enhance the effectiveness of product sales on online sales platforms. Specific embodiments of this system are described below.

[0362] First, the server collects keyword data from multiple search engines. This can be done using API-based data acquisition or web scraping techniques. The collected data is preprocessed on the server and then provided for analysis as a cleaned dataset.

[0363] In the analysis process, the server uses natural language processing technology to analyze the data and identify trending keywords and frequently searched terms relevant to the time period. This allows for the development of featured themes for products and services.

[0364] Next, the AI ​​on the server generates a marketing campaign plan and tagline based on past data and the results of the current analysis. During this process, the emotion engine analyzes the user's past feedback and behavioral data, customizing the content to take the user's emotional state into account. This enables more personalized suggestions.

[0365] The generated project proposals and taglines are sent to the user's device via the network. The device receives the notification and provides an interface that allows the user to review the project proposals and taglines. Based on this information, the user can register products or prepare special feature pages.

[0366] The product information selected by the user is registered from the terminal to the online sales platform. Subsequently, the server collects and monitors sales data for the listed products in real time. In addition, the emotion engine collects emotion data through user interactions and customer reviews, and generates feedback based on consumer emotions.

[0367] For example, if the server analyzes a trending theme like "Spring Picnic," it can suggest a relaxation-themed feature that takes into account the user's past purchase history and feedback. The emotion engine detects positive emotional responses to the "Picnic" theme and creates a promotional message that reflects them.

[0368] In this way, this system comprehensively utilizes search data and sentiment data to support the planning and implementation of more effective product sales strategies.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] The server uses search engine APIs and web scraping techniques to collect vast amounts of keyword data, including trending keywords and frequently occurring words. This data reflects the latest consumer interests.

[0372] Step 2:

[0373] The server cleans up the collected keyword data, removing unnecessary information. It also standardizes the data and prepares it for analysis. This improves the consistency and accuracy of the data.

[0374] Step 3:

[0375] The server applies natural language processing techniques to analyze keyword data. This analysis identifies trends and important themes that have attracted attention during a specific period.

[0376] Step 4:

[0377] The server's AI generates advertising and sales promotion plans based on the analysis results. It also references data from past success stories to formulate the most optimal content.

[0378] Step 5:

[0379] The emotion engine analyzes the user's past behavioral data and feedback to determine their emotional state. This information is used to personalize the generated project proposals and taglines.

[0380] Step 6:

[0381] The server sends the generated project proposal and tagline to the user's terminal via the network. The terminal displays an interface that allows the user to review the project content and make revisions as needed.

[0382] Step 7:

[0383] Users review the notified project proposal on their devices and revise it as needed. The revised project proposal is then sent back to the server as feedback.

[0384] Step 8:

[0385] Based on the project proposal approved or modified by the user, relevant product information is registered on the online sales platform via the terminal. The terminal interface provides support to simplify the registration process.

[0386] Step 9:

[0387] The server monitors sales data for listed products in real time. It also uses an emotion engine to collect customer reviews and feedback and analyze consumers' emotional states.

[0388] Step 10:

[0389] The server analyzes sales data and sentiment data to generate more effective feedback and improvement suggestions. This information is then communicated to the user's device and used to inform future sales strategies.

[0390] This entire process enables highly personalized product sales that incorporate consumer emotions and trends.

[0391] (Example 2)

[0392] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0393] In modern e-commerce, there is a demand for the rapid and accurate provision of product information that matches consumer needs. However, conventional systems build marketing campaigns using only the analysis of search keyword data, and do not fully utilize consumer sentiment or past behavior history. As a result, personalized marketing becomes difficult, and it is difficult to increase sales.

[0394] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0395] In this invention, the server includes means for collecting information through multiple data acquisition means, means for analyzing the collected information and identifying trends over a specific period, and means having artificial intelligence that generates a plan and creates a representation on a theme using the analysis results and sentiment analysis. This makes it possible to plan personalized marketing campaigns that take into account consumer emotions and behavior, thereby increasing sales.

[0396] "Data acquisition means" refers to a device or method used to collect information required by an information processing device, and has the function of efficiently acquiring information from various data sources.

[0397] "Analysis means" refers to an apparatus or method used to analyze collected information and identify specific trends or patterns.

[0398] "Artificial intelligence" refers to a computer system that can perform specific tasks through learning and reasoning, and that can make creative or logical decisions based on the data obtained in the process.

[0399] "Expression" refers to text or visual content generated based on analyzed information, intended to effectively convey a message in line with a specific theme or purpose.

[0400] A "communication network" is a network infrastructure used for sending and receiving information, providing the necessary connections for exchanging data between different terminals.

[0401] A "display device" is a device that allows a user to visually confirm information, and typically includes screens and monitors.

[0402] An "e-commerce platform" is an online marketplace where goods and services are bought and sold, and it plays a role in connecting sellers and buyers.

[0403] "Transaction data" refers to information related to purchase history and sales statistics recorded in e-commerce, and plays an important role in marketing and inventory management.

[0404] A "response" is a message or report that indicates conclusions or recommended actions derived from the data received by the system, and is provided to support the user's decision-making.

[0405] This invention is a system for maximizing the effectiveness of marketing activities on e-commerce platforms. Specific embodiments thereof are described below.

[0406] First, the server collects information from multiple data sources as a means of obtaining information. This process includes methods such as using search engine APIs to obtain data and utilizing web scraping techniques. The acquired information is organized on the server and then analyzed.

[0407] Next, the server uses natural language processing techniques to analyze the acquired data. This analysis can utilize libraries such as Python's NLTK or spaCy. This allows for the extraction of trends and patterns over a specific period, which can then be incorporated into marketing activity plans.

[0408] The AI ​​model on the server generates a plan related to the theme based on the analysis results, and simultaneously creates a representation. This process incorporates the analysis results of the user's past transaction data and sentiment data to help provide personalized marketing strategies.

[0409] The generated results are notified to the user's terminal via the communication network. The terminal, acting as a display device, presents this information to the user, allowing the user to evaluate the plan and its representation through an appropriate interface.

[0410] Users can register product information on the e-commerce platform based on the information they receive. The server then collects and analyzes transaction data for the registered products in real time. Based on the insights gained from this transaction data, it generates further responses, contributing to increased sales.

[0411] For example, if the server analyzes the theme "Spring Picnic," the generative AI model can propose a feature that emphasizes relaxation based on past consumer behavior and emotional data. It can also use emotional data to create expressions that highlight the positive image of a "picnic."

[0412] An example of a prompt is: "Analyze trending keywords related to spring picnics and generate a plan that takes user sentiment into account."

[0413] Thus, the system of the present invention aims to enhance competitiveness in e-commerce by comprehensively analyzing diverse information and providing personalized marketing strategies based on consumer emotions and behavior.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] The server first uses information acquisition methods to collect necessary data via search engine APIs and web scraping techniques. The input consists of a list of target keywords and related URLs, and the server extracts keyword data and related information from the web based on this. The output is a cleaned dataset, with duplicates and noise removed, which is then passed on to the next analysis step.

[0417] Step 2:

[0418] The server applies natural language processing techniques to the obtained dataset to perform trend analysis. At this stage, Python's NLTK and spaCy are used to tokenize the data, tag parts of speech, and analyze sentence structure. The input is the dataset generated in the previous step, and the output is a list of trending keywords and frequently searched terms for each period. This allows for the extraction of specific trends and themes.

[0419] Step 3:

[0420] The AI ​​model generation step on the server is where a marketing plan is generated based on the analysis results. The inputs here are a list of trending keywords and historical consumer data. The AI ​​model combines these to generate a plan and tagline tailored to the target market. The output is a personalized marketing strategy that takes user emotions into consideration.

[0421] Step 4:

[0422] The generated plan and tagline are transmitted from the server to the user's terminal via the communication network. The terminal receives this data and presents the information through an interface that the user can view. The input is data from the server, and the output is visualized information displayed through the user-operated interface.

[0423] Step 5:

[0424] The user registers product information on the e-commerce platform based on information received via their device. The user's input process enters the product name, description, price, etc., into the platform, completing the registration. The output is a publicly available product page.

[0425] Step 6:

[0426] The server instantly collects and analyzes transaction data for registered products and generates responses to improve sales. This process involves evaluating transaction statistics and performing sentiment analysis of customer reviews using a sentiment engine. Inputs are real-time sales data and customer feedback, and outputs are feedback reports that include improvement suggestions and revisions to marketing strategies.

[0427] (Application Example 2)

[0428] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0429] Online sales platforms require sophisticated product sales strategies that respond to the diverse needs and emotional responses of consumers. While traditional sales systems can grasp broad trends through search keyword analysis, they have struggled to implement targeted marketing based on the emotions of individual consumers. Therefore, a method is needed to develop more effective and personalized sales strategies.

[0430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0431] In this invention, the server is an information processing device for acquiring data, and includes means for collecting keyword information from multiple data collection systems on the web, means having a generative artificial intelligence that generates project proposals related to a theme and creates promotional messages based on the analysis results, and means having an emotion analysis mechanism for analyzing user emotion data and generating personalized advertising content. This enables the efficient generation of advertising content that is tailored to the individual needs and emotions of consumers.

[0432] An "information processing device" is used to collect keyword information from multiple data collection systems on the web.

[0433] "Keyword information" refers to information that indicates a specific theme or trend, obtained through users' search behavior.

[0434] "Generative artificial intelligence" is an artificial intelligence that has the function of generating project proposals related to a theme and creating promotional messages based on analysis results.

[0435] A "communication network" is a network used to notify users of generated project proposals and promotional messages to their electronic devices.

[0436] An "interface" is a system that provides a point of contact with the user to register relevant product information in the online sales system based on the content of the notification.

[0437] "Sales data" refers to data that includes sales information for registered products and is used to provide feedback for improving sales.

[0438] A "sentiment analysis mechanism" is a system that analyzes users' emotional data and has the function of generating personalized advertising content.

[0439] The system for implementing this invention is centered around a server that functions as an information processing device. The server acquires keyword information from multiple data collection systems on the internet for data collection. Web scraping libraries such as the Google Keyword Planner API and Beautiful Soup are used for this purpose. The acquired keyword information is analyzed using natural language processing techniques to identify search frequency and trends within a specific time frame. Libraries such as Spacy and NLTK are used for this analysis.

[0440] Based on the analysis results, generative artificial intelligence generates project proposals and promotional messages related to the theme. Here, generative AI models such as OpenAI's GPT are used, combining collected data with past sales performance data to provide optimal suggestions. During this process, user emotional data is collected through the user's electronic devices, and this data is analyzed by an emotional analysis mechanism. VADER and TextBlob are used specifically for emotional analysis.

[0441] A distinctive feature of this system is the advertising content that users are notified of. These notifications are sent via a communication network, and the user's device receives them, providing an interface for registering relevant product information in the online sales system. Through this process, advertising content optimized for the individual needs and emotions of consumers is generated.

[0442] For example, if the server analyzes trends related to "spring picnics," it will consider the user's past purchase history and sentiment data to generate a personalized message such as, "Why not refresh yourself with some new picnic gear?" An example of a prompt to the generating AI model would be, "We have determined that the user is interested in 'spring picnics' and has positive feelings towards them. Please generate advertising copy related to picnics."

[0443] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0444] Step 1:

[0445] The server retrieves keyword information from multiple data collection systems on the internet. It uses the Google Keyword Planner API and Beautiful Soup as input to collect the necessary keyword data. The collected data is then cleaned up and output with noise removed.

[0446] Step 2:

[0447] The server analyzes the collected keyword information using natural language processing techniques. Spacy and NLTK are used to tokenize, normalize, and extract nouns from the data. The input is the cleaned keyword information obtained in step 1, and the output is data that identifies trends and search frequencies within a specific time frame.

[0448] Step 3:

[0449] The server generates a project proposal and promotional message using a generative AI model based on the analysis results. During this process, it uses OpenAI's GPT to create prompt text, resulting in personalized advertising content. The input is the analysis data obtained in step 2, and the output is the generated project proposal and advertising message.

[0450] Step 4:

[0451] The user's device receives notifications from the server via the network and displays the generated project proposal and promotional message. The input is the advertising content generated in step 3, which is output as an interface that the user can view.

[0452] Step 5:

[0453] User emotion data is transmitted to the server via the terminal. This data is input from the user's terminal and analyzed by an emotion analysis mechanism on the server. Specifically, positive or negative emotions are evaluated using VADER and TextBlob, and the results are output.

[0454] Step 6:

[0455] The server optimizes product information for registration in the online sales system based on the user's emotional data and the generated project proposal. The input here is the emotional data from step 5 and the project proposal from step 3, and the output is optimized product registration information that takes these into account.

[0456] Step 7:

[0457] The user's device verifies the optimized product information and registers it in the online sales system as needed. The input is the optimized product information obtained in step 6, and the user's actions result in the product being registered on the online platform as the final output.

[0458] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0459] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0460] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0464] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0465] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0466] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0467] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0468] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0469] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0470] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0471] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0472] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0473] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0474] This invention is a system that obtains insights into consumer behavior on online shopping platforms through the collection and analysis of keyword data obtained from multiple search engines. This system is centered around an information processing device (server) and can be implemented in the following forms.

[0475] First, the server periodically collects trend and frequently occurring keyword data via the internet using APIs or scraping from various search engines. Then, after preprocessing the collected data, such as removing noise and standardizing it, the server analyzes it using natural language processing to identify topics of consumer interest. This allows for accurate understanding of changing trends over time.

[0476] Based on the generated data, the AI ​​on the server automatically creates effective sales promotion plans and slogans, referencing past sales performance and success stories. This enables the rapid development of marketing strategies. This information is immediately notified to the user's terminal via the network. Through the terminal's interface, the user can review the notified plan and modify or approve it as needed.

[0477] Next, the user selects products that match the decided project plan and lists those products on the online sales platform via the terminal. The terminal is equipped with an appropriate input interface to simplify product information entry and allow for the rapid creation of special feature pages.

[0478] Subsequently, the server monitors sales data for the listed products in real time and analyzes sales performance. Based on the data obtained, the server generates feedback to improve sales and notifies the terminal of this information. This feedback loop allows users to improve their product sales strategies and optimize sales.

[0479] For example, if the server analyzes the trending keyword "summer festival" and generates a project proposal such as "Cool Yukata Special," users will then list related yukata and summer festival goods on an online platform based on that proposal. The server tracks the sales data of these products and notifies users as needed about the effectiveness of the campaign and the need for additional promotion.

[0480] Thus, the system of the present invention provides an effective means of quickly reflecting consumer interests in product strategies and increasing sales.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The server uses search engine APIs via the internet to collect keyword data. The data collected includes terms that were frequently searched or trending within a specific period.

[0484] Step 2:

[0485] The server preprocesses the collected keyword data. This includes removing duplicates and noise, and normalizing the keywords. This ensures the reliability and integrity of the data.

[0486] Step 3:

[0487] The server applies natural language processing algorithms to identify consumer interests using pre-processed data. This allows it to identify trending themes that are relevant to the time period and region.

[0488] Step 4:

[0489] The AI ​​on the server generates project proposals and taglines based on analyzed trends. Based on past sales performance, it predicts anticipated consumer reactions and creates optimized content.

[0490] Step 5:

[0491] The server notifies the user's terminal of the generated project proposal and tagline. The terminal receives this information and provides an interface for displaying it to the user.

[0492] Step 6:

[0493] The user reviews the notification on their device and makes corrections as needed. They then send the corrected information to the server as feedback.

[0494] Step 7:

[0495] Users select products based on information received from the server and list them on the online sales platform. An interface for efficiently registering product information is provided on the terminal.

[0496] Step 8:

[0497] The server collects and constantly monitors sales data for listed products in real time. It analyzes sales volume, sales amount, and consumer purchasing trends.

[0498] Step 9:

[0499] The server analyzes sales trends based on the collected sales data, generates feedback for the user regarding the success rate of promotions and areas for improvement, and notifies the user's device.

[0500] Step 10:

[0501] Users can review feedback via their devices and use it to inform future planning and sales strategies, thereby enabling continuous improvement.

[0502] Through these steps, the system enables efficient product sales tailored to consumer interests.

[0503] (Example 1)

[0504] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0505] In the online marketplace, quickly understanding consumer interests and developing effective product strategies is essential for increasing sales. However, existing systems have limited data acquisition and analysis capabilities, making it difficult to conduct effective marketing activities in a short period. In particular, there has been a lack of systems that can capture rapidly changing consumer trends and utilize them for sales promotion. This creates a challenge in that it increases the risk of missing potential sales opportunities.

[0506] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0507] In this invention, the server includes means for communicating via a network and acquiring string data from multiple information provision platforms on the web; means for normalizing the acquired string data, removing unnecessary elements, and analyzing it to identify data trends over a certain period of time; and means equipped with artificial intelligence that automatically generates project proposals and attractive expressions related to a specific theme based on the analysis. This makes it possible to quickly reflect consumer interests in product strategies, optimize marketing effectiveness, and increase sales.

[0508] An "information processing device" is an electronic device used to acquire, analyze, and process data, and can access external databases and systems via a network.

[0509] "String data" refers to a set of characters and numbers used to represent specific information, and is usually represented in text format.

[0510] "Normalization" is a process that unifies the format and structure of data, and is used to improve efficiency when performing data analysis.

[0511] "Unnecessary elements" are data or information that hinder the extraction of useful information during analysis or processing, and are therefore subject to filtering.

[0512] "Analysis" is the process of examining collected data from multiple perspectives and extracting useful information and insights from it.

[0513] "Artificial intelligence" is a form of computer system that has the function of automatically learning and making decisions based on data, and plays a role in streamlining generation and analysis.

[0514] A "project proposal" is a plan or proposal that outlines a concept for achieving a specific goal, and it is an important element in marketing and strategy planning.

[0515] "Expression" refers to a concrete form, such as text or images, used to convey a specific intention or meaning, and is used in communication.

[0516] A "communication network" is a collection of electrical or optical lines and related equipment for sending and receiving information, and is a means of transmitting data quickly and efficiently.

[0517] An "operation screen" is the interface used by users when operating a system or device, and serves as a point of contact for inputting instructions and confirming information.

[0518] An "e-commerce platform" is a foundational system for buying and selling goods and services online, serving as a platform that connects consumers and sellers.

[0519] "Sales information" refers to data related to the transaction of goods or services, including sales figures, sales quantities, and buyer demographics.

[0520] A "guideline" is something that indicates the direction or policy for achieving a specific goal, and serves as a standard for actions and decision-making.

[0521] This invention provides an information processing system for obtaining insights into consumer behavior in online markets. The system is primarily server-based. The server communicates via a network and retrieves string data from multiple information-providing platforms on the web. Specifically, it can periodically collect keyword data using APIs from search engines such as Google and Bing.

[0522] The server normalizes the collected string data, removes unnecessary elements, and then analyzes it. This utilizes data preprocessing techniques using programming languages ​​such as Python and R. The preprocessed data is then analyzed by an automated artificial intelligence (AI) and used as evidence to explore market trends and consumer interests. A commonly used generative AI model (e.g., GPT-3) can be employed as the AI ​​model.

[0523] The AI-generated project proposals and taglines are related to a specific theme and are sent to the device in an appealing format. The device user reviews these and makes adjustments or approvals as needed. This information supports product registration on e-commerce platforms, allowing for quick listing through a simplified interface.

[0524] For example, a server might acquire the trending keyword "summer festival," and an automatically generated artificial intelligence might suggest a "Cool Yukata Special." Based on this plan, users would then list related products, such as yukata or summer festival-related goods, on an e-commerce platform. By developing such strategies, companies can conduct timely marketing activities.

[0525] An example of the prompt text mentioned above would be: "Generate a catchy slogan and promotional ideas for the online store based on this month's trending keywords. Keywords: summer festival, yukata, merchandise." Based on this prompt text, the AI ​​model automatically generates effective expressions to appeal to consumers. In this way, the system quickly reflects consumer interest in product strategies and provides an effective means to improve sales.

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1:

[0528] The server communicates with APIs from multiple information provision platforms and periodically retrieves keyword data. This input consists of search queries and trend data, and the data is returned in JSON format. Specifically, it sends requests to search engine APIs and saves the returned responses to a database. The output is a collection of raw data.

[0529] Step 2:

[0530] The server normalizes the collected raw data. The input is the raw data obtained in step 1, and the normalization process removes special characters and unnecessary elements. Specifically, it uses regular expressions to clean the data and format it into the required format. The output is normalized, clean data.

[0531] Step 3:

[0532] The server analyzes the normalized data. This input is the clean data obtained in step 2, and natural language processing techniques are used to analyze trends and consumer interests over a specific period. Specifically, this includes identifying themes using topic modeling and aggregating keyword frequencies. The output is an analysis that provides insights into consumer behavior.

[0533] Step 4:

[0534] The server automatically generates project proposals and taglines using a generative AI model. The input is the analysis results obtained in step 3, and the server creates prompt sentences based on these results. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves strategic expressions suggested by the model. The output is the generated project proposal and tagline.

[0535] Step 5:

[0536] The server notifies the terminal of the generated project proposal and catchphrase via the network. This input is the content generated in step 4 and is converted to a format suitable for the user's terminal. Specifically, it sends push notifications via email or a dedicated application to quickly deliver information to the user. The output is the notification that the user receives.

[0537] Step 6:

[0538] The user reviews the project proposal notified via their device and registers the relevant product information on the e-commerce platform. This input is the project proposal received in step 5, and the user enters the product information using the provided operation screen. Specific actions include manual product registration and information entry using templates. The output is the product data registered on the platform.

[0539] Step 7:

[0540] The server monitors sales information for registered products in real time. This input consists of product data and sales information registered in step 6, tracking sales volume and consumer response. Specifically, it retrieves data from the sales platform via an API and visualizes it on an analytics dashboard. The output is the sales analysis results.

[0541] Step 8:

[0542] The server generates feedback for sales improvement based on sales analysis results and notifies the user. The input is the analysis data obtained in step 7, and it creates guidelines for the next sales strategy. Specific actions include suggesting action items using a recommendation system. The output is a feedback notification and improvement suggestions provided to the user.

[0543] (Application Example 1)

[0544] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] In online sales, it is difficult to quickly grasp consumer interests and implement effective marketing strategies in a timely manner. Therefore, there is a need for a system that can develop appropriate sales strategies tailored to customer interests and optimize sales.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0547] In this invention, the server includes means for collecting keyword information from multiple search mechanisms on a network, means for analyzing the collected keyword information and identifying trends during a specified period, and means for generating a plan related to a theme using generative AI and creating a catchy slogan. This makes it possible to quickly and effectively formulate and execute dynamic sales strategies based on consumer interests.

[0548] A "network" is a system in which multiple computers and devices are connected to exchange information and communications.

[0549] A "search engine" is a system used to search for information on the internet, and it finds relevant information based on keywords entered by the user.

[0550] "Keyword information" refers to the main terms that consumers use when searching the internet, and it is information that indicates trends and consumer interests.

[0551] "Generative AI" is a type of artificial intelligence technology that automatically generates new information and ideas based on learned data.

[0552] A "plan" is a set of detailed guidelines or policies formulated to achieve a specific objective.

[0553] A "catchphrase" is a short, memorable phrase used to attract consumers' attention in order to promote a product or service.

[0554] A "sales platform" is an online or offline platform for providing goods or services.

[0555] An "interface" is a connection or procedure that enables the exchange of information between a user and a system.

[0556] "Sales figures" refer to data that shows the sales volume or revenue of a product over a specific period.

[0557] "Opinions" are improvement measures or suggestions presented based on analysis.

[0558] "Trends" refer to changes or trends in fashion under specific periods or conditions.

[0559] To realize the system of this invention, the server acquires information via a network and collects keyword information from a search mechanism. The server uses this information to analyze the data using natural language processing technology, specifically TensorFlow, and identifies trends. Based on the results of this analysis, the server uses generative AI to formulate a plan related to the theme and create a catchy slogan.

[0560] The server transmits the generated plan and catchphrase to the user's terminal via the network. Based on the received content, the terminal provides an interface for easily registering relevant product information on the online sales platform. This interface was developed using Flutter and is designed to be intuitive and easy for the user to operate.

[0561] Furthermore, the server collects sales figures for registered products in real time and performs immediate analysis. The results of the analysis are provided to the user as feedback for improving sales strategies. This allows users to constantly develop and implement sales strategies optimized for consumer needs.

[0562] As a concrete example, consider a scenario where a server detects the trend "Halloween" and creates related plan proposals. In this case, the generation AI suggests a "Special Feature on Original Halloween Goods" and generates a related tagline: "Check out the items that will make your costume shine now!" This makes it easier for users to register Halloween-related products on the platform and allows for the rapid creation of a special feature page.

[0563] An example of a prompt for the generation AI model would be: "Analyze the latest keyword data and generate compelling promotional taglines based on consumer insights into summer fashion."

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The server collects keyword information from multiple search mechanisms on the network. It uses authentication information for accessing network APIs as input. The output is a list of collected keywords. This process involves sending API requests and analyzing the retrieved responses to extract the target keywords.

[0567] Step 2:

[0568] The server analyzes the collected keyword information to identify search trends over a specific period. The input is the keyword list obtained in step 1. The output generates trend data as a result of the analysis. Using natural language processing techniques, it analyzes the frequency and time-series changes of keywords and performs specific actions to extract topics of consumer interest.

[0569] Step 3:

[0570] The server uses a generative AI to create a plan proposal and catchphrase based on the theme. The input is the trend data obtained in step 2. The output is the generated plan proposal and catchphrase. Prompt text is input to the generative AI, which then automatically generates an appropriate promotional message.

[0571] Step 4:

[0572] The server sends the generated plan and catchphrase to the terminal via the network. The input is the plan and catchphrase generated in step 3. The output is what is displayed on the user's terminal. This includes receiving the transmitted data on the terminal and allowing the user to confirm it.

[0573] Step 5:

[0574] The terminal provides an interface to assist in registering product information to the online sales platform based on the plan received from the server. The inputs are the plan and catchphrase received in step 4. The output is a registration support interface. Using Flutter, it provides forms and input fields for user-friendly UI operation.

[0575] Step 6:

[0576] The server collects sales figures for registered products in real time and generates suggestions for improving sales. The input is the latest sales data obtained from the sales platform. The output is feedback information based on the analysis results. It monitors sales data and uses analysis to develop strategies for improving sales.

[0577] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0578] This invention integrates an emotion engine that understands user sentiment into a project proposal generation system based on the analysis of search keyword data, in order to enhance the effectiveness of product sales on online sales platforms. Specific embodiments of this system are described below.

[0579] First, the server collects keyword data from multiple search engines. This can be done using API-based data acquisition or web scraping techniques. The collected data is preprocessed on the server and then provided for analysis as a cleaned dataset.

[0580] In the analysis process, the server uses natural language processing technology to analyze the data and identify trending keywords and frequently searched terms relevant to the time period. This allows for the development of featured themes for products and services.

[0581] Next, the AI ​​on the server generates a marketing campaign plan and tagline based on past data and the results of the current analysis. During this process, the emotion engine analyzes the user's past feedback and behavioral data, customizing the content to take the user's emotional state into account. This enables more personalized suggestions.

[0582] The generated project proposals and taglines are sent to the user's device via the network. The device receives the notification and provides an interface that allows the user to review the project proposals and taglines. Based on this information, the user can register products or prepare special feature pages.

[0583] The product information selected by the user is registered from the terminal to the online sales platform. Subsequently, the server collects and monitors sales data for the listed products in real time. In addition, the emotion engine collects emotion data through user interactions and customer reviews, and generates feedback based on consumer emotions.

[0584] For example, if the server analyzes a trending theme like "Spring Picnic," it can suggest a relaxation-themed feature that takes into account the user's past purchase history and feedback. The emotion engine detects positive emotional responses to the "Picnic" theme and creates a promotional message that reflects them.

[0585] In this way, this system comprehensively utilizes search data and sentiment data to support the planning and implementation of more effective product sales strategies.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] The server uses search engine APIs and web scraping techniques to collect vast amounts of keyword data, including trending keywords and frequently occurring words. This data reflects the latest consumer interests.

[0589] Step 2:

[0590] The server cleans up the collected keyword data, removing unnecessary information. It also standardizes the data and prepares it for analysis. This improves the consistency and accuracy of the data.

[0591] Step 3:

[0592] The server applies natural language processing techniques to analyze keyword data. This analysis identifies trends and important themes that have attracted attention during a specific period.

[0593] Step 4:

[0594] The server's AI generates advertising and sales promotion plans based on the analysis results. It also references data from past success stories to formulate the most optimal content.

[0595] Step 5:

[0596] The emotion engine analyzes the user's past behavioral data and feedback to determine their emotional state. This information is used to personalize the generated project proposals and taglines.

[0597] Step 6:

[0598] The server sends the generated project proposal and tagline to the user's terminal via the network. The terminal displays an interface that allows the user to review the project content and make revisions as needed.

[0599] Step 7:

[0600] Users review the notified project proposal on their devices and revise it as needed. The revised project proposal is then sent back to the server as feedback.

[0601] Step 8:

[0602] Based on the project proposal approved or modified by the user, relevant product information is registered on the online sales platform via the terminal. The terminal interface provides support to simplify the registration process.

[0603] Step 9:

[0604] The server monitors sales data for listed products in real time. It also uses an emotion engine to collect customer reviews and feedback and analyze consumers' emotional states.

[0605] Step 10:

[0606] The server analyzes sales data and sentiment data to generate more effective feedback and improvement suggestions. This information is then communicated to the user's device and used to inform future sales strategies.

[0607] This entire process enables highly personalized product sales that incorporate consumer emotions and trends.

[0608] (Example 2)

[0609] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0610] In modern e-commerce, there is a demand for the rapid and accurate provision of product information that matches consumer needs. However, conventional systems build marketing campaigns using only the analysis of search keyword data, and do not fully utilize consumer sentiment or past behavior history. As a result, personalized marketing becomes difficult, and it is difficult to increase sales.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0612] In this invention, the server includes means for collecting information through multiple data acquisition means, means for analyzing the collected information and identifying trends over a specific period, and means having artificial intelligence that generates a plan and creates a representation on a theme using the analysis results and sentiment analysis. This makes it possible to plan personalized marketing campaigns that take into account consumer emotions and behavior, thereby increasing sales.

[0613] "Data acquisition means" refers to a device or method used to collect information required by an information processing device, and has the function of efficiently acquiring information from various data sources.

[0614] "Analysis means" refers to an apparatus or method used to analyze collected information and identify specific trends or patterns.

[0615] "Artificial intelligence" refers to a computer system that can perform specific tasks through learning and reasoning, and that can make creative or logical decisions based on the data obtained in the process.

[0616] "Expression" refers to text or visual content generated based on analyzed information, intended to effectively convey a message in line with a specific theme or purpose.

[0617] A "communication network" is a network infrastructure used for sending and receiving information, providing the necessary connections for exchanging data between different terminals.

[0618] A "display device" is a device that allows a user to visually confirm information, and typically includes screens and monitors.

[0619] An "e-commerce platform" is an online marketplace where goods and services are bought and sold, and it plays a role in connecting sellers and buyers.

[0620] "Transaction data" refers to information related to purchase history and sales statistics recorded in e-commerce, and plays an important role in marketing and inventory management.

[0621] A "response" is a message or report that indicates conclusions or recommended actions derived from the data received by the system, and is provided to support the user's decision-making.

[0622] This invention is a system for maximizing the effectiveness of marketing activities on e-commerce platforms. Specific embodiments thereof are described below.

[0623] First, the server collects information from multiple data sources as a means of obtaining information. This process includes methods such as using search engine APIs to obtain data and utilizing web scraping techniques. The acquired information is organized on the server and then analyzed.

[0624] Next, the server uses natural language processing techniques to analyze the acquired data. This analysis can utilize libraries such as Python's NLTK or spaCy. This allows for the extraction of trends and patterns over a specific period, which can then be incorporated into marketing activity plans.

[0625] The AI ​​model on the server generates a plan related to the theme based on the analysis results, and simultaneously creates a representation. This process incorporates the analysis results of the user's past transaction data and sentiment data to help provide personalized marketing strategies.

[0626] The generated results are notified to the user's terminal via the communication network. The terminal, acting as a display device, presents this information to the user, allowing the user to evaluate the plan and its representation through an appropriate interface.

[0627] Users can register product information on the e-commerce platform based on the information they receive. The server then collects and analyzes transaction data for the registered products in real time. Based on the insights gained from this transaction data, it generates further responses, contributing to increased sales.

[0628] For example, if the server analyzes the theme "Spring Picnic," the generative AI model can propose a feature that emphasizes relaxation based on past consumer behavior and emotional data. It can also use emotional data to create expressions that highlight the positive image of a "picnic."

[0629] An example of a prompt is: "Analyze trending keywords related to spring picnics and generate a plan that takes user sentiment into account."

[0630] Thus, the system of the present invention aims to enhance competitiveness in e-commerce by comprehensively analyzing diverse information and providing personalized marketing strategies based on consumer emotions and behavior.

[0631] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0632] Step 1:

[0633] The server first uses information acquisition methods to collect necessary data via search engine APIs and web scraping techniques. The input consists of a list of target keywords and related URLs, and the server extracts keyword data and related information from the web based on this. The output is a cleaned dataset, with duplicates and noise removed, which is then passed on to the next analysis step.

[0634] Step 2:

[0635] The server applies natural language processing techniques to the obtained dataset to perform trend analysis. At this stage, Python's NLTK and spaCy are used to tokenize the data, tag parts of speech, and analyze sentence structure. The input is the dataset generated in the previous step, and the output is a list of trending keywords and frequently searched terms for each period. This allows for the extraction of specific trends and themes.

[0636] Step 3:

[0637] The AI ​​model generation step on the server is where a marketing plan is generated based on the analysis results. The inputs here are a list of trending keywords and historical consumer data. The AI ​​model combines these to generate a plan and tagline tailored to the target market. The output is a personalized marketing strategy that takes user emotions into consideration.

[0638] Step 4:

[0639] The generated plan and tagline are transmitted from the server to the user's terminal via the communication network. The terminal receives this data and presents the information through an interface that the user can view. The input is data from the server, and the output is visualized information displayed through the user-operated interface.

[0640] Step 5:

[0641] The user registers product information on the e-commerce platform based on information received via their device. The user's input process enters the product name, description, price, etc., into the platform, completing the registration. The output is a publicly available product page.

[0642] Step 6:

[0643] The server instantly collects and analyzes transaction data for registered products and generates responses to improve sales. This process involves evaluating transaction statistics and performing sentiment analysis of customer reviews using a sentiment engine. Inputs are real-time sales data and customer feedback, and outputs are feedback reports that include improvement suggestions and revisions to marketing strategies.

[0644] (Application Example 2)

[0645] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0646] Online sales platforms require sophisticated product sales strategies that respond to the diverse needs and emotional responses of consumers. While traditional sales systems can grasp broad trends through search keyword analysis, they have struggled to implement targeted marketing based on the emotions of individual consumers. Therefore, a method is needed to develop more effective and personalized sales strategies.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0648] In this invention, the server is an information processing device for acquiring data, and includes means for collecting keyword information from multiple data collection systems on the web, means having a generative artificial intelligence that generates project proposals related to a theme and creates promotional messages based on the analysis results, and means having an emotion analysis mechanism for analyzing user emotion data and generating personalized advertising content. This enables the efficient generation of advertising content that is tailored to the individual needs and emotions of consumers.

[0649] An "information processing device" is used to collect keyword information from multiple data collection systems on the web.

[0650] "Keyword information" refers to information that indicates a specific theme or trend, obtained through users' search behavior.

[0651] "Generative artificial intelligence" is an artificial intelligence that has the function of generating project proposals related to a theme and creating promotional messages based on analysis results.

[0652] A "communication network" is a network used to notify users of generated project proposals and promotional messages to their electronic devices.

[0653] An "interface" is a system that provides a point of contact with the user to register relevant product information in the online sales system based on the content of the notification.

[0654] "Sales data" refers to data that includes sales information for registered products and is used to provide feedback for improving sales.

[0655] A "sentiment analysis mechanism" is a system that analyzes users' emotional data and has the function of generating personalized advertising content.

[0656] The system for implementing this invention is centered around a server that functions as an information processing device. The server acquires keyword information from multiple data collection systems on the internet for data collection. Web scraping libraries such as the Google Keyword Planner API and Beautiful Soup are used for this purpose. The acquired keyword information is analyzed using natural language processing techniques to identify search frequency and trends within a specific time frame. Libraries such as Spacy and NLTK are used for this analysis.

[0657] Based on the analysis results, generative artificial intelligence generates project proposals and promotional messages related to the theme. Here, generative AI models such as OpenAI's GPT are used, combining collected data with past sales performance data to provide optimal suggestions. During this process, user emotional data is collected through the user's electronic devices, and this data is analyzed by an emotional analysis mechanism. VADER and TextBlob are used specifically for emotional analysis.

[0658] A distinctive feature of this system is the advertising content that users are notified of. These notifications are sent via a communication network, and the user's device receives them, providing an interface for registering relevant product information in the online sales system. Through this process, advertising content optimized for the individual needs and emotions of consumers is generated.

[0659] For example, if the server analyzes trends related to "spring picnics," it will consider the user's past purchase history and sentiment data to generate a personalized message such as, "Why not refresh yourself with some new picnic gear?" An example of a prompt to the generating AI model would be, "We have determined that the user is interested in 'spring picnics' and has positive feelings towards them. Please generate advertising copy related to picnics."

[0660] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0661] Step 1:

[0662] The server retrieves keyword information from multiple data collection systems on the internet. It uses the Google Keyword Planner API and Beautiful Soup as input to collect the necessary keyword data. The collected data is then cleaned up and output with noise removed.

[0663] Step 2:

[0664] The server analyzes the collected keyword information using natural language processing techniques. Spacy and NLTK are used to tokenize, normalize, and extract nouns from the data. The input is the cleaned keyword information obtained in step 1, and the output is data that identifies trends and search frequencies within a specific time frame.

[0665] Step 3:

[0666] The server generates a project proposal and promotional message using a generative AI model based on the analysis results. During this process, it uses OpenAI's GPT to create prompt text, resulting in personalized advertising content. The input is the analysis data obtained in step 2, and the output is the generated project proposal and advertising message.

[0667] Step 4:

[0668] The user's device receives notifications from the server via the network and displays the generated project proposal and promotional message. The input is the advertising content generated in step 3, which is output as an interface that the user can view.

[0669] Step 5:

[0670] User emotion data is transmitted to the server via the terminal. This data is input from the user's terminal and analyzed by an emotion analysis mechanism on the server. Specifically, positive or negative emotions are evaluated using VADER and TextBlob, and the results are output.

[0671] Step 6:

[0672] The server optimizes product information for registration in the online sales system based on the user's emotional data and the generated project proposal. The input here is the emotional data from step 5 and the project proposal from step 3, and the output is optimized product registration information that takes these into account.

[0673] Step 7:

[0674] The user's device verifies the optimized product information and registers it in the online sales system as needed. The input is the optimized product information obtained in step 6, and the user's actions result in the product being registered on the online platform as the final output.

[0675] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0676] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0677] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0678] [Fourth Embodiment]

[0679] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0680] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0681] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0682] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0683] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0684] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0685] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0686] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0687] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0688] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0689] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0690] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0691] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] This invention is a system that obtains insights into consumer behavior on online shopping platforms through the collection and analysis of keyword data obtained from multiple search engines. This system is centered around an information processing device (server) and can be implemented in the following forms.

[0693] First, the server periodically collects trend and frequently occurring keyword data via the internet using APIs or scraping from various search engines. Then, after preprocessing the collected data, such as removing noise and standardizing it, the server analyzes it using natural language processing to identify topics of consumer interest. This allows for accurate understanding of changing trends over time.

[0694] Based on the generated data, the AI ​​on the server automatically creates effective sales promotion plans and slogans, referencing past sales performance and success stories. This enables the rapid development of marketing strategies. This information is immediately notified to the user's terminal via the network. Through the terminal's interface, the user can review the notified plan and modify or approve it as needed.

[0695] Next, the user selects products that match the decided project plan and lists those products on the online sales platform via the terminal. The terminal is equipped with an appropriate input interface to simplify product information entry and allow for the rapid creation of special feature pages.

[0696] Subsequently, the server monitors sales data for the listed products in real time and analyzes sales performance. Based on the data obtained, the server generates feedback to improve sales and notifies the terminal of this information. This feedback loop allows users to improve their product sales strategies and optimize sales.

[0697] For example, if the server analyzes the trending keyword "summer festival" and generates a project proposal such as "Cool Yukata Special," users will then list related yukata and summer festival goods on an online platform based on that proposal. The server tracks the sales data of these products and notifies users as needed about the effectiveness of the campaign and the need for additional promotion.

[0698] Thus, the system of the present invention provides an effective means of quickly reflecting consumer interests in product strategies and increasing sales.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server uses search engine APIs via the internet to collect keyword data. The data collected includes terms that were frequently searched or trending within a specific period.

[0702] Step 2:

[0703] The server preprocesses the collected keyword data. This includes removing duplicates and noise, and normalizing the keywords. This ensures the reliability and integrity of the data.

[0704] Step 3:

[0705] The server applies natural language processing algorithms to identify consumer interests using pre-processed data. This allows it to identify trending themes that are relevant to the time period and region.

[0706] Step 4:

[0707] The AI ​​on the server generates project proposals and taglines based on analyzed trends. Based on past sales performance, it predicts anticipated consumer reactions and creates optimized content.

[0708] Step 5:

[0709] The server notifies the user's terminal of the generated project proposal and tagline. The terminal receives this information and provides an interface for displaying it to the user.

[0710] Step 6:

[0711] The user reviews the notification on their device and makes corrections as needed. They then send the corrected information to the server as feedback.

[0712] Step 7:

[0713] Users select products based on information received from the server and list them on the online sales platform. An interface for efficiently registering product information is provided on the terminal.

[0714] Step 8:

[0715] The server collects and constantly monitors sales data for listed products in real time. It analyzes sales volume, sales amount, and consumer purchasing trends.

[0716] Step 9:

[0717] The server analyzes sales trends based on the collected sales data, generates feedback for the user regarding the success rate of promotions and areas for improvement, and notifies the user's device.

[0718] Step 10:

[0719] Users can review feedback via their devices and use it to inform future planning and sales strategies, thereby enabling continuous improvement.

[0720] Through these steps, the system enables efficient product sales tailored to consumer interests.

[0721] (Example 1)

[0722] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] In the online marketplace, quickly understanding consumer interests and developing effective product strategies is essential for increasing sales. However, existing systems have limited data acquisition and analysis capabilities, making it difficult to conduct effective marketing activities in a short period. In particular, there has been a lack of systems that can capture rapidly changing consumer trends and utilize them for sales promotion. This creates a challenge in that it increases the risk of missing potential sales opportunities.

[0724] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0725] In this invention, the server includes means for communicating via a network and acquiring string data from multiple information provision platforms on the web; means for normalizing the acquired string data, removing unnecessary elements, and analyzing it to identify data trends over a certain period of time; and means equipped with artificial intelligence that automatically generates project proposals and attractive expressions related to a specific theme based on the analysis. This makes it possible to quickly reflect consumer interests in product strategies, optimize marketing effectiveness, and increase sales.

[0726] An "information processing device" is an electronic device used to acquire, analyze, and process data, and can access external databases and systems via a network.

[0727] "String data" refers to a set of characters and numbers used to represent specific information, and is usually represented in text format.

[0728] "Normalization" is a process that unifies the format and structure of data, and is used to improve efficiency when performing data analysis.

[0729] "Unnecessary elements" are data or information that hinder the extraction of useful information during analysis or processing, and are therefore subject to filtering.

[0730] "Analysis" is the process of examining collected data from multiple perspectives and extracting useful information and insights from it.

[0731] "Artificial intelligence" is a form of computer system that has the function of automatically learning and making decisions based on data, and plays a role in streamlining generation and analysis.

[0732] A "project proposal" is a plan or proposal that outlines a concept for achieving a specific goal, and it is an important element in marketing and strategy planning.

[0733] "Expression" refers to a concrete form, such as text or images, used to convey a specific intention or meaning, and is used in communication.

[0734] A "communication network" is a collection of electrical or optical lines and related equipment for sending and receiving information, and is a means of transmitting data quickly and efficiently.

[0735] An "operation screen" is the interface used by users when operating a system or device, and serves as a point of contact for inputting instructions and confirming information.

[0736] An "e-commerce platform" is a foundational system for buying and selling goods and services online, serving as a platform that connects consumers and sellers.

[0737] "Sales information" refers to data related to the transaction of goods or services, including sales figures, sales quantities, and buyer demographics.

[0738] A "guideline" is something that indicates the direction or policy for achieving a specific goal, and serves as a standard for actions and decision-making.

[0739] This invention provides an information processing system for obtaining insights into consumer behavior in online markets. The system is primarily server-based. The server communicates via a network and retrieves string data from multiple information-providing platforms on the web. Specifically, it can periodically collect keyword data using APIs from search engines such as Google and Bing.

[0740] The server normalizes the collected string data, removes unnecessary elements, and then analyzes it. This utilizes data preprocessing techniques using programming languages ​​such as Python and R. The preprocessed data is then analyzed by an automated artificial intelligence (AI) and used as evidence to explore market trends and consumer interests. A commonly used generative AI model (e.g., GPT-3) can be employed as the AI ​​model.

[0741] The AI-generated project proposals and taglines are related to a specific theme and are sent to the device in an appealing format. The device user reviews these and makes adjustments or approvals as needed. This information supports product registration on e-commerce platforms, allowing for quick listing through a simplified interface.

[0742] For example, a server might acquire the trending keyword "summer festival," and an automatically generated artificial intelligence might suggest a "Cool Yukata Special." Based on this plan, users would then list related products, such as yukata or summer festival-related goods, on an e-commerce platform. By developing such strategies, companies can conduct timely marketing activities.

[0743] An example of the prompt text mentioned above would be: "Generate a catchy slogan and promotional ideas for the online store based on this month's trending keywords. Keywords: summer festival, yukata, merchandise." Based on this prompt text, the AI ​​model automatically generates effective expressions to appeal to consumers. In this way, the system quickly reflects consumer interest in product strategies and provides an effective means to improve sales.

[0744] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0745] Step 1:

[0746] The server communicates with APIs from multiple information provision platforms and periodically retrieves keyword data. This input consists of search queries and trend data, and the data is returned in JSON format. Specifically, it sends requests to search engine APIs and saves the returned responses to a database. The output is a collection of raw data.

[0747] Step 2:

[0748] The server normalizes the collected raw data. The input is the raw data obtained in step 1, and the normalization process removes special characters and unnecessary elements. Specifically, it uses regular expressions to clean the data and format it into the required format. The output is normalized, clean data.

[0749] Step 3:

[0750] The server analyzes the normalized data. This input is the clean data obtained in step 2, and natural language processing techniques are used to analyze trends and consumer interests over a specific period. Specifically, this includes identifying themes using topic modeling and aggregating keyword frequencies. The output is an analysis that provides insights into consumer behavior.

[0751] Step 4:

[0752] The server automatically generates project proposals and taglines using a generative AI model. The input is the analysis results obtained in step 3, and the server creates prompt sentences based on these results. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves strategic expressions suggested by the model. The output is the generated project proposal and tagline.

[0753] Step 5:

[0754] The server notifies the terminal of the generated project proposal and catchphrase via the network. This input is the content generated in step 4 and is converted to a format suitable for the user's terminal. Specifically, it sends push notifications via email or a dedicated application to quickly deliver information to the user. The output is the notification that the user receives.

[0755] Step 6:

[0756] The user reviews the project proposal notified via their device and registers the relevant product information on the e-commerce platform. This input is the project proposal received in step 5, and the user enters the product information using the provided operation screen. Specific actions include manual product registration and information entry using templates. The output is the product data registered on the platform.

[0757] Step 7:

[0758] The server monitors sales information for registered products in real time. This input consists of product data and sales information registered in step 6, tracking sales volume and consumer response. Specifically, it retrieves data from the sales platform via an API and visualizes it on an analytics dashboard. The output is the sales analysis results.

[0759] Step 8:

[0760] The server generates feedback for sales improvement based on sales analysis results and notifies the user. The input is the analysis data obtained in step 7, and it creates guidelines for the next sales strategy. Specific actions include suggesting action items using a recommendation system. The output is a feedback notification and improvement suggestions provided to the user.

[0761] (Application Example 1)

[0762] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0763] In online sales, it is difficult to quickly grasp consumer interests and implement effective marketing strategies in a timely manner. Therefore, there is a need for a system that can develop appropriate sales strategies tailored to customer interests and optimize sales.

[0764] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0765] In this invention, the server includes means for collecting keyword information from multiple search mechanisms on a network, means for analyzing the collected keyword information and identifying trends during a specified period, and means for generating a plan related to a theme using generative AI and creating a catchy slogan. This makes it possible to quickly and effectively formulate and execute dynamic sales strategies based on consumer interests.

[0766] A "network" is a system in which multiple computers and devices are connected to exchange information and communications.

[0767] A "search engine" is a system used to search for information on the internet, and it finds relevant information based on keywords entered by the user.

[0768] "Keyword information" refers to the main terms that consumers use when searching the internet, and it is information that indicates trends and consumer interests.

[0769] "Generative AI" is a type of artificial intelligence technology that automatically generates new information and ideas based on learned data.

[0770] A "plan" is a set of detailed guidelines or policies formulated to achieve a specific objective.

[0771] A "catchphrase" is a short, memorable phrase used to attract consumers' attention in order to promote a product or service.

[0772] A "sales platform" is an online or offline platform for providing goods or services.

[0773] An "interface" is a connection or procedure that enables the exchange of information between a user and a system.

[0774] "Sales figures" refer to data that shows the sales volume or revenue of a product over a specific period.

[0775] "Opinions" are improvement measures or suggestions presented based on analysis.

[0776] "Trends" refer to changes or trends in fashion under specific periods or conditions.

[0777] To realize the system of this invention, the server acquires information via a network and collects keyword information from a search mechanism. The server uses this information to analyze the data using natural language processing technology, specifically TensorFlow, and identifies trends. Based on the results of this analysis, the server uses generative AI to formulate a plan related to the theme and create a catchy slogan.

[0778] The server transmits the generated plan and catchphrase to the user's terminal via the network. Based on the received content, the terminal provides an interface for easily registering relevant product information on the online sales platform. This interface was developed using Flutter and is designed to be intuitive and easy for the user to operate.

[0779] Furthermore, the server collects sales figures for registered products in real time and performs immediate analysis. The results of the analysis are provided to the user as feedback for improving sales strategies. This allows users to constantly develop and implement sales strategies optimized for consumer needs.

[0780] As a concrete example, consider a scenario where a server detects the trend "Halloween" and creates related plan proposals. In this case, the generation AI suggests a "Special Feature on Original Halloween Goods" and generates a related tagline: "Check out the items that will make your costume shine now!" This makes it easier for users to register Halloween-related products on the platform and allows for the rapid creation of a special feature page.

[0781] An example of a prompt for the generation AI model would be: "Analyze the latest keyword data and generate compelling promotional taglines based on consumer insights into summer fashion."

[0782] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0783] Step 1:

[0784] The server collects keyword information from multiple search mechanisms on the network. It uses authentication information for accessing network APIs as input. The output is a list of collected keywords. This process involves sending API requests and analyzing the retrieved responses to extract the target keywords.

[0785] Step 2:

[0786] The server analyzes the collected keyword information to identify search trends over a specific period. The input is the keyword list obtained in step 1. The output generates trend data as a result of the analysis. Using natural language processing techniques, it analyzes the frequency and time-series changes of keywords and performs specific actions to extract topics of consumer interest.

[0787] Step 3:

[0788] The server uses a generative AI to create a plan proposal and catchphrase based on the theme. The input is the trend data obtained in step 2. The output is the generated plan proposal and catchphrase. Prompt text is input to the generative AI, which then automatically generates an appropriate promotional message.

[0789] Step 4:

[0790] The server sends the generated plan and catchphrase to the terminal via the network. The input is the plan and catchphrase generated in step 3. The output is what is displayed on the user's terminal. This includes receiving the transmitted data on the terminal and allowing the user to confirm it.

[0791] Step 5:

[0792] The terminal provides an interface to assist in registering product information to the online sales platform based on the plan received from the server. The inputs are the plan and catchphrase received in step 4. The output is a registration support interface. Using Flutter, it provides forms and input fields for user-friendly UI operation.

[0793] Step 6:

[0794] The server collects sales figures for registered products in real time and generates suggestions for improving sales. The input is the latest sales data obtained from the sales platform. The output is feedback information based on the analysis results. It monitors sales data and uses analysis to develop strategies for improving sales.

[0795] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0796] This invention integrates an emotion engine that understands user sentiment into a project proposal generation system based on the analysis of search keyword data, in order to enhance the effectiveness of product sales on online sales platforms. Specific embodiments of this system are described below.

[0797] First, the server collects keyword data from multiple search engines. This can be done using API-based data acquisition or web scraping techniques. The collected data is preprocessed on the server and then provided for analysis as a cleaned dataset.

[0798] In the analysis process, the server uses natural language processing technology to analyze the data and identify trending keywords and frequently searched terms relevant to the time period. This allows for the development of featured themes for products and services.

[0799] Next, the AI ​​on the server generates a marketing campaign plan and tagline based on past data and the results of the current analysis. During this process, the emotion engine analyzes the user's past feedback and behavioral data, customizing the content to take the user's emotional state into account. This enables more personalized suggestions.

[0800] The generated project proposals and taglines are sent to the user's device via the network. The device receives the notification and provides an interface that allows the user to review the project proposals and taglines. Based on this information, the user can register products or prepare special feature pages.

[0801] The product information selected by the user is registered from the terminal to the online sales platform. Subsequently, the server collects and monitors sales data for the listed products in real time. In addition, the emotion engine collects emotion data through user interactions and customer reviews, and generates feedback based on consumer emotions.

[0802] For example, if the server analyzes a trending theme like "Spring Picnic," it can suggest a relaxation-themed feature that takes into account the user's past purchase history and feedback. The emotion engine detects positive emotional responses to the "Picnic" theme and creates a promotional message that reflects them.

[0803] In this way, this system comprehensively utilizes search data and sentiment data to support the planning and implementation of more effective product sales strategies.

[0804] The following describes the processing flow.

[0805] Step 1:

[0806] The server uses search engine APIs and web scraping techniques to collect vast amounts of keyword data, including trending keywords and frequently occurring words. This data reflects the latest consumer interests.

[0807] Step 2:

[0808] The server cleans up the collected keyword data, removing unnecessary information. It also standardizes the data and prepares it for analysis. This improves the consistency and accuracy of the data.

[0809] Step 3:

[0810] The server applies natural language processing techniques to analyze keyword data. This analysis identifies trends and important themes that have attracted attention during a specific period.

[0811] Step 4:

[0812] The server's AI generates advertising and sales promotion plans based on the analysis results. It also references data from past success stories to formulate the most optimal content.

[0813] Step 5:

[0814] The emotion engine analyzes the user's past behavioral data and feedback to determine their emotional state. This information is used to personalize the generated project proposals and taglines.

[0815] Step 6:

[0816] The server sends the generated project proposal and tagline to the user's terminal via the network. The terminal displays an interface that allows the user to review the project content and make revisions as needed.

[0817] Step 7:

[0818] Users review the notified project proposal on their devices and revise it as needed. The revised project proposal is then sent back to the server as feedback.

[0819] Step 8:

[0820] Based on the project proposal approved or modified by the user, relevant product information is registered on the online sales platform via the terminal. The terminal interface provides support to simplify the registration process.

[0821] Step 9:

[0822] The server monitors sales data for listed products in real time. It also uses an emotion engine to collect customer reviews and feedback and analyze consumers' emotional states.

[0823] Step 10:

[0824] The server analyzes sales data and sentiment data to generate more effective feedback and improvement suggestions. This information is then communicated to the user's device and used to inform future sales strategies.

[0825] This entire process enables highly personalized product sales that incorporate consumer emotions and trends.

[0826] (Example 2)

[0827] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0828] In modern e-commerce, there is a demand for the rapid and accurate provision of product information that matches consumer needs. However, conventional systems build marketing campaigns using only the analysis of search keyword data, and do not fully utilize consumer sentiment or past behavior history. As a result, personalized marketing becomes difficult, and it is difficult to increase sales.

[0829] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0830] In this invention, the server includes means for collecting information through multiple data acquisition means, means for analyzing the collected information and identifying trends over a specific period, and means having artificial intelligence that generates a plan and creates a representation on a theme using the analysis results and sentiment analysis. This makes it possible to plan personalized marketing campaigns that take into account consumer emotions and behavior, thereby increasing sales.

[0831] "Data acquisition means" refers to a device or method used to collect information required by an information processing device, and has the function of efficiently acquiring information from various data sources.

[0832] "Analysis means" refers to an apparatus or method used to analyze collected information and identify specific trends or patterns.

[0833] "Artificial intelligence" refers to a computer system that can perform specific tasks through learning and reasoning, and that can make creative or logical decisions based on the data obtained in the process.

[0834] "Expression" refers to text or visual content generated based on analyzed information, intended to effectively convey a message in line with a specific theme or purpose.

[0835] A "communication network" is a network infrastructure used for sending and receiving information, providing the necessary connections for exchanging data between different terminals.

[0836] A "display device" is a device that allows a user to visually confirm information, and typically includes screens and monitors.

[0837] An "e-commerce platform" is an online marketplace where goods and services are bought and sold, and it plays a role in connecting sellers and buyers.

[0838] "Transaction data" refers to information related to purchase history and sales statistics recorded in e-commerce, and plays an important role in marketing and inventory management.

[0839] A "response" is a message or report that indicates conclusions or recommended actions derived from the data received by the system, and is provided to support the user's decision-making.

[0840] This invention is a system for maximizing the effectiveness of marketing activities on e-commerce platforms. Specific embodiments thereof are described below.

[0841] First, the server collects information from multiple data sources as a means of obtaining information. This process includes methods such as using search engine APIs to obtain data and utilizing web scraping techniques. The acquired information is organized on the server and then analyzed.

[0842] Next, the server uses natural language processing techniques to analyze the acquired data. This analysis can utilize libraries such as Python's NLTK or spaCy. This allows for the extraction of trends and patterns over a specific period, which can then be incorporated into marketing activity plans.

[0843] The AI ​​model on the server generates a plan related to the theme based on the analysis results, and simultaneously creates a representation. This process incorporates the analysis results of the user's past transaction data and sentiment data to help provide personalized marketing strategies.

[0844] The generated results are notified to the user's terminal via the communication network. The terminal, acting as a display device, presents this information to the user, allowing the user to evaluate the plan and its representation through an appropriate interface.

[0845] Users can register product information on the e-commerce platform based on the information they receive. The server then collects and analyzes transaction data for the registered products in real time. Based on the insights gained from this transaction data, it generates further responses, contributing to increased sales.

[0846] For example, if the server analyzes the theme "Spring Picnic," the generative AI model can propose a feature that emphasizes relaxation based on past consumer behavior and emotional data. It can also use emotional data to create expressions that highlight the positive image of a "picnic."

[0847] An example of a prompt is: "Analyze trending keywords related to spring picnics and generate a plan that takes user sentiment into account."

[0848] Thus, the system of the present invention aims to enhance competitiveness in e-commerce by comprehensively analyzing diverse information and providing personalized marketing strategies based on consumer emotions and behavior.

[0849] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0850] Step 1:

[0851] The server first uses information acquisition methods to collect necessary data via search engine APIs and web scraping techniques. The input consists of a list of target keywords and related URLs, and the server extracts keyword data and related information from the web based on this. The output is a cleaned dataset, with duplicates and noise removed, which is then passed on to the next analysis step.

[0852] Step 2:

[0853] The server applies natural language processing techniques to the obtained dataset to perform trend analysis. At this stage, Python's NLTK and spaCy are used to tokenize the data, tag parts of speech, and analyze sentence structure. The input is the dataset generated in the previous step, and the output is a list of trending keywords and frequently searched terms for each period. This allows for the extraction of specific trends and themes.

[0854] Step 3:

[0855] The AI ​​model generation step on the server is where a marketing plan is generated based on the analysis results. The inputs here are a list of trending keywords and historical consumer data. The AI ​​model combines these to generate a plan and tagline tailored to the target market. The output is a personalized marketing strategy that takes user emotions into consideration.

[0856] Step 4:

[0857] The generated plan and tagline are transmitted from the server to the user's terminal via the communication network. The terminal receives this data and presents the information through an interface that the user can view. The input is data from the server, and the output is visualized information displayed through the user-operated interface.

[0858] Step 5:

[0859] The user registers product information on the e-commerce platform based on information received via their device. The user's input process enters the product name, description, price, etc., into the platform, completing the registration. The output is a publicly available product page.

[0860] Step 6:

[0861] The server instantly collects and analyzes transaction data for registered products and generates responses to improve sales. This process involves evaluating transaction statistics and performing sentiment analysis of customer reviews using a sentiment engine. Inputs are real-time sales data and customer feedback, and outputs are feedback reports that include improvement suggestions and revisions to marketing strategies.

[0862] (Application Example 2)

[0863] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0864] Online sales platforms require sophisticated product sales strategies that respond to the diverse needs and emotional responses of consumers. While traditional sales systems can grasp broad trends through search keyword analysis, they have struggled to implement targeted marketing based on the emotions of individual consumers. Therefore, a method is needed to develop more effective and personalized sales strategies.

[0865] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0866] In this invention, the server is an information processing device for acquiring data, and includes means for collecting keyword information from multiple data collection systems on the web, means having a generative artificial intelligence that generates project proposals related to a theme and creates promotional messages based on the analysis results, and means having an emotion analysis mechanism for analyzing user emotion data and generating personalized advertising content. This enables the efficient generation of advertising content that is tailored to the individual needs and emotions of consumers.

[0867] An "information processing device" is used to collect keyword information from multiple data collection systems on the web.

[0868] "Keyword information" refers to information that indicates a specific theme or trend, obtained through users' search behavior.

[0869] "Generative artificial intelligence" is an artificial intelligence that has the function of generating project proposals related to a theme and creating promotional messages based on analysis results.

[0870] A "communication network" is a network used to notify users of generated project proposals and promotional messages to their electronic devices.

[0871] An "interface" is a system that provides a point of contact with the user to register relevant product information in the online sales system based on the content of the notification.

[0872] "Sales data" refers to data that includes sales information for registered products and is used to provide feedback for improving sales.

[0873] A "sentiment analysis mechanism" is a system that analyzes users' emotional data and has the function of generating personalized advertising content.

[0874] The system for implementing this invention is centered around a server that functions as an information processing device. The server acquires keyword information from multiple data collection systems on the internet for data collection. Web scraping libraries such as the Google Keyword Planner API and Beautiful Soup are used for this purpose. The acquired keyword information is analyzed using natural language processing techniques to identify search frequency and trends within a specific time frame. Libraries such as Spacy and NLTK are used for this analysis.

[0875] Based on the analysis results, generative artificial intelligence generates project proposals and promotional messages related to the theme. Here, generative AI models such as OpenAI's GPT are used, combining collected data with past sales performance data to provide optimal suggestions. During this process, user emotional data is collected through the user's electronic devices, and this data is analyzed by an emotional analysis mechanism. VADER and TextBlob are used specifically for emotional analysis.

[0876] A distinctive feature of this system is the advertising content that users are notified of. These notifications are sent via a communication network, and the user's device receives them, providing an interface for registering relevant product information in the online sales system. Through this process, advertising content optimized for the individual needs and emotions of consumers is generated.

[0877] For example, if the server analyzes trends related to "spring picnics," it will consider the user's past purchase history and sentiment data to generate a personalized message such as, "Why not refresh yourself with some new picnic gear?" An example of a prompt to the generating AI model would be, "We have determined that the user is interested in 'spring picnics' and has positive feelings towards them. Please generate advertising copy related to picnics."

[0878] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0879] Step 1:

[0880] The server retrieves keyword information from multiple data collection systems on the internet. It uses the Google Keyword Planner API and Beautiful Soup as input to collect the necessary keyword data. The collected data is then cleaned up and output with noise removed.

[0881] Step 2:

[0882] The server analyzes the collected keyword information using natural language processing techniques. Spacy and NLTK are used to tokenize, normalize, and extract nouns from the data. The input is the cleaned keyword information obtained in step 1, and the output is data that identifies trends and search frequencies within a specific time frame.

[0883] Step 3:

[0884] The server generates a project proposal and promotional message using a generative AI model based on the analysis results. During this process, it uses OpenAI's GPT to create prompt text, resulting in personalized advertising content. The input is the analysis data obtained in step 2, and the output is the generated project proposal and advertising message.

[0885] Step 4:

[0886] The user's device receives notifications from the server via the network and displays the generated project proposal and promotional message. The input is the advertising content generated in step 3, which is output as an interface that the user can view.

[0887] Step 5:

[0888] User emotion data is transmitted to the server via the terminal. This data is input from the user's terminal and analyzed by an emotion analysis mechanism on the server. Specifically, positive or negative emotions are evaluated using VADER and TextBlob, and the results are output.

[0889] Step 6:

[0890] The server optimizes product information for registration in the online sales system based on the user's emotional data and the generated project proposal. The input here is the emotional data from step 5 and the project proposal from step 3, and the output is optimized product registration information that takes these into account.

[0891] Step 7:

[0892] The user's device verifies the optimized product information and registers it in the online sales system as needed. The input is the optimized product information obtained in step 6, and the user's actions result in the product being registered on the online platform as the final output.

[0893] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0894] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0895] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0896] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0897] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0898] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0899] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0900] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0901] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0902] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0903] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0904] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0905] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0906] 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.

[0907] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0908] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0909] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0910] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0911] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0912] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0913] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0914] The following is further disclosed regarding the embodiments described above.

[0915] (Claim 1)

[0916] An information processing device for acquiring data, comprising means for collecting keyword data from multiple search engines on the web,

[0917] A means of analyzing collected keyword data to identify search frequency and trends over a specific period,

[0918] A means having a generative artificial intelligence that generates project proposals related to the theme and creates catchphrases based on the above analysis results,

[0919] A means of notifying the user's terminal of the generated project proposal and catchphrase via the network,

[0920] A means of providing an interface for registering relevant product information on an online sales platform based on the notification content,

[0921] A means of collecting sales data for registered products and providing feedback for sales improvement,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein the generating artificial intelligence has a function to optimize the project proposal by learning past sales performance data and identifying successful sales categories.

[0925] (Claim 3)

[0926] The system according to claim 1, wherein the aforementioned sales data is collected in real time and analysis is performed to dynamically adjust the featured content and promotional activities.

[0927] "Example 1"

[0928] (Claim 1)

[0929] An information processing device comprising means for communicating via a network and obtaining string data from multiple information provision platforms on the web,

[0930] A means for normalizing acquired string data, removing unnecessary elements, and analyzing it to identify data trends over a certain period of time,

[0931] A means equipped with artificial intelligence that automatically generates project proposals and attractive expressions related to a specific theme based on analysis,

[0932] A means for transmitting the generated project proposal and attractive presentation to the user's terminal via a communication network,

[0933] A means for providing an operation screen for registering relevant product information on an e-commerce platform based on the transmitted content,

[0934] A means of obtaining sales information for registered products and generating guidelines for improving sales,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, wherein the artificial intelligence has a function to learn past sales history data and optimize the project proposal while identifying successful sales categories.

[0938] (Claim 3)

[0939] The system according to claim 1, wherein the aforementioned sales information is acquired immediately, and analysis is performed to dynamically adjust the featured content and sales promotion activities.

[0940] "Application Example 1"

[0941] (Claim 1)

[0942] An information processing device for acquiring data, comprising means for collecting keyword information from multiple search mechanisms on a network,

[0943] A means for analyzing collected keyword information to identify the frequency and trends of searches during a specified period,

[0944] A means having a generative AI that generates a plan proposal related to the theme and creates a catchy slogan based on the above analysis results,

[0945] A means of notifying the user's terminal of the generated plan and catchphrase via the network,

[0946] A means for providing an input device for registering relevant product information on an online sales platform based on the content of the notification,

[0947] A means of collecting sales figures for registered products and providing feedback for increasing sales,

[0948] A means for presenting a draft plan generated on the user's terminal and providing an interface that allows the user to modify or approve its contents,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, wherein the generating AI has a function to optimize the plan by learning past sales performance information and identifying successful sales areas.

[0952] (Claim 3)

[0953] The system according to claim 1, wherein the aforementioned sales figures are collected in real time and analysis is performed to dynamically adjust the content of special features and planning activities.

[0954] "Example 2 of combining an emotion engine"

[0955] (Claim 1)

[0956] An information processing device for acquiring data, comprising means for collecting information through a plurality of data acquisition means,

[0957] A means of analyzing collected information and identifying trends over a specific period,

[0958] A means having artificial intelligence that generates a plan and creates an expression related to a theme using analysis results and sentiment analysis,

[0959] Means for notifying the user's display device of the generated plan and representation via a communication network,

[0960] A means to provide a mechanism for registering relevant product information on an e-commerce platform based on the notification,

[0961] A means of collecting transaction data for registered products and providing responses to improve performance,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, wherein the artificial intelligence has a function to optimize the plan by learning past transaction data and identifying successful transaction categories.

[0965] (Claim 3)

[0966] The system according to claim 1, wherein the aforementioned transaction data is collected immediately and analysis is performed to dynamically adjust the content of the plan and promotional activities.

[0967] "Application example 2 when combining with an emotional engine"

[0968] (Claim 1)

[0969] An information processing device for acquiring data, comprising means for collecting keyword information from multiple data collection systems on the web,

[0970] A means of analyzing collected keyword information to identify search frequency and trends within a specific time frame,

[0971] A means having a generative artificial intelligence that generates project proposals related to a theme based on analysis results and creates promotional messages,

[0972] A means for notifying users of their electronic devices via a communication network of the generated project proposal and promotional message,

[0973] A means of providing an interface for registering relevant product information in an online sales system based on the content of the notification,

[0974] A means of collecting sales data from registered product information and providing feedback to improve sales,

[0975] A means having an emotion analysis mechanism for analyzing user emotion data and generating personalized advertising content,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, wherein the generating artificial intelligence and emotion analysis mechanism have a function to optimize the project proposal by learning past sales history data and user emotional response data, and identifying successful sales categories and positive emotional responses.

[0979] (Claim 3)

[0980] The system according to claim 1, wherein the aforementioned sales data is collected in real time, analysis is performed to dynamically adjust the featured content and advertising activities, and the sentiment analysis mechanism is enabled to improve promotional messages based on real-time sentiment data. [Explanation of symbols]

[0981] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information processing device for acquiring data, comprising means for collecting keyword data from multiple search engines on the web, A means of analyzing collected keyword data to identify search frequency and trends over a specific period, A means having a generative artificial intelligence that generates project proposals related to the theme and creates catchphrases based on the above analysis results, A means of notifying the user's terminal of the generated project proposal and catchphrase via the network, A means of providing an interface for registering relevant product information on an online sales platform based on the notification content, A means of collecting sales data for registered products and providing feedback for sales improvement, A system that includes this.

2. The system according to claim 1, wherein the generating artificial intelligence has a function to optimize the project proposal by learning past sales performance data and identifying successful sales categories.

3. The system according to claim 1, wherein the aforementioned sales data is collected in real time and analysis is performed to dynamically adjust the featured content and promotional activities.

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