system

Abstract

Provide a system. Solution: A means of collecting data on the business of the client, information on competitors, and data on the company's own products; A means of analyzing the collected data and conducting keyword and competitive comparisons; A means for automatically generating banner advertising content based on the analysis results; A means of recommending optimal ad delivery destinations, A means of collecting advertising performance data and analyzing its effectiveness; A means of automatically generating proposal materials and approaches for the target audience based on the analyzed results. A system including:

Description

[Technical Field]

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

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

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

[0004] As the internet advertising market grows rapidly, many companies are seeking efficient and effective advertising strategies. However, the current situation is that advertising tasks, including creating advertising content, selecting optimal ad distribution destinations, analyzing advertising effectiveness, narrowing down target audiences, and creating proposal materials, all require a high level of specialized knowledge and a significant amount of time. This makes it difficult for small companies and companies with limited marketing resources to achieve sufficient advertising effectiveness and maintain a competitive edge over their rivals. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following: a system including: means for collecting data on clients' business activities, information on competitors, and the company's own products; means for analyzing the collected data and performing keyword and competitor comparisons; means for automatically generating banner advertising content based on the analysis results; means for recommending optimal ad distribution destinations; means for collecting advertising performance data and analyzing its effectiveness; and means for automatically generating proposal materials and approach methods for target demographics based on the analyzed effectiveness results. This system allows companies to streamline their advertising operations and conduct effective advertising activities even with limited resources.

[0006] "Proposal recipient" refers to the company or organization that is the target of marketing activities in an advertising project.

[0007] "Business activities" refers to the totality of the work and business activities conducted by a company or organization.

[0008] "Competitors" refer to other companies that offer similar products or services to the target company in a particular market or industry and are in competition with the target company.

[0009] "In-house products" refer to products and services developed and provided by the company itself.

[0010] "Data collection methods" refers to the methods and techniques used to collect information about the target company or market.

[0011] "Analytics" refers to the technologies and algorithms used to analyze collected data and extract meaningful information and insights.

[0012] "Means for automatically generating advertising content" refers to technology that automatically creates advertising materials based on analysis results.

[0013] "Advertising delivery destination recommendation method" refers to technology that selects and recommends the most suitable advertising platform.

[0014] "Performance data" refers to indicators that show the effectiveness of an advertisement, such as the number of ad views, number of clicks, and conversion rate.

[0015] "Effectiveness analysis means" refers to technology that analyzes collected performance data and evaluates the efficiency and effectiveness of advertising.

[0016] A "proposal document" refers to an explanatory document submitted to target companies or customers in marketing or sales activities.

[0017] "Approach method" refers to the marketing and sales techniques used toward target companies and customers. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention provides an advertising sales tool designed to improve the efficiency and effectiveness of Internet advertising operations. Specifically, the system collects data on clients' business activities, competitors, and the company's own products, analyzes this data, automatically generates advertising content, recommends optimal ad delivery destinations, and analyzes the effectiveness of advertising.

[0040] Program processing

[0041] Data Collection Process

[0042] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[0043] Examples:

[0044] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[0045] Data Analysis Process

[0046] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0047] Examples:

[0048] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0049] Automated advertising content creation process

[0050] Based on the analysis results, the server automatically generates advertising content, specifically by selecting appropriate keywords and creating banner ads based on templates.

[0051] Examples:

[0052] The server generates a banner ad containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[0053] Advertising destination recommendation process

[0054] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0055] Examples:

[0056] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[0057] Effects analysis process

[0058] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0059] Examples:

[0060] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0061] Automatic generation process for proposal materials and approach methods

[0062] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0063] Examples:

[0064] Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, and will propose an approach that combines social media campaigns and email marketing.

[0065] In this way, the system of the present invention highly automates all advertising operations, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, in order to maximize advertising effectiveness.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user enters the name of the proposed company into the interface and sends an information gathering request.

[0069] For example, enter "Company A" and press the Start Collection button.

[0070] Step 2:

[0071] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[0072] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[0073] Step 3:

[0074] The server retrieves competitor information from external databases and market reports via API.

[0075] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[0076] Step 4:

[0077] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[0078] For example, obtain detailed specifications and past sales data for your company's "Product X."

[0079] Step 5:

[0080] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[0081] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[0082] Step 6:

[0083] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[0084] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[0085] Step 7:

[0086] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[0087] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[0088] Step 8:

[0089] The server selects keywords to be used for banner ads based on the results of data analysis.

[0090] For example, identify the main message to use in your banner: "Experience our quality products now!"

[0091] Step 9:

[0092] The server selects an appropriate design from pre-designed ad templates.

[0093] For example, choose a simple and elegant design to emphasize high quality.

[0094] Step 10:

[0095] The server combines the selected keywords with templates to automatically generate banner ads.

[0096] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[0097] Step 11:

[0098] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[0099] For example, determine whether LINE user engagement is high in a particular industry.

[0100] Step 12:

[0101] The server selects the most suitable advertising platform and recommends it to the device.

[0102] For example, we recommend that Company A distribute advertisements via LINE.

[0103] Step 13:

[0104] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[0105] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[0106] Step 14:

[0107] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[0108] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[0109] Step 15:

[0110] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[0111] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0112] Example 1

[0113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0114] In conventional internet advertising, the processes of ad proposal, creation, distribution, effectiveness analysis, and optimization are all performed separately, resulting in inefficiency and requiring a lot of time and effort. Furthermore, there are issues with the accuracy of data collection and analysis, and the appropriateness of ad distribution, making it difficult to maximize advertising effectiveness. To solve these problems, a highly automated system that centralizes all advertising operations is required.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0116] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating advertising content based on the analysis results, means for analyzing past advertising performance data and the latest market data and recommending optimal advertising distribution destinations, means for collecting performance data from advertising platforms during the advertising campaign period and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results. This makes it possible to centrally and efficiently execute the entire process from data collection to analysis, advertising creation, distribution, analysis, and proposals.

[0117] "Business details of the proposed company" refers to information about the business, services, and products that a specific company or organization primarily conducts.

[0118] "Competitor information" is data about other companies or entities operating in the same market or industry as the proposal recipient.

[0119] "Data on your company's products" refers to detailed information about the products and services your company offers.

[0120] "Means of data collection" refers to the technologies or tools used to collect specific information, including, for example, web scraping technologies and APIs.

[0121] "Analyzing the data" is the process of examining the collected information in detail to gain new knowledge and insights.

[0122] "Keyword and Competitive Comparison Tools" are methods for identifying key words and phrases based on collected data and analyzing them against competitors.

[0123] "Means for automatically generating advertising content" refers to technology or software that automatically creates advertising content such as images, text, and videos based on analysis results.

[0124] "Historical advertising performance data" refers to data showing the results of advertising campaigns that have been run to date, including clicks, conversion rates, and engagement.

[0125] "Latest market data" means the latest information on current market movements and trends.

[0126] "Means for recommending optimal ad delivery destinations" refers to technologies and methods that, based on analyzed data, suggest to users the optimal platforms and media for effectively delivering advertisements.

[0127] "Collecting performance data from advertising platforms during the advertising campaign" refers to the process of obtaining data regarding the performance of the advertisement from each advertising platform during the period the advertisement is running.

[0128] "Means for analyzing effectiveness" means a method for analyzing collected advertising performance data and evaluating the success of the advertising.

[0129] "Analyzed effectiveness results" are the specific results and evaluation results obtained after analyzing advertising performance data.

[0130] "Means for automatically generating proposal materials and approaches for target demographics" refers to technology or software that automatically creates proposal materials and specific marketing approaches suitable for a specific target demographic based on the analyzed results.

[0131] The present invention is a system configured to provide an advertising sales tool and to improve the efficiency and effectiveness of Internet advertising operations. Specific program processing and embodiments thereof will be described in detail below.

[0132] This system involves a series of processes: collecting data on the client's business, information on competitors, and the company's own products, analyzing this data to automatically generate advertising content, recommending optimal ad distribution destinations, and analyzing the effectiveness of the advertising. Based on the results of the effectiveness analysis, it then automatically generates proposal materials and specific approach methods optimized for the target demographic.

[0133] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[0134] As a concrete example, when a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also obtains market data and customer reviews for competitors "Company B" and "Company C" from an external database. It also obtains detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[0135] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0136] As a concrete example, the server extracts keywords such as "high quality" and "reliability" from Company A's business activities, and analyzes that Company B has an advantage in "price competitiveness," while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0137] The server automatically generates advertising content based on the analysis results. Specifically, it selects appropriate keywords and creates banner ads based on templates.

[0138] As a specific example, the server generates a banner advertisement containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[0139] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0140] As a specific example, the server determines from past advertising data that LINE user engagement is high in a specific industry and recommends that Company A distribute advertisements via LINE.

[0141] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0142] As a specific example, the server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0143] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0144] As a concrete example, Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0145] To demonstrate the features of this system, a generative AI model is used to generate advertising content. Below are examples of prompts that users can use to instruct the generative AI model:

[0146] Example prompt sentence:

[0147] Create an advertising banner to showcase Company A's high-quality products. Focus on the following points:

[0148] Reliability

[0149] high performance

[0150] Comprehensive after-sales service

[0151] Company A's logo should also be included in the banner.

[0152] In this way, the system according to the present invention centrally and efficiently executes the entire process from data collection and analysis, to advertisement creation, distribution, analysis, and proposals, aiming to improve the efficiency and effectiveness of advertising operations.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1: Enter the name of the company you are proposing to

[0155] The user accesses the system interface and inputs the name of the company to which the proposal is to be made.

[0156] Specific operation: The user enters "Company A" in the input field and clicks the Start Data Collection button.

[0157] Input: Name of the proposed company (e.g. Company A)

[0158] Output: Send the name of the proposed company to the server

[0159] Step 2: Start collecting data

[0160] The server starts collecting data based on the name of the proposed company received from the user.

[0161] Specific operation:

[0162] 1. Official website scraping: Access the official website of Company A and collect “Business Overview”, “Product Information”, and “Latest News”.

[0163] 2. Obtaining data from external databases: Use APIs to obtain market data and customer reviews from competitors "Company B" and "Company C."

[0164] 3. Collect information on your company's products from an internal database: Access the database within the system to obtain detailed information on your company's "Product X" and data on past advertising campaigns.

[0165] Input: Name of the proposed company (e.g. Company A), API of external database, query of internal database

[0166] Output: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[0167] Step 3: Data analysis

[0168] The server analyzes the collected data and extracts key keywords, competitive comparisons, and the benefits of the company's products.

[0169] Specific operation:

[0170] 1. Keyword extraction using NLP technology: Extract keywords such as "high quality" and "reliability" from Company A's business activities.

[0171] 2. Conduct competitive comparisons: Compare data from companies B and C with company A's data to identify competitive advantages.

[0172] 3. Analysis of the benefits of your company's products: Analyze how your company's product X's "high performance" and "excellent after-sales service" will work to your advantage in Company A's market.

[0173] Input: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[0174] Output: Keyword list, competitor comparison results, merit list of your company's products

[0175] Step 4: Automatic generation of advertising content

[0176] The server automatically generates advertising content based on the analysis results.

[0177] Specific operation:

[0178] 1. Keyword selection: Select selling points such as "high-quality products" from the analysis data.

[0179] 2. Place on banner template: Insert the message "Experience our high-quality products now!" into the template and place Company A's logo.

[0180] 3. Check the output: Create a preview of the ad banner and check the consistency of the text and design.

[0181] Input: Keyword list, template, company logo

[0182] Output: Advertising banner

[0183] Step 5: Recommend ad destinations

[0184] The server analyzes past advertising performance data and the latest market data to recommend the optimal advertising destinations.

[0185] Specific operation:

[0186] 1. Performance data collection: Extract past advertising data from the database.

[0187] 2. Analysis using machine learning models: Using machine learning models, we identify platforms (e.g., LINE) with high advertising engagement in specific industries.

[0188] 3. Proposal for ad distribution destination: Recommend ad distribution via LINE as the optimal ad distribution destination for Company A.

[0189] Input: Historical advertising performance data, latest market data

[0190] Output: Optimal ad distribution platform (e.g. LINE)

[0191] Step 6: Effectiveness analysis

[0192] The server collects performance data from the advertising platform during the advertising campaign and analyzes the effectiveness.

[0193] Specific operation:

[0194] 1. Use of API: Obtain click counts, conversion rates, and target demographic attribute data from advertising platforms (e.g., LINE API).

[0195] 2. Data aggregation and analysis: The acquired data is aggregated to derive high engagement among the target demographic (e.g., women aged 25-35).

[0196] 3. Visualization of results: Visualize the analysis results in graphs and charts to make them easy for users to understand.

[0197] Input: Ad performance data

[0198] Output: Effectiveness analysis results (e.g., high engagement among women aged 25-35)

[0199] Step 7: Automatic generation of proposal materials and approaches

[0200] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approach methods optimized for the target audience and provides them to the user.

[0201] Specific operation:

[0202] 1. Creating presentation materials: Automatically generate presentation materials that demonstrate marketing strategies targeting women aged 25-35.

[0203] 2. Proposal of approach method: Include in the proposal a specific approach method that combines social media campaigns and email marketing.

[0204] 3. Preview the final document: Review the generated proposal and make any necessary adjustments.

[0205] Input: Effect analysis results, template

[0206] Output: Proposal materials and specific approach

[0207] In this way, this system efficiently automates a series of processes, from detailed data collection and analysis, to ad generation, distribution recommendations, effectiveness analysis, and proposal document creation, and provides them to users.

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] In conventional internet advertising, business analysis, creation of advertising content, selection of distribution destinations, and effectiveness analysis are all done manually, which is time-consuming, labor-intensive, and inefficient.In addition, it is difficult to quickly make presentations and advertising proposals to client companies on-site, which places a heavy burden on sales representatives.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes means for collecting data on the business details of the proposal recipient company, information on competing companies, and data on the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating banner advertising content based on the analysis results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results, and means for displaying proposal recipient company information, advertising content previews, recommended advertising destinations, and success rates using a portable display device. This automates the entire process from data collection and analysis, advertising content generation, advertising delivery recommendations, effectiveness analysis, and proposal material creation, and makes it possible to display related information on-site in real time.

[0213] "Business details of the proposed company" refers to basic information and activities of the business being proposed to.

[0214] "Competitor information" refers to information about other companies operating in the same market, including competitor product information, strategies, strengths and weaknesses.

[0215] "Data on our own products" refers to detailed information about the products and services we handle, including product features, prices, and past sales data.

[0216] "Collection methods" refers to the methods and tools used to obtain the required data from the internet or other sources, and may include web scraping techniques and APIs.

[0217] "Means of analyzing data" refers to methods and technologies for understanding and analyzing collected information, such as natural language processing technology and machine learning models.

[0218] "Means for automatically generating banner advertising content" refers to technology for automatically generating advertising banners based on collected and analyzed data.

[0219] "Means for recommending ad delivery destinations" refers to methods and tools for selecting and proposing optimal ad delivery destinations, including machine learning models that use past ad performance data and market trends.

[0220] "Performance data" refers to metrics and data used to measure the effectiveness of advertising campaigns, such as the number of ad clicks and conversion rates.

[0221] "Means for automatically generating proposal materials and approaches" refers to technology that automatically generates proposal materials and approaches optimized for the target audience based on the analysis results.

[0222] "Portable display device" refers to a portable display device, such as smart glasses.

[0223] "Ad content preview" refers to the function or process that allows you to check the content of the advertisement that will actually be displayed in advance.

[0224] "Probability of success" refers to a statistical metric used to indicate the likelihood of success of a proposal or advertising campaign.

[0225] The present invention provides a system for supporting advertising sales, and a specific embodiment thereof will be described. This system uses a portable display device to present various data in real time, enabling effective advertising proposals. Here, the description will focus on an embodiment using smart glasses.

[0226] First, the server collects data on the client company's business, competitors, and the client's products. Data collection is done using web scraping technology and data acquisition via API. Specific tools used include BeautifulSoup and Scrapy.

[0227] The collected data is then analyzed on a server. This analysis uses natural language processing (NLP) technology to extract keywords and information for competitive comparison. Software libraries used include spaCy and NLTK. Based on the analysis, the company's strengths and weaknesses, as well as the competitive advantages of its products, are revealed.

[0228] Based on the results of this analysis, the server automatically generates banner ad content. An ad template engine is used to generate the ads. Specifically, a template engine such as Jinja2 is used. For example, a banner ad containing the message "Experience our high-quality products now!" is generated and placed alongside the logo of the proposed company.

[0229] The server then uses a machine learning model to recommend the optimal ad delivery destination. By analyzing past ad performance data and market trends, it proposes the optimal platform and media. Specifically, it often uses Tensorflow (registered trademark) or PyTorch, which are built on Python. For example, it determines "platforms with high user engagement in specific industries" and recommends them to the proposed destination.

[0230] During the advertising campaign, the server collects and analyzes advertising performance data. This data is obtained via the advertising platform's API (e.g., LINE API). The server analyzes the number of clicks, conversion rate, target demographic attribute data, etc. to derive an effective advertising strategy.

[0231] Finally, based on the effectiveness results, the server automatically generates proposal materials and specific approaches for the target demographic. For example, it creates presentation materials proposing social media campaigns and email marketing as marketing strategies targeting women aged 25-35.

[0232] To support this process, smart glasses are used as portable display devices. The advertising assistant app installed on the smart glasses displays information about the companies being proposed to, previews of advertising content, recommended ad distribution destinations, and success rates in real time. Users can conduct effective sales activities while checking the displayed visual information.

[0233] For example, if a user targets "Company D," the smart glasses will display the following information:

[0234] 1. Company information such as "Company D: Providing high-quality food"

[0235] 2. Preview of the banner ad "Fresh Vegetable Campaign!"

[0236] 3. Recommendations for advertising outlets that say "Instagram advertising is effective"

[0237] 4. Presenting the probability of success: "This proposal has an 85% chance of success."

[0238] An example prompt might look like this:

[0239] "Please create a proposal for Company D. Company D provides high-quality food products and its main competitors are Companies E and F. Company D's main product is fresh vegetables, and past advertising campaigns have been successful on Instagram."

[0240] In this way, the system of the present invention automates the entire process from data collection and analysis, to generating advertising content, recommending advertising delivery, analyzing effectiveness, and creating proposal materials, allowing sales representatives to make advertising proposals efficiently.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] The server receives the name of the proposed company. The input here is the name of the proposed company specified by the user, and the server starts collecting data based on this.

[0244] Specifically, when a user enters the name of "Company A" into the system, the server uses web scraping technology (e.g., BeautifulSoup) and APIs (e.g., Scrapy) to obtain data on Company A's business activities, information on competitors, and its own products from the company's official website and external databases.

[0245] Step 2:

[0246] The server stores the collected data and prepares it for analysis. The input data is the collected business details, information on competitors, and data on the company's own products.

[0247] Specifically, the server stores data retrieved from web pages and APIs in a database and uses NLP technology to convert it into an analyzable format.

[0248] Step 3:

[0249] The server analyzes the data. The input here is the saved data, and the output is the analysis results (keywords, competitor comparisons, strengths of the company's products, etc.).

[0250] Specifically, the server uses a natural language processing library (such as spaCy or NLTK) to extract the key keywords of the proposed company and its strengths and weaknesses compared to its competitors.

[0251] Step 4:

[0252] The server automatically generates advertising content based on the analysis results. The input is the analysis results, and the output is the generated banner advertising content.

[0253] Specifically, the server uses an advertising template engine (such as Jinja2) to generate a banner ad that reflects the keywords. For example, it creates a banner that includes the message "Experience our high-quality products now!"

[0254] Step 5:

[0255] The server recommends the optimal ad delivery destination. The input is the analysis results and past ad performance data, and the output is the recommended ad delivery destination.

[0256] Specifically, the server uses machine learning models (such as TensorFlow or PyTorch) to predict the optimal distribution destination based on past data, suggesting, for example, "platforms with high user engagement in specific industries."

[0257] Step 6:

[0258] The server collects and analyzes performance data for the advertising campaign, with the input being the data collected during the advertising campaign and the output being the analysis results.

[0259] Specifically, the server collects data such as click counts and conversion rates from the advertising platform (e.g., the LINE API), analyzes it, and identifies the attributes of the target demographic and the effectiveness of the advertisement.

[0260] Step 7:

[0261] The server automatically generates proposal materials and approaches for the target audience based on the analysis results. The input is the results of the effectiveness analysis, and the output is the generated proposal materials and approaches.

[0262] Specifically, the server generates presentation materials showing the optimal marketing strategy for the target audience, proposing a strategy that combines, for example, a social media campaign and email marketing.

[0263] Step 8:

[0264] The device (smart glasses) displays to the user information about the proposed company, a preview of the advertising content, recommendations for ad delivery destinations, and the probability of success. The input is data sent from the server, and the output is the information displayed on the glasses' display.

[0265] Specifically, the advertising assistant app installed on the device presents company information and ad previews as visual information, which the user can use to conduct sales activities.

[0266] The above steps enable a complete process from data collection and analysis, to generating advertising content, recommending distribution, analyzing effectiveness, creating proposal materials, and displaying the results in real time on-site.

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] This invention provides a system that recognizes user emotions and automatically generates customized advertising content based on them by combining an emotion engine with an advertising sales tool. This system realizes more effective advertising by incorporating user emotional information in the process of collecting and analyzing data on the client's business, competitors, and the company's own products.

[0269] Program processing

[0270] Data Collection Process

[0271] The server receives an information gathering request by having the user input the name of the proposed company into the interface, and then collects data on the company's business operations, competitors, and products from the company's official website, external databases, market reports, etc. The data is obtained using web scraping technology and APIs.

[0272] Examples:

[0273] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[0274] Data Analysis Process

[0275] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0276] Examples:

[0277] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0278] User emotion recognition process by emotion engine

[0279] The server uses an emotion engine to recognize the user's emotions in real time based on the information provided by the user, thereby understanding the user's current psychological state and reflecting it in the analysis data.

[0280] Examples:

[0281] As users set up their ad campaigns, the emotion engine detects tension or excitement from their facial expressions and tone of voice, and if the user feels stressed, the system will recommend easier, more intuitive actions.

[0282] Automated advertising content creation process

[0283] Based on the analysis results and the recognition results of the emotion engine, the server automatically generates advertising content. Specifically, it selects appropriate keywords and creates banner ads based on templates. The tone and message of the ad are also adjusted according to the user's emotions.

[0284] Examples:

[0285] The server generates a banner ad with the message "Experience our quality products now!" and places it alongside the logo of Company A. If the user is excited, it uses more powerful fonts and colors.

[0286] Advertising destination recommendation process

[0287] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0288] Examples:

[0289] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[0290] Effects analysis process

[0291] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0292] Examples:

[0293] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0294] Automatic generation process for proposal materials and approach methods

[0295] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and specific approaches optimized for the target demographic and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0296] Examples:

[0297] Sarver creates a presentation that outlines a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users are nervous, he uses visually appealing infographics.

[0298] In this way, the system of the present invention automates processes that incorporate user emotional information, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, thereby realizing more efficient advertising operations and more effective marketing.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The user enters the name of the proposed company into the interface and sends an information gathering request.

[0302] For example, enter "Company A" and press the Start Collection button.

[0303] Step 2:

[0304] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[0305] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[0306] Step 3:

[0307] The server retrieves competitor information from external databases and market reports via API.

[0308] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[0309] Step 4:

[0310] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[0311] For example, obtain detailed specifications and past sales data for your company's "Product X."

[0312] Step 5:

[0313] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[0314] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[0315] Step 6:

[0316] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[0317] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[0318] Step 7:

[0319] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[0320] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[0321] Step 8:

[0322] The server uses an emotion engine to recognize the user's emotions in real time.

[0323] For example, emotions are detected from the user's facial expressions and tone of voice.

[0324] Step 9:

[0325] The server selects keywords to be used for banner ads based on the results of data analysis and user emotional data.

[0326] For example, we recommend the message, "Experience our high-quality products now!"

[0327] Step 10:

[0328] The server selects an appropriate design from pre-designed ad templates.

[0329] For example, choose a simple and elegant design to emphasize high quality.

[0330] Step 11:

[0331] The server combines the selected keywords with templates to automatically generate banner ads.

[0332] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[0333] Step 12:

[0334] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[0335] For example, determine whether LINE user engagement is high in a particular industry.

[0336] Step 13:

[0337] The server selects the most suitable advertising platform and recommends it to the device.

[0338] For example, we recommend that Company A distribute advertisements via LINE.

[0339] Step 14:

[0340] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[0341] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[0342] Step 15:

[0343] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[0344] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[0345] Step 16:

[0346] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[0347] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0348] Example 2

[0349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] Conventional advertising sales tools do not take user emotions into account when collecting and analyzing client company information and competitor data, which means that advertising content is not effectively targeted. This results in poor advertising performance and limited effectiveness of marketing strategies.

[0351] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the business activities of proposal recipients, information on competing companies, and the company's own products, means for analyzing the collected data and performing keyword and competitive comparisons, means for recognizing user emotions in real time and reflecting them in the analysis results, means for automatically generating advertising content based on the analysis results and emotion recognition data, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results and emotion recognition data. This makes it possible to generate effective advertising content that incorporates user emotional information and optimize marketing strategies.

[0352] "Business details of the proposed company" is an outline of the main business and activities of the company that will be collected and analyzed.

[0353] "Competitor information" is data about other companies competing in the same market as the company to which the proposal is being made.

[0354] "Data on our own products" refers to information about the products and services offered by the company making the proposal.

[0355] "User emotion" refers to the psychological state of the user as recognized from the facial expression, tone of voice, etc. of the user using the system.

[0356] "Analysis results" refer to the results of analyzing collected data using natural language processing technology, etc.

[0357] "Advertising content" refers to promotional text, images, banners, etc. that are generated based on the analysis results.

[0358] "Advertising destination" refers to the medium or platform on which the generated advertisement is displayed.

[0359] "Performance data" refers to data that indicates the effectiveness of an advertisement, such as the number of clicks and conversion rate after the advertisement is delivered.

[0360] "Proposal materials" are presentation materials and reports created based on marketing strategies and advertising content.

[0361] "Approach" refers to the optimal marketing strategy or method for the target audience.

[0362] "Web scraping technology" is a technology that automatically obtains data from websites.

[0363] An "API" is an interface that allows different software systems to communicate with each other.

[0364] "Natural language processing technology" is a technology that allows computers to understand and analyze text written in natural language.

[0365] "Emotion recognition means" refers to a technique or device for recognizing a user's emotions from facial expressions, tone of voice, and the like.

[0366] "Real-time" refers to processing occurring immediately after a user action is taken.

[0367] The present invention relates to a system that automatically generates customized advertising content by incorporating user emotional information during the process of collecting and analyzing data on the client's business, competitors, and the company's own products. To specifically implement this system, the following methods and processes are used.

[0368] Hardware and software used

[0369] server:

[0370] You need a server that can run web scraping technologies and APIs to gather information from multiple data sources (official websites, external databases, market reports).

[0371] A server equipped with a high-performance processor and GPU is also required to run the emotion engine.

[0372] Device:

[0373] A personal computer or tablet device that can run an interface (web browser or dedicated application) for users to input data is required.

[0374] A device equipped with a camera and microphone is required, and real-time emotional recognition of the user's facial expressions and voice is required.

[0375] Main technologies used:

[0376] Web scraping technologies (e.g., Beautiful Soup, Selenium)

[0377] API (e.g. RESTful API)

[0378] Natural language processing technology (e.g., Python's NLTK, SpaCy)

[0379] Emotion recognition technology (e.g. OpenCV, TensorFlow)

[0380] Machine learning models (e.g., Scikit-learn, TensorFlow)

[0381] System operation process

[0382] Data collection process:

[0383] When the server receives the name of the company from the user, it collects data on the company's business, competitors, and products from the company's official website, external databases, and market reports, using web scraping technology and APIs.

[0384] Examples:

[0385] When a user specifies "Company A," the server collects "Business Overview," "Product Information," and "Latest News" from Company A's official website. It also collects market data and customer reviews for competitors "Company B" and "Company C." It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[0386] Data analysis process:

[0387] The data collected by the server is analyzed using natural language processing technology, automatically extracting key keywords such as the client company's business, competitor comparisons, and the benefits of the company's products.

[0388] Examples:

[0389] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness," but Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0390] Emotion Recognition Process:

[0391] Using the emotion engine, the server analyzes the user's facial expressions and tone of voice to recognize emotions in real time, allowing it to understand the user's current psychological state and reflect it in data analysis.

[0392] Examples:

[0393] As users set up their ad campaigns, an emotion engine detects tension or excitement from their facial expressions and tone of voice. If they are feeling stressed, the system recommends easier, more intuitive actions.

[0394] Advertising content generation process:

[0395] Based on the analysis and emotion recognition results, the server selects appropriate keywords and automatically generates banner ads based on templates. The tone and message of the ads are also fine-tuned according to the user's emotions.

[0396] Examples:

[0397] A banner ad containing the message "Experience our quality products now!" is generated and placed alongside the logo of Company A. If the user is excited, stronger fonts and colors are used.

[0398] Ad destination recommendation process:

[0399] The server analyzes past advertising performance data and market data, and uses machine learning models to recommend optimal ad delivery destinations.

[0400] Examples:

[0401] The server analyzes past advertising data, determines that LINE users in a particular market have high engagement, and recommends that Company A distribute advertisements via LINE.

[0402] Effectiveness analysis process:

[0403] During the advertising campaign, the server collects performance data from each advertising platform and automatically analyzes its effectiveness.

[0404] Examples:

[0405] The server obtains the number of clicks, conversion rate, and target demographic data for Company A's banner ads from the LINE API, and derives the result that the click rate was particularly high among women aged 25-35.

[0406] Proposal and approach generation process:

[0407] Based on the results of the effectiveness analysis and emotion recognition data, the server automatically generates proposal materials and specific approaches for the target audience.

[0408] Examples:

[0409] Sarver created a presentation to outline a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users were nervous, he used visual infographics to help them understand.

[0410] In this way, the system of the present invention can automate processes that incorporate user emotional information, making it possible to improve the efficiency of advertising operations and achieve effective marketing.

[0411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0412] Step 1: Processing information collection requests

[0413] The user inputs the name of the company to which the proposal is to be submitted into the interface, and this input is sent to the server via the terminal.

[0414] Specific behavior and output:

[0415] When the user enters "Company A" and clicks the "Collect Information" button, the data is sent to the server, which then stores the received company name as data required for the next process.

[0416] Step 2: Data collection

[0417] Based on the name of the proposed company, the server uses web scraping technology and APIs to collect information from external databases and market reports.

[0418] Specific operations and inputs / outputs:

[0419] The server accesses the official website of Company A and scrapes "Business Overview," "Product Information," and "Latest News." It then retrieves market data and customer reviews of competitors B and C from an external database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database. These data are input and output as collected information.

[0420] Step 3: Data analysis

[0421] The server analyzes the collected data using natural language processing technology to extract the main keywords of the proposed company, comparisons with competitors, and the benefits of the company's products.

[0422] Specific operations and inputs / outputs:

[0423] The server takes the collected data as input and performs natural language processing. For example, it extracts keywords such as "high quality" and "reliability" from Company A's business activities and compares them with Company B's "price competitiveness," and concludes that Company A has an advantage in "quality." It also derives that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service." The analysis results are output.

[0424] Step 4: Emotion Recognition

[0425] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing their emotions in real time. This recognition data is reflected in the analysis data.

[0426] Specific operations and inputs / outputs:

[0427] When a user sets up an advertising campaign, video and audio data from the device's camera and microphone are sent to the server. The emotion engine analyzes this data and determines whether the user is nervous or excited. The recognized emotion data is output and used as input for the next step.

[0428] Step 5: Generate advertising content

[0429] The server automatically generates advertising content based on the analysis results and emotion recognition data, using appropriate keyword selection and templates.

[0430] Specific operations and inputs / outputs:

[0431] The server takes the analysis results and the user's emotional data as input and generates a banner ad with the message "Experience our high-quality product now!" The ad also includes the logo of Company A. If the user is excited, bold fonts and vivid colors are used. The generated ad banner is output.

[0432] Step 6: Recommend ad destinations

[0433] The server analyzes historical advertising performance data and the latest market data and uses machine learning models to recommend optimal ad delivery destinations.

[0434] Specific operations and inputs / outputs:

[0435] The server inputs past advertising data and market data into a machine learning model. For example, it discovers that user engagement on LINE is high, and recommends that Company A distribute ads on LINE. Recommendation information is output.

[0436] Step 7: Analyze the effectiveness of your advertising campaign

[0437] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness.

[0438] Specific operations and inputs / outputs:

[0439] The server analyzes the number of clicks, conversion rate, and target demographic attribute data obtained from the LINE API, etc. For example, it may find that the click rate is particularly high among women aged 25-35. This analysis result is then output.

[0440] Step 8: Generate proposals and approaches

[0441] The server automatically generates proposal materials and specific approaches for the target audience based on the results of the effectiveness analysis and emotion recognition data.

[0442] Specific operations and inputs / outputs:

[0443] The server takes the analysis results and sentiment data as input and creates presentation materials showing marketing strategies for women aged 25-35. It also provides effective approaches that combine social media campaigns and email marketing. If the user is nervous, it makes extensive use of visually easy-to-understand infographics. The generated proposal materials and approach methods are output.

[0444] (Application example 2)

[0445] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0446] Conventional advertising sales tools only collect and analyze information on the business activities of clients and competitors, but do not generate advertising content that takes into account the emotions and psychological state of users. As a result, advertising effectiveness is not maximized, and it takes time and effort to create an optimal advertising strategy.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0448] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and performing keyword and competitor comparisons, means for recognizing user emotions and reflecting the emotional information in real time, means for automatically generating banner advertising content based on the analysis results and emotion recognition results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing effectiveness, and means for automatically generating proposal materials and approach methods for target demographics based on the analyzed effectiveness results and emotion recognition results. This makes it possible to generate advertising content that takes user emotions into consideration and to quickly plan optimal advertising strategies.

[0449] "Business details of the proposed company" refers to the business details, business overview, and information on the products offered by the company to which the advertisement is being proposed.

[0450] "Competitor information" refers to data on other companies competing in the same market as the target company, as well as insights such as market share and product lineup.

[0451] "Data on your own products" refers to detailed information about the products and services your company offers, their performance, past sales performance, and marketing data.

[0452] "Means of collection" refers to the methods and technologies used to gather data and information via the Internet, etc.

[0453] "Means of analysis" refers to methods and techniques for analyzing collected data and extracting important keywords and features.

[0454] "Keyword and Competitive Comparison" refers to the process of identifying important words and phrases related to the client company's business and using them to evaluate the differences and advantages of the client company compared to its competitors.

[0455] "Means of recognizing user emotions and reflecting that emotional information in real time" refers to a method of using emotion recognition technology to determine a user's psychological state in real time from their facial expressions, tone of voice, etc., and reflecting the results in advertisement generation and analysis.

[0456] "Means for automatically generating banner advertisement content" refers to a technology for automatically creating banner-style advertisements based on collected and analyzed information and user sentiment results.

[0457] "Means for recommending optimal ad delivery destinations" refers to a method of suggesting to users the platforms and media that are predicted to deliver ads most effectively, based on past performance data of ads and market data.

[0458] "Means for collecting advertising performance data and analyzing its effectiveness" refers to methods for collecting data such as the number of clicks and conversion rate obtained during the advertising campaign period and analyzing its effectiveness.

[0459] "Means for automatically generating proposal materials and approaches" refers to technology that automatically creates marketing materials and specific advertising strategies specific to the target audience based on analyzed data and emotion recognition results.

[0460] In this invention, an automatic advertising content generation system that incorporates user emotions is realized through the following steps.

[0461] First, the user enters information such as the name of the company to which they are making a proposal, their advertising objectives, and their target user demographic through the interface. Based on this information, the server collects data on the business activities of the company to which they are making a proposal, information on their competitors, and their own products. Data collection is carried out using web scraping technology and APIs (e.g., Google (registered trademark) API, Facebook API).

[0462] The collected data is analyzed using natural language processing (NLP) technology. This analysis extracts the client company's key keywords, comparison information with competitors, and the advantages of the company's products. The server then builds the basis for advertising content based on the analyzed information.

[0463] Next, the smartphone's camera and microphone are used to recognize the user's emotions in real time. The emotion engine uses Google Cloud Vision and Amazon Rekognition to analyze emotions from the user's facial expressions and tone of voice. This emotional information is reflected in ad generation, automatically generating ad content that matches the user's psychological state.

[0464] To automatically generate advertising content, appropriate keywords and messages are selected, and banner ads are created based on templates. For example, Canva API is used to customize design templates and adjust colors and fonts based on emotions.

[0465] Recommendations for optimal ad delivery are based on past ad performance data and market data. The server uses machine learning models to predict and recommend the most effective ad platform. For example, social media platforms such as LINE and Instagram may be selected.

[0466] During the advertising campaign, the server collects performance data from the advertising platform and automatically analyzes its effectiveness. The analysis results are visualized on a dashboard and provided to users. Analysis tools used include Google Analytics and Mixpanel.

[0467] Furthermore, the server automatically generates proposal materials and specific approaches for the target audience based on the analysis and emotion recognition results. Marketing materials and advertising strategy proposals can make extensive use of visually easy-to-understand infographics.

[0468] As a concrete example, consider a food company that wants to advertise a new, high-quality gourmet food product. By entering the company's name and the purpose "gourmet food advertising" into the app, the system collects and analyzes the company's information. At the same time, as the user sets up an advertising campaign, the emotional engine recognizes the user's emotional state. Based on this, an advertising banner emphasizing the product's high quality is generated, and advertising is recommended on social media platforms that are predicted to be particularly popular, such as Instagram.

[0469] An example prompt for a generative AI model might look like this:

[0470] Collect data and generate advertising content for the following companies' advertising campaigns:

[0471] Company name:XX Food

[0472] Advertising objective: Introducing a new line of high-quality gourmet food

[0473] Target market: Young people's food market

[0474] Competitors: YY Foods, ZZ Foods

[0475] Include ad effectiveness analysis based on user sentiment.

[0476] In this way, it becomes possible to automatically generate advertising content that incorporates user emotions and to quickly develop marketing strategies.

[0477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0478] Step 1:

[0479] The user inputs information such as the name of the company to be proposed to, advertising purpose, and target user demographics into the interface via their device. The input information is sent to the server. The input data includes the company name, advertising purpose, target demographics, etc., and subsequent data collection and analysis are based on this.

[0480] Step 2:

[0481] Based on the input name of the proposed company, the server uses web scraping technology and APIs (e.g., Google API, Facebook API) to collect data on the company's business details, competitor information, and company merchandise from the company's official website, external databases, market reports, etc. This allows for the acquisition of company overviews, product information, and market data.

[0482] Step 3:

[0483] The server analyzes the collected data using natural language processing technology. During the analysis process, the company's key keywords, comparisons with competitors, and the benefits of its products are extracted. This processing is performed using Python NLP libraries (e.g., spaCy, NLTK). The input data is the collected text information, and the output is the extracted keywords and competitive comparison information.

[0484] Step 4:

[0485] The server uses the smartphone's camera and microphone to recognize the user's emotions in real time. This recognition utilizes the emotion recognition functions of Google Cloud Vision and Amazon Rekognition. The input data is the user's facial expression images and voice data, and the output is analyzed emotional information. Based on this, the user's psychological state is evaluated.

[0486] Step 5:

[0487] Based on the analysis and emotion recognition results, the server automatically generates advertising content. Specifically, it selects appropriate keywords and messages and creates template-based banner ads using the Canva API. The input data includes the analysis results and emotion information, and the output is the completed banner ad.

[0488] Step 6:

[0489] The server recommends the optimal ad placement based on past ad performance data and market data. It uses a machine learning model to predict the most effective ad platform and provides recommendations to users. This process uses Python machine learning libraries (e.g., scikit-learn), with the input being past performance data and the output being recommended placements.

[0490] Step 7:

[0491] During the advertising campaign, the server collects performance data from the advertising platforms and analyzes their effectiveness. The analysis is performed using Google Analytics and Mixpanel, with the input data being the performance data obtained from each platform and the output being a dashboard of the analysis results.

[0492] Step 8:

[0493] The server automatically generates proposal materials and approaches for the target demographic based on the analyzed data and emotion recognition results. These materials include specific marketing strategies and advertising approaches. The input data are the analysis results and emotion information, and the output is proposal materials.

[0494] This makes it possible to generate advertising content that takes user emotions into consideration and quickly develop optimal advertising strategies.

[0495] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0496] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0497] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0498] [Second embodiment]

[0499] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0500] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0501] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0502] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0503] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0504] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0505] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0506] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0507] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0508] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0509] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0510] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0511] The present invention provides an advertising sales tool designed to improve the efficiency and effectiveness of Internet advertising operations. Specifically, the system collects data on clients' business activities, competitors, and the company's own products, analyzes this data, automatically generates advertising content, recommends optimal ad delivery destinations, and analyzes the effectiveness of advertising.

[0512] Program processing

[0513] Data Collection Process

[0514] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[0515] Examples:

[0516] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[0517] Data Analysis Process

[0518] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0519] Examples:

[0520] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0521] Automated advertising content creation process

[0522] Based on the analysis results, the server automatically generates advertising content, specifically by selecting appropriate keywords and creating banner ads based on templates.

[0523] Examples:

[0524] The server generates a banner ad containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[0525] Advertising destination recommendation process

[0526] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0527] Examples:

[0528] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[0529] Effects analysis process

[0530] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0531] Examples:

[0532] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0533] Automatic generation process for proposal materials and approach methods

[0534] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0535] Examples:

[0536] Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, and will propose an approach that combines social media campaigns and email marketing.

[0537] In this way, the system of the present invention highly automates all advertising operations, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, in order to maximize advertising effectiveness.

[0538] The processing flow will be explained below.

[0539] Step 1:

[0540] The user enters the name of the proposed company into the interface and sends an information gathering request.

[0541] For example, enter "Company A" and press the Start Collection button.

[0542] Step 2:

[0543] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[0544] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[0545] Step 3:

[0546] The server retrieves competitor information from external databases and market reports via API.

[0547] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[0548] Step 4:

[0549] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[0550] For example, obtain detailed specifications and past sales data for your company's "Product X."

[0551] Step 5:

[0552] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[0553] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[0554] Step 6:

[0555] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[0556] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[0557] Step 7:

[0558] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[0559] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[0560] Step 8:

[0561] The server selects keywords to be used for banner ads based on the results of data analysis.

[0562] For example, identify the main message to use in your banner: "Experience our quality products now!"

[0563] Step 9:

[0564] The server selects an appropriate design from pre-designed ad templates.

[0565] For example, choose a simple and elegant design to emphasize high quality.

[0566] Step 10:

[0567] The server combines the selected keywords with templates to automatically generate banner ads.

[0568] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[0569] Step 11:

[0570] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[0571] For example, determine whether LINE user engagement is high in a particular industry.

[0572] Step 12:

[0573] The server selects the most suitable advertising platform and recommends it to the device.

[0574] For example, we recommend that Company A distribute advertisements via LINE.

[0575] Step 13:

[0576] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[0577] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[0578] Step 14:

[0579] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[0580] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[0581] Step 15:

[0582] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[0583] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0584] Example 1

[0585] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0586] In conventional internet advertising, the processes of ad proposal, creation, distribution, effectiveness analysis, and optimization are all performed separately, resulting in inefficiency and requiring a lot of time and effort. Furthermore, there are issues with the accuracy of data collection and analysis, and the appropriateness of ad distribution, making it difficult to maximize advertising effectiveness. To solve these problems, a highly automated system that centralizes all advertising operations is required.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0588] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating advertising content based on the analysis results, means for analyzing past advertising performance data and the latest market data and recommending optimal advertising distribution destinations, means for collecting performance data from advertising platforms during the advertising campaign period and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results. This makes it possible to centrally and efficiently execute the entire process from data collection to analysis, advertising creation, distribution, analysis, and proposals.

[0589] "Business details of the proposed company" refers to information about the business, services, and products that a specific company or organization primarily conducts.

[0590] "Competitor information" is data about other companies or entities operating in the same market or industry as the proposal recipient.

[0591] "Data on your company's products" refers to detailed information about the products and services your company offers.

[0592] "Means of data collection" refers to the technologies or tools used to collect specific information, including, for example, web scraping technologies and APIs.

[0593] "Analyzing the data" is the process of examining the collected information in detail to gain new knowledge and insights.

[0594] "Keyword and Competitive Comparison Tools" are methods for identifying key words and phrases based on collected data and analyzing them against competitors.

[0595] "Means for automatically generating advertising content" refers to technology or software that automatically creates advertising content such as images, text, and videos based on analysis results.

[0596] "Historical advertising performance data" refers to data showing the results of advertising campaigns that have been run to date, including clicks, conversion rates, and engagement.

[0597] "Latest market data" means the latest information on current market movements and trends.

[0598] "Means for recommending optimal ad delivery destinations" refers to technologies and methods that, based on analyzed data, suggest to users the optimal platforms and media for effectively delivering advertisements.

[0599] "Collecting performance data from advertising platforms during the advertising campaign" refers to the process of obtaining data regarding the performance of the advertisement from each advertising platform during the period the advertisement is running.

[0600] "Means for analyzing effectiveness" means a method for analyzing collected advertising performance data and evaluating the success of the advertising.

[0601] "Analyzed effectiveness results" are the specific results and evaluation results obtained after analyzing advertising performance data.

[0602] "Means for automatically generating proposal materials and approaches for target demographics" refers to technology or software that automatically creates proposal materials and specific marketing approaches suitable for a specific target demographic based on the analyzed results.

[0603] The present invention is a system configured to provide an advertising sales tool and to improve the efficiency and effectiveness of Internet advertising operations. Specific program processing and embodiments thereof will be described in detail below.

[0604] This system involves a series of processes: collecting data on the client's business, information on competitors, and the company's own products, analyzing this data to automatically generate advertising content, recommending optimal ad distribution destinations, and analyzing the effectiveness of the advertising. Based on the results of the effectiveness analysis, it then automatically generates proposal materials and specific approach methods optimized for the target demographic.

[0605] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[0606] As a concrete example, when a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also obtains market data and customer reviews for competitors "Company B" and "Company C" from an external database. It also obtains detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[0607] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0608] As a concrete example, the server extracts keywords such as "high quality" and "reliability" from Company A's business activities, and analyzes that Company B has an advantage in "price competitiveness," while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0609] The server automatically generates advertising content based on the analysis results. Specifically, it selects appropriate keywords and creates banner ads based on templates.

[0610] As a specific example, the server generates a banner advertisement containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[0611] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0612] As a specific example, the server determines from past advertising data that LINE user engagement is high in a specific industry and recommends that Company A distribute advertisements via LINE.

[0613] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0614] As a specific example, the server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0615] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0616] As a concrete example, Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0617] To demonstrate the features of this system, a generative AI model is used to generate advertising content. Below are examples of prompts that users can use to instruct the generative AI model:

[0618] Example prompt sentence:

[0619] Create an advertising banner to showcase Company A's high-quality products. Focus on the following points:

[0620] Reliability

[0621] high performance

[0622] Comprehensive after-sales service

[0623] Company A's logo should also be included in the banner.

[0624] In this way, the system according to the present invention centrally and efficiently executes the entire process from data collection and analysis, to advertisement creation, distribution, analysis, and proposals, aiming to improve the efficiency and effectiveness of advertising operations.

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1: Enter the name of the company you are proposing to

[0627] The user accesses the system interface and inputs the name of the company to which the proposal is to be made.

[0628] Specific operation: The user enters "Company A" in the input field and clicks the Start Data Collection button.

[0629] Input: Name of the proposed company (e.g. Company A)

[0630] Output: Send the name of the proposed company to the server

[0631] Step 2: Start collecting data

[0632] The server starts collecting data based on the name of the proposed company received from the user.

[0633] Specific operation:

[0634] 1. Official website scraping: Access the official website of Company A and collect “Business Overview”, “Product Information”, and “Latest News”.

[0635] 2. Obtaining data from external databases: Use APIs to obtain market data and customer reviews from competitors "Company B" and "Company C."

[0636] 3. Collect information on your company's products from an internal database: Access the database within the system to obtain detailed information on your company's "Product X" and data on past advertising campaigns.

[0637] Input: Name of the proposed company (e.g. Company A), API of external database, query of internal database

[0638] Output: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[0639] Step 3: Data analysis

[0640] The server analyzes the collected data and extracts key keywords, competitive comparisons, and the benefits of the company's products.

[0641] Specific operation:

[0642] 1. Keyword extraction using NLP technology: Extract keywords such as "high quality" and "reliability" from Company A's business activities.

[0643] 2. Conduct competitive comparisons: Compare data from companies B and C with company A's data to identify competitive advantages.

[0644] 3. Analysis of the benefits of your company's products: Analyze how your company's product X's "high performance" and "excellent after-sales service" will work to your advantage in Company A's market.

[0645] Input: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[0646] Output: Keyword list, competitor comparison results, merit list of your company's products

[0647] Step 4: Automatic generation of advertising content

[0648] The server automatically generates advertising content based on the analysis results.

[0649] Specific operation:

[0650] 1. Keyword selection: Select selling points such as "high-quality products" from the analysis data.

[0651] 2. Place on banner template: Insert the message "Experience our high-quality products now!" into the template and place Company A's logo.

[0652] 3. Check the output: Create a preview of the ad banner and check the consistency of the text and design.

[0653] Input: Keyword list, template, company logo

[0654] Output: Advertising banner

[0655] Step 5: Recommend ad destinations

[0656] The server analyzes past advertising performance data and the latest market data to recommend the optimal advertising destinations.

[0657] Specific operation:

[0658] 1. Performance data collection: Extract past advertising data from the database.

[0659] 2. Analysis using machine learning models: Using machine learning models, we identify platforms (e.g., LINE) with high advertising engagement in specific industries.

[0660] 3. Proposal for ad distribution destination: Recommend ad distribution via LINE as the optimal ad distribution destination for Company A.

[0661] Input: Historical advertising performance data, latest market data

[0662] Output: Optimal ad distribution platform (e.g. LINE)

[0663] Step 6: Effectiveness analysis

[0664] The server collects performance data from the advertising platform during the advertising campaign and analyzes the effectiveness.

[0665] Specific operation:

[0666] 1. Use of API: Obtain click counts, conversion rates, and target demographic attribute data from advertising platforms (e.g., LINE API).

[0667] 2. Data aggregation and analysis: The acquired data is aggregated to derive high engagement among the target demographic (e.g., women aged 25-35).

[0668] 3. Visualization of results: Visualize the analysis results in graphs and charts to make them easy for users to understand.

[0669] Input: Ad performance data

[0670] Output: Effectiveness analysis results (e.g., high engagement among women aged 25-35)

[0671] Step 7: Automatic generation of proposal materials and approaches

[0672] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approach methods optimized for the target audience and provides them to the user.

[0673] Specific operation:

[0674] 1. Creating presentation materials: Automatically generate presentation materials that demonstrate marketing strategies targeting women aged 25-35.

[0675] 2. Proposal of approach method: Include in the proposal a specific approach method that combines social media campaigns and email marketing.

[0676] 3. Preview the final document: Review the generated proposal and make any necessary adjustments.

[0677] Input: Effect analysis results, template

[0678] Output: Proposal materials and specific approach

[0679] In this way, this system efficiently automates a series of processes, from detailed data collection and analysis, to ad generation, distribution recommendations, effectiveness analysis, and proposal document creation, and provides them to users.

[0680] (Application example 1)

[0681] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0682] In conventional internet advertising, business analysis, creation of advertising content, selection of distribution destinations, and effectiveness analysis are all done manually, which is time-consuming, labor-intensive, and inefficient.In addition, it is difficult to quickly make presentations and advertising proposals to client companies on-site, which places a heavy burden on sales representatives.

[0683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0684] In this invention, the server includes means for collecting data on the business details of the proposal recipient company, information on competing companies, and data on the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating banner advertising content based on the analysis results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results, and means for displaying proposal recipient company information, advertising content previews, recommended advertising destinations, and success rates using a portable display device. This automates the entire process from data collection and analysis, advertising content generation, advertising delivery recommendations, effectiveness analysis, and proposal material creation, and makes it possible to display related information on-site in real time.

[0685] "Business details of the proposed company" refers to basic information and activities of the business being proposed to.

[0686] "Competitor information" refers to information about other companies operating in the same market, including competitor product information, strategies, strengths and weaknesses.

[0687] "Data on our own products" refers to detailed information about the products and services we handle, including product features, prices, and past sales data.

[0688] "Collection methods" refers to the methods and tools used to obtain the required data from the internet or other sources, and may include web scraping techniques and APIs.

[0689] "Means of analyzing data" refers to methods and technologies for understanding and analyzing collected information, such as natural language processing technology and machine learning models.

[0690] "Means for automatically generating banner advertising content" refers to technology for automatically generating advertising banners based on collected and analyzed data.

[0691] "Means for recommending ad delivery destinations" refers to methods and tools for selecting and proposing optimal ad delivery destinations, including machine learning models that use past ad performance data and market trends.

[0692] "Performance data" refers to metrics and data used to measure the effectiveness of advertising campaigns, such as the number of ad clicks and conversion rates.

[0693] "Means for automatically generating proposal materials and approaches" refers to technology that automatically generates proposal materials and approaches optimized for the target audience based on the analysis results.

[0694] "Portable display device" refers to a portable display device, such as smart glasses.

[0695] "Ad content preview" refers to the function or process that allows you to check the content of the advertisement that will actually be displayed in advance.

[0696] "Probability of success" refers to a statistical metric used to indicate the likelihood of success of a proposal or advertising campaign.

[0697] The present invention provides a system for supporting advertising sales, and a specific embodiment thereof will be described. This system uses a portable display device to present various data in real time, enabling effective advertising proposals. Here, the description will focus on an embodiment using smart glasses.

[0698] First, the server collects data on the client company's business, competitors, and the client's products. Data collection is done using web scraping technology and data acquisition via API. Specific tools used include BeautifulSoup and Scrapy.

[0699] The collected data is then analyzed on a server. This analysis uses natural language processing (NLP) technology to extract keywords and information for competitive comparison. Software libraries used include spaCy and NLTK. Based on the analysis, the company's strengths and weaknesses, as well as the competitive advantages of its products, are revealed.

[0700] Based on the results of this analysis, the server automatically generates banner ad content. An ad template engine is used to generate the ads. Specifically, a template engine such as Jinja2 is used. For example, a banner ad containing the message "Experience our high-quality products now!" is generated and placed alongside the logo of the proposed company.

[0701] The server then uses a machine learning model to recommend the optimal ad delivery destination. By analyzing past ad performance data and market trends, it proposes the optimal platform and media. Specifically, it often uses TensorFlow or PyTorch, which are built on Python. For example, it determines "platforms with high user engagement in a specific industry" and recommends them to the proposed destination.

[0702] During the advertising campaign, the server collects and analyzes advertising performance data. This data is obtained via the advertising platform's API (e.g., LINE API). The server analyzes the number of clicks, conversion rate, target demographic attribute data, etc. to derive an effective advertising strategy.

[0703] Finally, based on the effectiveness results, the server automatically generates proposal materials and specific approaches for the target demographic. For example, it creates presentation materials proposing social media campaigns and email marketing as marketing strategies targeting women aged 25-35.

[0704] To support this process, smart glasses are used as portable display devices. The advertising assistant app installed on the smart glasses displays information about the companies being proposed to, previews of advertising content, recommended ad distribution destinations, and success rates in real time. Users can conduct effective sales activities while checking the displayed visual information.

[0705] For example, if a user targets "Company D," the smart glasses will display the following information:

[0706] 1. Company information such as "Company D: Providing high-quality food"

[0707] 2. Preview of the banner ad "Fresh Vegetable Campaign!"

[0708] 3. Recommendations for advertising outlets that say "Instagram advertising is effective"

[0709] 4. Presenting the probability of success: "This proposal has an 85% chance of success."

[0710] An example prompt might look like this:

[0711] "Please create a proposal for Company D. Company D provides high-quality food products and its main competitors are Companies E and F. Company D's main product is fresh vegetables, and past advertising campaigns have been successful on Instagram."

[0712] In this way, the system of the present invention automates the entire process from data collection and analysis, to generating advertising content, recommending advertising delivery, analyzing effectiveness, and creating proposal materials, allowing sales representatives to make advertising proposals efficiently.

[0713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0714] Step 1:

[0715] The server receives the name of the proposed company. The input here is the name of the proposed company specified by the user, and the server starts collecting data based on this.

[0716] Specifically, when a user enters the name of "Company A" into the system, the server uses web scraping technology (e.g., BeautifulSoup) and APIs (e.g., Scrapy) to obtain data on Company A's business activities, information on competitors, and its own products from the company's official website and external databases.

[0717] Step 2:

[0718] The server stores the collected data and prepares it for analysis. The input data is the collected business details, information on competitors, and data on the company's own products.

[0719] Specifically, the server stores data retrieved from web pages and APIs in a database and uses NLP technology to convert it into an analyzable format.

[0720] Step 3:

[0721] The server analyzes the data. The input here is the saved data, and the output is the analysis results (keywords, competitor comparisons, strengths of the company's products, etc.).

[0722] Specifically, the server uses a natural language processing library (such as spaCy or NLTK) to extract the key keywords of the proposed company and its strengths and weaknesses compared to its competitors.

[0723] Step 4:

[0724] The server automatically generates advertising content based on the analysis results. The input is the analysis results, and the output is the generated banner advertising content.

[0725] Specifically, the server uses an advertising template engine (such as Jinja2) to generate a banner ad that reflects the keywords. For example, it creates a banner that includes the message "Experience our high-quality products now!"

[0726] Step 5:

[0727] The server recommends the optimal ad delivery destination. The input is the analysis results and past ad performance data, and the output is the recommended ad delivery destination.

[0728] Specifically, the server uses machine learning models (such as TensorFlow or PyTorch) to predict the optimal distribution destination based on past data, suggesting, for example, "platforms with high user engagement in specific industries."

[0729] Step 6:

[0730] The server collects and analyzes performance data for the advertising campaign, with the input being the data collected during the advertising campaign and the output being the analysis results.

[0731] Specifically, the server collects data such as click counts and conversion rates from the advertising platform (e.g., the LINE API), analyzes it, and identifies the attributes of the target demographic and the effectiveness of the advertisement.

[0732] Step 7:

[0733] The server automatically generates proposal materials and approaches for the target audience based on the analysis results. The input is the results of the effectiveness analysis, and the output is the generated proposal materials and approaches.

[0734] Specifically, the server generates presentation materials showing the optimal marketing strategy for the target audience, proposing a strategy that combines, for example, a social media campaign and email marketing.

[0735] Step 8:

[0736] The device (smart glasses) displays to the user information about the proposed company, a preview of the advertising content, recommendations for ad delivery destinations, and the probability of success. The input is data sent from the server, and the output is the information displayed on the glasses' display.

[0737] Specifically, the advertising assistant app installed on the device presents company information and ad previews as visual information, which the user can use to conduct sales activities.

[0738] The above steps enable a complete process from data collection and analysis, to generating advertising content, recommending distribution, analyzing effectiveness, creating proposal materials, and displaying the results in real time on-site.

[0739] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0740] This invention provides a system that recognizes user emotions and automatically generates customized advertising content based on them by combining an emotion engine with an advertising sales tool. This system realizes more effective advertising by incorporating user emotional information in the process of collecting and analyzing data on the client's business, competitors, and the company's own products.

[0741] Program processing

[0742] Data Collection Process

[0743] The server receives an information gathering request by having the user input the name of the proposed company into the interface, and then collects data on the company's business operations, competitors, and products from the company's official website, external databases, market reports, etc. The data is obtained using web scraping technology and APIs.

[0744] Examples:

[0745] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[0746] Data Analysis Process

[0747] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0748] Examples:

[0749] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0750] User emotion recognition process by emotion engine

[0751] The server uses an emotion engine to recognize the user's emotions in real time based on the information provided by the user, thereby understanding the user's current psychological state and reflecting it in the analysis data.

[0752] Examples:

[0753] As users set up their ad campaigns, the emotion engine detects tension or excitement from their facial expressions and tone of voice, and if the user feels stressed, the system will recommend easier, more intuitive actions.

[0754] Automated advertising content creation process

[0755] Based on the analysis results and the recognition results of the emotion engine, the server automatically generates advertising content. Specifically, it selects appropriate keywords and creates banner ads based on templates. The tone and message of the ad are also adjusted according to the user's emotions.

[0756] Examples:

[0757] The server generates a banner ad with the message "Experience our quality products now!" and places it alongside the logo of Company A. If the user is excited, it uses more powerful fonts and colors.

[0758] Advertising destination recommendation process

[0759] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0760] Examples:

[0761] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[0762] Effects analysis process

[0763] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[0764] Examples:

[0765] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[0766] Automatic generation process for proposal materials and approach methods

[0767] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and specific approaches optimized for the target demographic and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[0768] Examples:

[0769] Sarver creates a presentation that outlines a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users are nervous, he uses visually appealing infographics.

[0770] In this way, the system of the present invention automates processes that incorporate user emotional information, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, thereby realizing more efficient advertising operations and more effective marketing.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The user enters the name of the proposed company into the interface and sends an information gathering request.

[0774] For example, enter "Company A" and press the Start Collection button.

[0775] Step 2:

[0776] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[0777] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[0778] Step 3:

[0779] The server retrieves competitor information from external databases and market reports via API.

[0780] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[0781] Step 4:

[0782] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[0783] For example, obtain detailed specifications and past sales data for your company's "Product X."

[0784] Step 5:

[0785] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[0786] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[0787] Step 6:

[0788] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[0789] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[0790] Step 7:

[0791] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[0792] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[0793] Step 8:

[0794] The server uses an emotion engine to recognize the user's emotions in real time.

[0795] For example, emotions are detected from the user's facial expressions and tone of voice.

[0796] Step 9:

[0797] The server selects keywords to be used for banner ads based on the results of data analysis and user emotional data.

[0798] For example, we recommend the message, "Experience our high-quality products now!"

[0799] Step 10:

[0800] The server selects an appropriate design from pre-designed ad templates.

[0801] For example, choose a simple and elegant design to emphasize high quality.

[0802] Step 11:

[0803] The server combines the selected keywords with templates to automatically generate banner ads.

[0804] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[0805] Step 12:

[0806] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[0807] For example, determine whether LINE user engagement is high in a particular industry.

[0808] Step 13:

[0809] The server selects the most suitable advertising platform and recommends it to the device.

[0810] For example, we recommend that Company A distribute advertisements via LINE.

[0811] Step 14:

[0812] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[0813] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[0814] Step 15:

[0815] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[0816] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[0817] Step 16:

[0818] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[0819] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[0820] Example 2

[0821] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0822] Conventional advertising sales tools do not take user emotions into account when collecting and analyzing client company information and competitor data, which means that advertising content is not effectively targeted. This results in poor advertising performance and limited effectiveness of marketing strategies.

[0823] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the business activities of proposal recipients, information on competing companies, and the company's own products, means for analyzing the collected data and performing keyword and competitive comparisons, means for recognizing user emotions in real time and reflecting them in the analysis results, means for automatically generating advertising content based on the analysis results and emotion recognition data, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results and emotion recognition data. This makes it possible to generate effective advertising content that incorporates user emotional information and optimize marketing strategies.

[0824] "Business details of the proposed company" is an outline of the main business and activities of the company that will be collected and analyzed.

[0825] "Competitor information" is data about other companies competing in the same market as the company to which the proposal is being made.

[0826] "Data on our own products" refers to information about the products and services offered by the company making the proposal.

[0827] "User emotion" refers to the psychological state of the user as recognized from the facial expression, tone of voice, etc. of the user using the system.

[0828] "Analysis results" refer to the results of analyzing collected data using natural language processing technology, etc.

[0829] "Advertising content" refers to promotional text, images, banners, etc. that are generated based on the analysis results.

[0830] "Advertising destination" refers to the medium or platform on which the generated advertisement is displayed.

[0831] "Performance data" refers to data that indicates the effectiveness of an advertisement, such as the number of clicks and conversion rate after the advertisement is delivered.

[0832] "Proposal materials" are presentation materials and reports created based on marketing strategies and advertising content.

[0833] "Approach" refers to the optimal marketing strategy or method for the target audience.

[0834] "Web scraping technology" is a technology that automatically obtains data from websites.

[0835] An "API" is an interface that allows different software systems to communicate with each other.

[0836] "Natural language processing technology" is a technology that allows computers to understand and analyze text written in natural language.

[0837] "Emotion recognition means" refers to a technique or device for recognizing a user's emotions from facial expressions, tone of voice, and the like.

[0838] "Real-time" refers to processing occurring immediately after a user action is taken.

[0839] The present invention relates to a system that automatically generates customized advertising content by incorporating user emotional information during the process of collecting and analyzing data on the client's business, competitors, and the company's own products. To specifically implement this system, the following methods and processes are used.

[0840] Hardware and software used

[0841] server:

[0842] You need a server that can run web scraping technologies and APIs to gather information from multiple data sources (official websites, external databases, market reports).

[0843] A server equipped with a high-performance processor and GPU is also required to run the emotion engine.

[0844] Device:

[0845] A personal computer or tablet device that can run an interface (web browser or dedicated application) for users to input data is required.

[0846] A device equipped with a camera and microphone is required, and real-time emotional recognition of the user's facial expressions and voice is required.

[0847] Main technologies used:

[0848] Web scraping technologies (e.g., Beautiful Soup, Selenium)

[0849] API (e.g. RESTful API)

[0850] Natural language processing technology (e.g., Python's NLTK, SpaCy)

[0851] Emotion recognition technology (e.g. OpenCV, TensorFlow)

[0852] Machine learning models (e.g., Scikit-learn, TensorFlow)

[0853] System operation process

[0854] Data collection process:

[0855] When the server receives the name of the company from the user, it collects data on the company's business, competitors, and products from the company's official website, external databases, and market reports, using web scraping technology and APIs.

[0856] Examples:

[0857] When a user specifies "Company A," the server collects "Business Overview," "Product Information," and "Latest News" from Company A's official website. It also collects market data and customer reviews for competitors "Company B" and "Company C." It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[0858] Data analysis process:

[0859] The data collected by the server is analyzed using natural language processing technology, automatically extracting key keywords such as the client company's business, competitor comparisons, and the benefits of the company's products.

[0860] Examples:

[0861] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness," but Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0862] Emotion Recognition Process:

[0863] Using the emotion engine, the server analyzes the user's facial expressions and tone of voice to recognize emotions in real time, allowing it to understand the user's current psychological state and reflect it in data analysis.

[0864] Examples:

[0865] As users set up their ad campaigns, an emotion engine detects tension or excitement from their facial expressions and tone of voice. If they are feeling stressed, the system recommends easier, more intuitive actions.

[0866] Advertising content generation process:

[0867] Based on the analysis and emotion recognition results, the server selects appropriate keywords and automatically generates banner ads based on templates. The tone and message of the ads are also fine-tuned according to the user's emotions.

[0868] Examples:

[0869] A banner ad containing the message "Experience our quality products now!" is generated and placed alongside the logo of Company A. If the user is excited, stronger fonts and colors are used.

[0870] Ad destination recommendation process:

[0871] The server analyzes past advertising performance data and market data, and uses machine learning models to recommend optimal ad delivery destinations.

[0872] Examples:

[0873] The server analyzes past advertising data, determines that LINE users in a particular market have high engagement, and recommends that Company A distribute advertisements via LINE.

[0874] Effectiveness analysis process:

[0875] During the advertising campaign, the server collects performance data from each advertising platform and automatically analyzes its effectiveness.

[0876] Examples:

[0877] The server obtains the number of clicks, conversion rate, and target demographic data for Company A's banner ads from the LINE API, and derives the result that the click rate was particularly high among women aged 25-35.

[0878] Proposal and approach generation process:

[0879] Based on the results of the effectiveness analysis and emotion recognition data, the server automatically generates proposal materials and specific approaches for the target audience.

[0880] Examples:

[0881] Sarver created a presentation to outline a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users were nervous, he used visual infographics to help them understand.

[0882] In this way, the system of the present invention can automate processes that incorporate user emotional information, making it possible to improve the efficiency of advertising operations and achieve effective marketing.

[0883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0884] Step 1: Processing information collection requests

[0885] The user inputs the name of the company to which the proposal is to be submitted into the interface, and this input is sent to the server via the terminal.

[0886] Specific behavior and output:

[0887] When the user enters "Company A" and clicks the "Collect Information" button, the data is sent to the server, which then stores the received company name as data required for the next process.

[0888] Step 2: Data collection

[0889] Based on the name of the proposed company, the server uses web scraping technology and APIs to collect information from external databases and market reports.

[0890] Specific operations and inputs / outputs:

[0891] The server accesses the official website of Company A and scrapes "Business Overview," "Product Information," and "Latest News." It then retrieves market data and customer reviews of competitors B and C from an external database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database. These data are input and output as collected information.

[0892] Step 3: Data analysis

[0893] The server analyzes the collected data using natural language processing technology to extract the main keywords of the proposed company, comparisons with competitors, and the benefits of the company's products.

[0894] Specific operations and inputs / outputs:

[0895] The server takes the collected data as input and performs natural language processing. For example, it extracts keywords such as "high quality" and "reliability" from Company A's business activities and compares them with Company B's "price competitiveness," and concludes that Company A has an advantage in "quality." It also derives that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service." The analysis results are output.

[0896] Step 4: Emotion Recognition

[0897] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing their emotions in real time. This recognition data is reflected in the analysis data.

[0898] Specific operations and inputs / outputs:

[0899] When a user sets up an advertising campaign, video and audio data from the device's camera and microphone are sent to the server. The emotion engine analyzes this data and determines whether the user is nervous or excited. The recognized emotion data is output and used as input for the next step.

[0900] Step 5: Generate advertising content

[0901] The server automatically generates advertising content based on the analysis results and emotion recognition data, using appropriate keyword selection and templates.

[0902] Specific operations and inputs / outputs:

[0903] The server takes the analysis results and the user's emotional data as input and generates a banner ad with the message "Experience our high-quality product now!" The ad also includes the logo of Company A. If the user is excited, bold fonts and vivid colors are used. The generated ad banner is output.

[0904] Step 6: Recommend ad destinations

[0905] The server analyzes historical advertising performance data and the latest market data and uses machine learning models to recommend optimal ad delivery destinations.

[0906] Specific operations and inputs / outputs:

[0907] The server inputs past advertising data and market data into a machine learning model. For example, it discovers that user engagement on LINE is high, and recommends that Company A distribute ads on LINE. Recommendation information is output.

[0908] Step 7: Analyze the effectiveness of your advertising campaign

[0909] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness.

[0910] Specific operations and inputs / outputs:

[0911] The server analyzes the number of clicks, conversion rate, and target demographic attribute data obtained from the LINE API, etc. For example, it may find that the click rate is particularly high among women aged 25-35. This analysis result is then output.

[0912] Step 8: Generate proposals and approaches

[0913] The server automatically generates proposal materials and specific approaches for the target audience based on the results of the effectiveness analysis and emotion recognition data.

[0914] Specific operations and inputs / outputs:

[0915] The server takes the analysis results and sentiment data as input and creates presentation materials showing marketing strategies for women aged 25-35. It also provides effective approaches that combine social media campaigns and email marketing. If the user is nervous, it makes extensive use of visually easy-to-understand infographics. The generated proposal materials and approach methods are output.

[0916] (Application example 2)

[0917] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0918] Conventional advertising sales tools only collect and analyze information on the business activities of clients and competitors, but do not generate advertising content that takes into account the emotions and psychological state of users. As a result, advertising effectiveness is not maximized, and it takes time and effort to create an optimal advertising strategy.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0920] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and performing keyword and competitor comparisons, means for recognizing user emotions and reflecting the emotional information in real time, means for automatically generating banner advertising content based on the analysis results and emotion recognition results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing effectiveness, and means for automatically generating proposal materials and approach methods for target demographics based on the analyzed effectiveness results and emotion recognition results. This makes it possible to generate advertising content that takes user emotions into consideration and to quickly plan optimal advertising strategies.

[0921] "Business details of the proposed company" refers to the business details, business overview, and information on the products offered by the company to which the advertisement is being proposed.

[0922] "Competitor information" refers to data on other companies competing in the same market as the target company, as well as insights such as market share and product lineup.

[0923] "Data on your own products" refers to detailed information about the products and services your company offers, their performance, past sales performance, and marketing data.

[0924] "Means of collection" refers to the methods and technologies used to gather data and information via the Internet, etc.

[0925] "Means of analysis" refers to methods and techniques for analyzing collected data and extracting important keywords and features.

[0926] "Keyword and Competitive Comparison" refers to the process of identifying important words and phrases related to the client company's business and using them to evaluate the differences and advantages of the client company compared to its competitors.

[0927] "Means of recognizing user emotions and reflecting that emotional information in real time" refers to a method of using emotion recognition technology to determine a user's psychological state in real time from their facial expressions, tone of voice, etc., and reflecting the results in advertisement generation and analysis.

[0928] "Means for automatically generating banner advertisement content" refers to a technology for automatically creating banner-style advertisements based on collected and analyzed information and user sentiment results.

[0929] "Means for recommending optimal ad delivery destinations" refers to a method of suggesting to users the platforms and media that are predicted to deliver ads most effectively, based on past performance data of ads and market data.

[0930] "Means for collecting advertising performance data and analyzing its effectiveness" refers to methods for collecting data such as the number of clicks and conversion rate obtained during the advertising campaign period and analyzing its effectiveness.

[0931] "Means for automatically generating proposal materials and approaches" refers to technology that automatically creates marketing materials and specific advertising strategies specific to the target audience based on analyzed data and emotion recognition results.

[0932] In this invention, an automatic advertising content generation system that incorporates user emotions is realized through the following steps.

[0933] First, the user enters information such as the name of the company to which they are making a proposal, their advertising objectives, and their target user demographic through the interface. Based on this information, the server collects data on the business activities of the company to which they are making a proposal, information on their competitors, and their own products. Data collection is done using web scraping technology and APIs (e.g., Google API, Facebook API).

[0934] The collected data is analyzed using natural language processing (NLP) technology. This analysis extracts the client company's key keywords, comparison information with competitors, and the advantages of the company's products. The server then builds the basis for advertising content based on the analyzed information.

[0935] Next, the smartphone's camera and microphone are used to recognize the user's emotions in real time. The emotion engine uses Google Cloud Vision and Amazon Rekognition to analyze emotions from the user's facial expressions and tone of voice. This emotional information is reflected in ad generation, automatically generating ad content that matches the user's psychological state.

[0936] To automatically generate advertising content, appropriate keywords and messages are selected, and banner ads are created based on templates. For example, Canva API is used to customize design templates and adjust colors and fonts based on emotions.

[0937] Recommendations for optimal ad delivery are based on past ad performance data and market data. The server uses machine learning models to predict and recommend the most effective ad platform. For example, social media platforms such as LINE and Instagram may be selected.

[0938] During the advertising campaign, the server collects performance data from the advertising platform and automatically analyzes its effectiveness. The analysis results are visualized on a dashboard and provided to users. Analysis tools used include Google Analytics and Mixpanel.

[0939] Furthermore, the server automatically generates proposal materials and specific approaches for the target audience based on the analysis and emotion recognition results. Marketing materials and advertising strategy proposals can make extensive use of visually easy-to-understand infographics.

[0940] As a concrete example, consider a food company that wants to advertise a new, high-quality gourmet food product. By entering the company's name and the purpose "gourmet food advertising" into the app, the system collects and analyzes the company's information. At the same time, as the user sets up an advertising campaign, the emotional engine recognizes the user's emotional state. Based on this, an advertising banner emphasizing the product's high quality is generated, and advertising is recommended on social media platforms that are predicted to be particularly popular, such as Instagram.

[0941] An example prompt for a generative AI model might look like this:

[0942] Collect data and generate advertising content for the following companies' advertising campaigns:

[0943] Company name:XX Food

[0944] Advertising objective: Introducing a new line of high-quality gourmet food

[0945] Target market: Young people's food market

[0946] Competitors: YY Foods, ZZ Foods

[0947] Include ad effectiveness analysis based on user sentiment.

[0948] In this way, it becomes possible to automatically generate advertising content that incorporates user emotions and to quickly develop marketing strategies.

[0949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] The user inputs information such as the name of the company to be proposed to, advertising purpose, and target user demographics into the interface via their device. The input information is sent to the server. The input data includes the company name, advertising purpose, target demographics, etc., and subsequent data collection and analysis are based on this.

[0952] Step 2:

[0953] Based on the input name of the proposed company, the server uses web scraping technology and APIs (e.g., Google API, Facebook API) to collect data on the company's business details, competitor information, and company merchandise from the company's official website, external databases, market reports, etc. This allows for the acquisition of company overviews, product information, and market data.

[0954] Step 3:

[0955] The server analyzes the collected data using natural language processing technology. During the analysis process, the company's key keywords, comparisons with competitors, and the benefits of its products are extracted. This processing is performed using Python NLP libraries (e.g., spaCy, NLTK). The input data is the collected text information, and the output is the extracted keywords and competitive comparison information.

[0956] Step 4:

[0957] The server uses the smartphone's camera and microphone to recognize the user's emotions in real time. This recognition utilizes the emotion recognition functions of Google Cloud Vision and Amazon Rekognition. The input data is the user's facial expression images and voice data, and the output is analyzed emotional information. Based on this, the user's psychological state is evaluated.

[0958] Step 5:

[0959] Based on the analysis and emotion recognition results, the server automatically generates advertising content. Specifically, it selects appropriate keywords and messages and creates template-based banner ads using the Canva API. The input data includes the analysis results and emotion information, and the output is the completed banner ad.

[0960] Step 6:

[0961] The server recommends the optimal ad placement based on past ad performance data and market data. It uses a machine learning model to predict the most effective ad platform and provides recommendations to users. This process uses Python machine learning libraries (e.g., scikit-learn), with the input being past performance data and the output being recommended placements.

[0962] Step 7:

[0963] During the advertising campaign, the server collects performance data from the advertising platforms and analyzes their effectiveness. The analysis is performed using Google Analytics and Mixpanel, with the input data being the performance data obtained from each platform and the output being a dashboard of the analysis results.

[0964] Step 8:

[0965] The server automatically generates proposal materials and approaches for the target demographic based on the analyzed data and emotion recognition results. These materials include specific marketing strategies and advertising approaches. The input data are the analysis results and emotion information, and the output is proposal materials.

[0966] This makes it possible to generate advertising content that takes user emotions into consideration and quickly develop optimal advertising strategies.

[0967] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0968] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0969] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0970] [Third embodiment]

[0971] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0972] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0973] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0974] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0975] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0976] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0977] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0978] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0979] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0980] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0981] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0982] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0983] The present invention provides an advertising sales tool designed to improve the efficiency and effectiveness of Internet advertising operations. Specifically, the system collects data on clients' business activities, competitors, and the company's own products, analyzes this data, automatically generates advertising content, recommends optimal ad delivery destinations, and analyzes the effectiveness of advertising.

[0984] Program processing

[0985] Data Collection Process

[0986] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[0987] Examples:

[0988] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[0989] Data Analysis Process

[0990] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[0991] Examples:

[0992] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[0993] Automated advertising content creation process

[0994] Based on the analysis results, the server automatically generates advertising content, specifically by selecting appropriate keywords and creating banner ads based on templates.

[0995] Examples:

[0996] The server generates a banner ad containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[0997] Advertising destination recommendation process

[0998] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[0999] Examples:

[1000] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[1001] Effects analysis process

[1002] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1003] Examples:

[1004] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1005] Automatic generation process for proposal materials and approach methods

[1006] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1007] Examples:

[1008] Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, and will propose an approach that combines social media campaigns and email marketing.

[1009] In this way, the system of the present invention highly automates all advertising operations, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, in order to maximize advertising effectiveness.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] The user enters the name of the proposed company into the interface and sends an information gathering request.

[1013] For example, enter "Company A" and press the Start Collection button.

[1014] Step 2:

[1015] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[1016] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[1017] Step 3:

[1018] The server retrieves competitor information from external databases and market reports via API.

[1019] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[1020] Step 4:

[1021] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[1022] For example, obtain detailed specifications and past sales data for your company's "Product X."

[1023] Step 5:

[1024] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[1025] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[1026] Step 6:

[1027] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[1028] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[1029] Step 7:

[1030] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[1031] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[1032] Step 8:

[1033] The server selects keywords to be used for banner ads based on the results of data analysis.

[1034] For example, identify the main message to use in your banner: "Experience our quality products now!"

[1035] Step 9:

[1036] The server selects an appropriate design from pre-designed ad templates.

[1037] For example, choose a simple and elegant design to emphasize high quality.

[1038] Step 10:

[1039] The server combines the selected keywords with templates to automatically generate banner ads.

[1040] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[1041] Step 11:

[1042] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[1043] For example, determine whether LINE user engagement is high in a particular industry.

[1044] Step 12:

[1045] The server selects the most suitable advertising platform and recommends it to the device.

[1046] For example, we recommend that Company A distribute advertisements via LINE.

[1047] Step 13:

[1048] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[1049] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[1050] Step 14:

[1051] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[1052] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[1053] Step 15:

[1054] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[1055] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1056] Example 1

[1057] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1058] In conventional internet advertising, the processes of ad proposal, creation, distribution, effectiveness analysis, and optimization are all performed separately, resulting in inefficiency and requiring a lot of time and effort. Furthermore, there are issues with the accuracy of data collection and analysis, and the appropriateness of ad distribution, making it difficult to maximize advertising effectiveness. To solve these problems, a highly automated system that centralizes all advertising operations is required.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1060] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating advertising content based on the analysis results, means for analyzing past advertising performance data and the latest market data and recommending optimal advertising distribution destinations, means for collecting performance data from advertising platforms during the advertising campaign period and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results. This makes it possible to centrally and efficiently execute the entire process from data collection to analysis, advertising creation, distribution, analysis, and proposals.

[1061] "Business details of the proposed company" refers to information about the business, services, and products that a specific company or organization primarily conducts.

[1062] "Competitor information" is data about other companies or entities operating in the same market or industry as the proposal recipient.

[1063] "Data on your company's products" refers to detailed information about the products and services your company offers.

[1064] "Means of data collection" refers to the technologies or tools used to collect specific information, including, for example, web scraping technologies and APIs.

[1065] "Analyzing the data" is the process of examining the collected information in detail to gain new knowledge and insights.

[1066] "Keyword and Competitive Comparison Tools" are methods for identifying key words and phrases based on collected data and analyzing them against competitors.

[1067] "Means for automatically generating advertising content" refers to technology or software that automatically creates advertising content such as images, text, and videos based on analysis results.

[1068] "Historical advertising performance data" refers to data showing the results of advertising campaigns that have been run to date, including clicks, conversion rates, and engagement.

[1069] "Latest market data" means the latest information on current market movements and trends.

[1070] "Means for recommending optimal ad delivery destinations" refers to technologies and methods that, based on analyzed data, suggest to users the optimal platforms and media for effectively delivering advertisements.

[1071] "Collecting performance data from advertising platforms during the advertising campaign" refers to the process of obtaining data regarding the performance of the advertisement from each advertising platform during the period the advertisement is running.

[1072] "Means for analyzing effectiveness" means a method for analyzing collected advertising performance data and evaluating the success of the advertising.

[1073] "Analyzed effectiveness results" are the specific results and evaluation results obtained after analyzing advertising performance data.

[1074] "Means for automatically generating proposal materials and approaches for target demographics" refers to technology or software that automatically creates proposal materials and specific marketing approaches suitable for a specific target demographic based on the analyzed results.

[1075] The present invention is a system configured to provide an advertising sales tool and to improve the efficiency and effectiveness of Internet advertising operations. Specific program processing and embodiments thereof will be described in detail below.

[1076] This system involves a series of processes: collecting data on the client's business, information on competitors, and the company's own products, analyzing this data to automatically generate advertising content, recommending optimal ad distribution destinations, and analyzing the effectiveness of the advertising. Based on the results of the effectiveness analysis, it then automatically generates proposal materials and specific approach methods optimized for the target demographic.

[1077] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[1078] As a concrete example, when a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also obtains market data and customer reviews for competitors "Company B" and "Company C" from an external database. It also obtains detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[1079] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[1080] As a concrete example, the server extracts keywords such as "high quality" and "reliability" from Company A's business activities, and analyzes that Company B has an advantage in "price competitiveness," while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1081] The server automatically generates advertising content based on the analysis results. Specifically, it selects appropriate keywords and creates banner ads based on templates.

[1082] As a specific example, the server generates a banner advertisement containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[1083] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[1084] As a specific example, the server determines from past advertising data that LINE user engagement is high in a specific industry and recommends that Company A distribute advertisements via LINE.

[1085] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1086] As a specific example, the server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1087] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1088] As a concrete example, Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1089] To demonstrate the features of this system, a generative AI model is used to generate advertising content. Below are examples of prompts that users can use to instruct the generative AI model:

[1090] Example prompt sentence:

[1091] Create an advertising banner to showcase Company A's high-quality products. Focus on the following points:

[1092] Reliability

[1093] high performance

[1094] Comprehensive after-sales service

[1095] Company A's logo should also be included in the banner.

[1096] In this way, the system according to the present invention centrally and efficiently executes the entire process from data collection and analysis, to advertisement creation, distribution, analysis, and proposals, aiming to improve the efficiency and effectiveness of advertising operations.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] Step 1: Enter the name of the company you are proposing to

[1099] The user accesses the system interface and inputs the name of the company to which the proposal is to be made.

[1100] Specific operation: The user enters "Company A" in the input field and clicks the Start Data Collection button.

[1101] Input: Name of the proposed company (e.g. Company A)

[1102] Output: Send the name of the proposed company to the server

[1103] Step 2: Start collecting data

[1104] The server starts collecting data based on the name of the proposed company received from the user.

[1105] Specific operation:

[1106] 1. Official website scraping: Access the official website of Company A and collect “Business Overview”, “Product Information”, and “Latest News”.

[1107] 2. Obtaining data from external databases: Use APIs to obtain market data and customer reviews from competitors "Company B" and "Company C."

[1108] 3. Collect information on your company's products from an internal database: Access the database within the system to obtain detailed information on your company's "Product X" and data on past advertising campaigns.

[1109] Input: Name of the proposed company (e.g. Company A), API of external database, query of internal database

[1110] Output: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[1111] Step 3: Data analysis

[1112] The server analyzes the collected data and extracts key keywords, competitive comparisons, and the benefits of the company's products.

[1113] Specific operation:

[1114] 1. Keyword extraction using NLP technology: Extract keywords such as "high quality" and "reliability" from Company A's business activities.

[1115] 2. Conduct competitive comparisons: Compare data from companies B and C with company A's data to identify competitive advantages.

[1116] 3. Analysis of the benefits of your company's products: Analyze how your company's product X's "high performance" and "excellent after-sales service" will work to your advantage in Company A's market.

[1117] Input: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[1118] Output: Keyword list, competitor comparison results, merit list of your company's products

[1119] Step 4: Automatic generation of advertising content

[1120] The server automatically generates advertising content based on the analysis results.

[1121] Specific operation:

[1122] 1. Keyword selection: Select selling points such as "high-quality products" from the analysis data.

[1123] 2. Place on banner template: Insert the message "Experience our high-quality products now!" into the template and place Company A's logo.

[1124] 3. Check the output: Create a preview of the ad banner and check the consistency of the text and design.

[1125] Input: Keyword list, template, company logo

[1126] Output: Advertising banner

[1127] Step 5: Recommend ad destinations

[1128] The server analyzes past advertising performance data and the latest market data to recommend the optimal advertising destinations.

[1129] Specific operation:

[1130] 1. Performance data collection: Extract past advertising data from the database.

[1131] 2. Analysis using machine learning models: Using machine learning models, we identify platforms (e.g., LINE) with high advertising engagement in specific industries.

[1132] 3. Proposal for ad distribution destination: Recommend ad distribution via LINE as the optimal ad distribution destination for Company A.

[1133] Input: Historical advertising performance data, latest market data

[1134] Output: Optimal ad distribution platform (e.g. LINE)

[1135] Step 6: Effectiveness analysis

[1136] The server collects performance data from the advertising platform during the advertising campaign and analyzes the effectiveness.

[1137] Specific operation:

[1138] 1. Use of API: Obtain click counts, conversion rates, and target demographic attribute data from advertising platforms (e.g., LINE API).

[1139] 2. Data aggregation and analysis: The acquired data is aggregated to derive high engagement among the target demographic (e.g., women aged 25-35).

[1140] 3. Visualization of results: Visualize the analysis results in graphs and charts to make them easy for users to understand.

[1141] Input: Ad performance data

[1142] Output: Effectiveness analysis results (e.g., high engagement among women aged 25-35)

[1143] Step 7: Automatic generation of proposal materials and approaches

[1144] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approach methods optimized for the target audience and provides them to the user.

[1145] Specific operation:

[1146] 1. Creating presentation materials: Automatically generate presentation materials that demonstrate marketing strategies targeting women aged 25-35.

[1147] 2. Proposal of approach method: Include in the proposal a specific approach method that combines social media campaigns and email marketing.

[1148] 3. Preview the final document: Review the generated proposal and make any necessary adjustments.

[1149] Input: Effect analysis results, template

[1150] Output: Proposal materials and specific approach

[1151] In this way, this system efficiently automates a series of processes, from detailed data collection and analysis, to ad generation, distribution recommendations, effectiveness analysis, and proposal document creation, and provides them to users.

[1152] (Application example 1)

[1153] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1154] In conventional internet advertising, business analysis, creation of advertising content, selection of distribution destinations, and effectiveness analysis are all done manually, which is time-consuming, labor-intensive, and inefficient.In addition, it is difficult to quickly make presentations and advertising proposals to client companies on-site, which places a heavy burden on sales representatives.

[1155] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1156] In this invention, the server includes means for collecting data on the business details of the proposal recipient company, information on competing companies, and data on the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating banner advertising content based on the analysis results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results, and means for displaying proposal recipient company information, advertising content previews, recommended advertising destinations, and success rates using a portable display device. This automates the entire process from data collection and analysis, advertising content generation, advertising delivery recommendations, effectiveness analysis, and proposal material creation, and makes it possible to display related information on-site in real time.

[1157] "Business details of the proposed company" refers to basic information and activities of the business being proposed to.

[1158] "Competitor information" refers to information about other companies operating in the same market, including competitor product information, strategies, strengths and weaknesses.

[1159] "Data on our own products" refers to detailed information about the products and services we handle, including product features, prices, and past sales data.

[1160] "Collection methods" refers to the methods and tools used to obtain the required data from the internet or other sources, and may include web scraping techniques and APIs.

[1161] "Means of analyzing data" refers to methods and technologies for understanding and analyzing collected information, such as natural language processing technology and machine learning models.

[1162] "Means for automatically generating banner advertising content" refers to technology for automatically generating advertising banners based on collected and analyzed data.

[1163] "Means for recommending ad delivery destinations" refers to methods and tools for selecting and proposing optimal ad delivery destinations, including machine learning models that use past ad performance data and market trends.

[1164] "Performance data" refers to metrics and data used to measure the effectiveness of advertising campaigns, such as the number of ad clicks and conversion rates.

[1165] "Means for automatically generating proposal materials and approaches" refers to technology that automatically generates proposal materials and approaches optimized for the target audience based on the analysis results.

[1166] "Portable display device" refers to a portable display device, such as smart glasses.

[1167] "Ad content preview" refers to the function or process that allows you to check the content of the advertisement that will actually be displayed in advance.

[1168] "Probability of success" refers to a statistical metric used to indicate the likelihood of success of a proposal or advertising campaign.

[1169] The present invention provides a system for supporting advertising sales, and a specific embodiment thereof will be described. This system uses a portable display device to present various data in real time, enabling effective advertising proposals. Here, the description will focus on an embodiment using smart glasses.

[1170] First, the server collects data on the client company's business, competitors, and the client's products. Data collection is done using web scraping technology and data acquisition via API. Specific tools used include BeautifulSoup and Scrapy.

[1171] The collected data is then analyzed on a server. This analysis uses natural language processing (NLP) technology to extract keywords and information for competitive comparison. Software libraries used include spaCy and NLTK. Based on the analysis, the company's strengths and weaknesses, as well as the competitive advantages of its products, are revealed.

[1172] Based on the results of this analysis, the server automatically generates banner ad content. An ad template engine is used to generate the ads. Specifically, a template engine such as Jinja2 is used. For example, a banner ad containing the message "Experience our high-quality products now!" is generated and placed alongside the logo of the proposed company.

[1173] The server then uses a machine learning model to recommend the optimal ad delivery destination. By analyzing past ad performance data and market trends, it proposes the optimal platform and media. Specifically, it often uses TensorFlow or PyTorch, which are built on Python. For example, it determines "platforms with high user engagement in a specific industry" and recommends them to the proposed destination.

[1174] During the advertising campaign, the server collects and analyzes advertising performance data. This data is obtained via the advertising platform's API (e.g., LINE API). The server analyzes the number of clicks, conversion rate, target demographic attribute data, etc. to derive an effective advertising strategy.

[1175] Finally, based on the effectiveness results, the server automatically generates proposal materials and specific approaches for the target demographic. For example, it creates presentation materials proposing social media campaigns and email marketing as marketing strategies targeting women aged 25-35.

[1176] To support this process, smart glasses are used as portable display devices. The advertising assistant app installed on the smart glasses displays information about the companies being proposed to, previews of advertising content, recommended ad distribution destinations, and success rates in real time. Users can conduct effective sales activities while checking the displayed visual information.

[1177] For example, if a user targets "Company D," the smart glasses will display the following information:

[1178] 1. Company information such as "Company D: Providing high-quality food"

[1179] 2. Preview of the banner ad "Fresh Vegetable Campaign!"

[1180] 3. Recommendations for advertising outlets that say "Instagram advertising is effective"

[1181] 4. Presenting the probability of success: "This proposal has an 85% chance of success."

[1182] An example prompt might look like this:

[1183] "Please create a proposal for Company D. Company D provides high-quality food products and its main competitors are Companies E and F. Company D's main product is fresh vegetables, and past advertising campaigns have been successful on Instagram."

[1184] In this way, the system of the present invention automates the entire process from data collection and analysis, to generating advertising content, recommending advertising delivery, analyzing effectiveness, and creating proposal materials, allowing sales representatives to make advertising proposals efficiently.

[1185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1186] Step 1:

[1187] The server receives the name of the proposed company. The input here is the name of the proposed company specified by the user, and the server starts collecting data based on this.

[1188] Specifically, when a user enters the name of "Company A" into the system, the server uses web scraping technology (e.g., BeautifulSoup) and APIs (e.g., Scrapy) to obtain data on Company A's business activities, information on competitors, and its own products from the company's official website and external databases.

[1189] Step 2:

[1190] The server stores the collected data and prepares it for analysis. The input data is the collected business details, information on competitors, and data on the company's own products.

[1191] Specifically, the server stores data retrieved from web pages and APIs in a database and uses NLP technology to convert it into an analyzable format.

[1192] Step 3:

[1193] The server analyzes the data. The input here is the saved data, and the output is the analysis results (keywords, competitor comparisons, strengths of the company's products, etc.).

[1194] Specifically, the server uses a natural language processing library (such as spaCy or NLTK) to extract the key keywords of the proposed company and its strengths and weaknesses compared to its competitors.

[1195] Step 4:

[1196] The server automatically generates advertising content based on the analysis results. The input is the analysis results, and the output is the generated banner advertising content.

[1197] Specifically, the server uses an advertising template engine (such as Jinja2) to generate a banner ad that reflects the keywords. For example, it creates a banner that includes the message "Experience our high-quality products now!"

[1198] Step 5:

[1199] The server recommends the optimal ad delivery destination. The input is the analysis results and past ad performance data, and the output is the recommended ad delivery destination.

[1200] Specifically, the server uses machine learning models (such as TensorFlow or PyTorch) to predict the optimal distribution destination based on past data, suggesting, for example, "platforms with high user engagement in specific industries."

[1201] Step 6:

[1202] The server collects and analyzes performance data for the advertising campaign, with the input being the data collected during the advertising campaign and the output being the analysis results.

[1203] Specifically, the server collects data such as click counts and conversion rates from the advertising platform (e.g., the LINE API), analyzes it, and identifies the attributes of the target demographic and the effectiveness of the advertisement.

[1204] Step 7:

[1205] The server automatically generates proposal materials and approaches for the target audience based on the analysis results. The input is the results of the effectiveness analysis, and the output is the generated proposal materials and approaches.

[1206] Specifically, the server generates presentation materials showing the optimal marketing strategy for the target audience, proposing a strategy that combines, for example, a social media campaign and email marketing.

[1207] Step 8:

[1208] The device (smart glasses) displays to the user information about the proposed company, a preview of the advertising content, recommendations for ad delivery destinations, and the probability of success. The input is data sent from the server, and the output is the information displayed on the glasses' display.

[1209] Specifically, the advertising assistant app installed on the device presents company information and ad previews as visual information, which the user can use to conduct sales activities.

[1210] The above steps enable a complete process from data collection and analysis, to generating advertising content, recommending distribution, analyzing effectiveness, creating proposal materials, and displaying the results in real time on-site.

[1211] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1212] This invention provides a system that recognizes user emotions and automatically generates customized advertising content based on them by combining an emotion engine with an advertising sales tool. This system realizes more effective advertising by incorporating user emotional information in the process of collecting and analyzing data on the client's business, competitors, and the company's own products.

[1213] Program processing

[1214] Data Collection Process

[1215] The server receives an information gathering request by having the user input the name of the proposed company into the interface, and then collects data on the company's business operations, competitors, and products from the company's official website, external databases, market reports, etc. The data is obtained using web scraping technology and APIs.

[1216] Examples:

[1217] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[1218] Data Analysis Process

[1219] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[1220] Examples:

[1221] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1222] User emotion recognition process by emotion engine

[1223] The server uses an emotion engine to recognize the user's emotions in real time based on the information provided by the user, thereby understanding the user's current psychological state and reflecting it in the analysis data.

[1224] Examples:

[1225] As users set up their ad campaigns, the emotion engine detects tension or excitement from their facial expressions and tone of voice, and if the user feels stressed, the system will recommend easier, more intuitive actions.

[1226] Automated advertising content creation process

[1227] Based on the analysis results and the recognition results of the emotion engine, the server automatically generates advertising content. Specifically, it selects appropriate keywords and creates banner ads based on templates. The tone and message of the ad are also adjusted according to the user's emotions.

[1228] Examples:

[1229] The server generates a banner ad with the message "Experience our quality products now!" and places it alongside the logo of Company A. If the user is excited, it uses more powerful fonts and colors.

[1230] Advertising destination recommendation process

[1231] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[1232] Examples:

[1233] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[1234] Effects analysis process

[1235] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1236] Examples:

[1237] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1238] Automatic generation process for proposal materials and approach methods

[1239] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and specific approaches optimized for the target demographic and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1240] Examples:

[1241] Sarver creates a presentation that outlines a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users are nervous, he uses visually appealing infographics.

[1242] In this way, the system of the present invention automates processes that incorporate user emotional information, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, thereby realizing more efficient advertising operations and more effective marketing.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] The user enters the name of the proposed company into the interface and sends an information gathering request.

[1246] For example, enter "Company A" and press the Start Collection button.

[1247] Step 2:

[1248] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[1249] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[1250] Step 3:

[1251] The server retrieves competitor information from external databases and market reports via API.

[1252] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[1253] Step 4:

[1254] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[1255] For example, obtain detailed specifications and past sales data for your company's "Product X."

[1256] Step 5:

[1257] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[1258] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[1259] Step 6:

[1260] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[1261] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[1262] Step 7:

[1263] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[1264] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[1265] Step 8:

[1266] The server uses an emotion engine to recognize the user's emotions in real time.

[1267] For example, emotions are detected from the user's facial expressions and tone of voice.

[1268] Step 9:

[1269] The server selects keywords to be used for banner ads based on the results of data analysis and user emotional data.

[1270] For example, we recommend the message, "Experience our high-quality products now!"

[1271] Step 10:

[1272] The server selects an appropriate design from pre-designed ad templates.

[1273] For example, choose a simple and elegant design to emphasize high quality.

[1274] Step 11:

[1275] The server combines the selected keywords with templates to automatically generate banner ads.

[1276] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[1277] Step 12:

[1278] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[1279] For example, determine whether LINE user engagement is high in a particular industry.

[1280] Step 13:

[1281] The server selects the most suitable advertising platform and recommends it to the device.

[1282] For example, we recommend that Company A distribute advertisements via LINE.

[1283] Step 14:

[1284] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[1285] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[1286] Step 15:

[1287] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[1288] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[1289] Step 16:

[1290] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[1291] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1292] Example 2

[1293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1294] Conventional advertising sales tools do not take user emotions into account when collecting and analyzing client company information and competitor data, which means that advertising content is not effectively targeted. This results in poor advertising performance and limited effectiveness of marketing strategies.

[1295] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the business activities of proposal recipients, information on competing companies, and the company's own products, means for analyzing the collected data and performing keyword and competitive comparisons, means for recognizing user emotions in real time and reflecting them in the analysis results, means for automatically generating advertising content based on the analysis results and emotion recognition data, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results and emotion recognition data. This makes it possible to generate effective advertising content that incorporates user emotional information and optimize marketing strategies.

[1296] "Business details of the proposed company" is an outline of the main business and activities of the company that will be collected and analyzed.

[1297] "Competitor information" is data about other companies competing in the same market as the company to which the proposal is being made.

[1298] "Data on our own products" refers to information about the products and services offered by the company making the proposal.

[1299] "User emotion" refers to the psychological state of the user as recognized from the facial expression, tone of voice, etc. of the user using the system.

[1300] "Analysis results" refer to the results of analyzing collected data using natural language processing technology, etc.

[1301] "Advertising content" refers to promotional text, images, banners, etc. that are generated based on the analysis results.

[1302] "Advertising destination" refers to the medium or platform on which the generated advertisement is displayed.

[1303] "Performance data" refers to data that indicates the effectiveness of an advertisement, such as the number of clicks and conversion rate after the advertisement is delivered.

[1304] "Proposal materials" are presentation materials and reports created based on marketing strategies and advertising content.

[1305] "Approach" refers to the optimal marketing strategy or method for the target audience.

[1306] "Web scraping technology" is a technology that automatically obtains data from websites.

[1307] An "API" is an interface that allows different software systems to communicate with each other.

[1308] "Natural language processing technology" is a technology that allows computers to understand and analyze text written in natural language.

[1309] "Emotion recognition means" refers to a technique or device for recognizing a user's emotions from facial expressions, tone of voice, and the like.

[1310] "Real-time" refers to processing occurring immediately after a user action is taken.

[1311] The present invention relates to a system that automatically generates customized advertising content by incorporating user emotional information during the process of collecting and analyzing data on the client's business, competitors, and the company's own products. To specifically implement this system, the following methods and processes are used.

[1312] Hardware and software used

[1313] server:

[1314] You need a server that can run web scraping technologies and APIs to gather information from multiple data sources (official websites, external databases, market reports).

[1315] A server equipped with a high-performance processor and GPU is also required to run the emotion engine.

[1316] Device:

[1317] A personal computer or tablet device that can run an interface (web browser or dedicated application) for users to input data is required.

[1318] A device equipped with a camera and microphone is required, and real-time emotional recognition of the user's facial expressions and voice is required.

[1319] Main technologies used:

[1320] Web scraping technologies (e.g., Beautiful Soup, Selenium)

[1321] API (e.g. RESTful API)

[1322] Natural language processing technology (e.g., Python's NLTK, SpaCy)

[1323] Emotion recognition technology (e.g. OpenCV, TensorFlow)

[1324] Machine learning models (e.g., Scikit-learn, TensorFlow)

[1325] System operation process

[1326] Data collection process:

[1327] When the server receives the name of the company from the user, it collects data on the company's business, competitors, and products from the company's official website, external databases, and market reports, using web scraping technology and APIs.

[1328] Examples:

[1329] When a user specifies "Company A," the server collects "Business Overview," "Product Information," and "Latest News" from Company A's official website. It also collects market data and customer reviews for competitors "Company B" and "Company C." It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[1330] Data analysis process:

[1331] The data collected by the server is analyzed using natural language processing technology, automatically extracting key keywords such as the client company's business, competitor comparisons, and the benefits of the company's products.

[1332] Examples:

[1333] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness," but Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1334] Emotion Recognition Process:

[1335] Using the emotion engine, the server analyzes the user's facial expressions and tone of voice to recognize emotions in real time, allowing it to understand the user's current psychological state and reflect it in data analysis.

[1336] Examples:

[1337] As users set up their ad campaigns, an emotion engine detects tension or excitement from their facial expressions and tone of voice. If they are feeling stressed, the system recommends easier, more intuitive actions.

[1338] Advertising content generation process:

[1339] Based on the analysis and emotion recognition results, the server selects appropriate keywords and automatically generates banner ads based on templates. The tone and message of the ads are also fine-tuned according to the user's emotions.

[1340] Examples:

[1341] A banner ad containing the message "Experience our quality products now!" is generated and placed alongside the logo of Company A. If the user is excited, stronger fonts and colors are used.

[1342] Ad destination recommendation process:

[1343] The server analyzes past advertising performance data and market data, and uses machine learning models to recommend optimal ad delivery destinations.

[1344] Examples:

[1345] The server analyzes past advertising data, determines that LINE users in a particular market have high engagement, and recommends that Company A distribute advertisements via LINE.

[1346] Effectiveness analysis process:

[1347] During the advertising campaign, the server collects performance data from each advertising platform and automatically analyzes its effectiveness.

[1348] Examples:

[1349] The server obtains the number of clicks, conversion rate, and target demographic data for Company A's banner ads from the LINE API, and derives the result that the click rate was particularly high among women aged 25-35.

[1350] Proposal and approach generation process:

[1351] Based on the results of the effectiveness analysis and emotion recognition data, the server automatically generates proposal materials and specific approaches for the target audience.

[1352] Examples:

[1353] Sarver created a presentation to outline a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users were nervous, he used visual infographics to help them understand.

[1354] In this way, the system of the present invention can automate processes that incorporate user emotional information, making it possible to improve the efficiency of advertising operations and achieve effective marketing.

[1355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1356] Step 1: Processing information collection requests

[1357] The user inputs the name of the company to which the proposal is to be submitted into the interface, and this input is sent to the server via the terminal.

[1358] Specific behavior and output:

[1359] When the user enters "Company A" and clicks the "Collect Information" button, the data is sent to the server, which then stores the received company name as data required for the next process.

[1360] Step 2: Data collection

[1361] Based on the name of the proposed company, the server uses web scraping technology and APIs to collect information from external databases and market reports.

[1362] Specific operations and inputs / outputs:

[1363] The server accesses the official website of Company A and scrapes "Business Overview," "Product Information," and "Latest News." It then retrieves market data and customer reviews of competitors B and C from an external database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from an internal database. These data are input and output as collected information.

[1364] Step 3: Data analysis

[1365] The server analyzes the collected data using natural language processing technology to extract the main keywords of the proposed company, comparisons with competitors, and the benefits of the company's products.

[1366] Specific operations and inputs / outputs:

[1367] The server takes the collected data as input and performs natural language processing. For example, it extracts keywords such as "high quality" and "reliability" from Company A's business activities and compares them with Company B's "price competitiveness," and concludes that Company A has an advantage in "quality." It also derives that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service." The analysis results are output.

[1368] Step 4: Emotion Recognition

[1369] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing their emotions in real time. This recognition data is reflected in the analysis data.

[1370] Specific operations and inputs / outputs:

[1371] When a user sets up an advertising campaign, video and audio data from the device's camera and microphone are sent to the server. The emotion engine analyzes this data and determines whether the user is nervous or excited. The recognized emotion data is output and used as input for the next step.

[1372] Step 5: Generate advertising content

[1373] The server automatically generates advertising content based on the analysis results and emotion recognition data, using appropriate keyword selection and templates.

[1374] Specific operations and inputs / outputs:

[1375] The server takes the analysis results and the user's emotional data as input and generates a banner ad with the message "Experience our high-quality product now!" The ad also includes the logo of Company A. If the user is excited, bold fonts and vivid colors are used. The generated ad banner is output.

[1376] Step 6: Recommend ad destinations

[1377] The server analyzes historical advertising performance data and the latest market data and uses machine learning models to recommend optimal ad delivery destinations.

[1378] Specific operations and inputs / outputs:

[1379] The server inputs past advertising data and market data into a machine learning model. For example, it discovers that user engagement on LINE is high, and recommends that Company A distribute ads on LINE. Recommendation information is output.

[1380] Step 7: Analyze the effectiveness of your advertising campaign

[1381] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness.

[1382] Specific operations and inputs / outputs:

[1383] The server analyzes the number of clicks, conversion rate, and target demographic attribute data obtained from the LINE API, etc. For example, it may find that the click rate is particularly high among women aged 25-35. This analysis result is then output.

[1384] Step 8: Generate proposals and approaches

[1385] The server automatically generates proposal materials and specific approaches for the target audience based on the results of the effectiveness analysis and emotion recognition data.

[1386] Specific operations and inputs / outputs:

[1387] The server takes the analysis results and sentiment data as input and creates presentation materials showing marketing strategies for women aged 25-35. It also provides effective approaches that combine social media campaigns and email marketing. If the user is nervous, it makes extensive use of visually easy-to-understand infographics. The generated proposal materials and approach methods are output.

[1388] (Application example 2)

[1389] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1390] Conventional advertising sales tools only collect and analyze information on the business activities of clients and competitors, but do not generate advertising content that takes into account the emotions and psychological state of users. As a result, advertising effectiveness is not maximized, and it takes time and effort to create an optimal advertising strategy.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1392] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and performing keyword and competitor comparisons, means for recognizing user emotions and reflecting the emotional information in real time, means for automatically generating banner advertising content based on the analysis results and emotion recognition results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing effectiveness, and means for automatically generating proposal materials and approach methods for target demographics based on the analyzed effectiveness results and emotion recognition results. This makes it possible to generate advertising content that takes user emotions into consideration and to quickly plan optimal advertising strategies.

[1393] "Business details of the proposed company" refers to the business details, business overview, and information on the products offered by the company to which the advertisement is being proposed.

[1394] "Competitor information" refers to data on other companies competing in the same market as the target company, as well as insights such as market share and product lineup.

[1395] "Data on your own products" refers to detailed information about the products and services your company offers, their performance, past sales performance, and marketing data.

[1396] "Means of collection" refers to the methods and technologies used to gather data and information via the Internet, etc.

[1397] "Means of analysis" refers to methods and techniques for analyzing collected data and extracting important keywords and features.

[1398] "Keyword and Competitive Comparison" refers to the process of identifying important words and phrases related to the client company's business and using them to evaluate the differences and advantages of the client company compared to its competitors.

[1399] "Means of recognizing user emotions and reflecting that emotional information in real time" refers to a method of using emotion recognition technology to determine a user's psychological state in real time from their facial expressions, tone of voice, etc., and reflecting the results in advertisement generation and analysis.

[1400] "Means for automatically generating banner advertisement content" refers to a technology for automatically creating banner-style advertisements based on collected and analyzed information and user sentiment results.

[1401] "Means for recommending optimal ad delivery destinations" refers to a method of suggesting to users the platforms and media that are predicted to deliver ads most effectively, based on past performance data of ads and market data.

[1402] "Means for collecting advertising performance data and analyzing its effectiveness" refers to methods for collecting data such as the number of clicks and conversion rate obtained during the advertising campaign period and analyzing its effectiveness.

[1403] "Means for automatically generating proposal materials and approaches" refers to technology that automatically creates marketing materials and specific advertising strategies specific to the target audience based on analyzed data and emotion recognition results.

[1404] In this invention, an automatic advertising content generation system that incorporates user emotions is realized through the following steps.

[1405] First, the user enters information such as the name of the company to which they are making a proposal, their advertising objectives, and their target user demographic through the interface. Based on this information, the server collects data on the business activities of the company to which they are making a proposal, information on their competitors, and their own products. Data collection is done using web scraping technology and APIs (e.g., Google API, Facebook API).

[1406] The collected data is analyzed using natural language processing (NLP) technology. This analysis extracts the client company's key keywords, comparison information with competitors, and the advantages of the company's products. The server then builds the basis for advertising content based on the analyzed information.

[1407] Next, the smartphone's camera and microphone are used to recognize the user's emotions in real time. The emotion engine uses Google Cloud Vision and Amazon Rekognition to analyze emotions from the user's facial expressions and tone of voice. This emotional information is reflected in ad generation, automatically generating ad content that matches the user's psychological state.

[1408] To automatically generate advertising content, appropriate keywords and messages are selected, and banner ads are created based on templates. For example, Canva API is used to customize design templates and adjust colors and fonts based on emotions.

[1409] Recommendations for optimal ad delivery are based on past ad performance data and market data. The server uses machine learning models to predict and recommend the most effective ad platform. For example, social media platforms such as LINE and Instagram may be selected.

[1410] During the advertising campaign, the server collects performance data from the advertising platform and automatically analyzes its effectiveness. The analysis results are visualized on a dashboard and provided to users. Analysis tools used include Google Analytics and Mixpanel.

[1411] Furthermore, the server automatically generates proposal materials and specific approaches for the target audience based on the analysis and emotion recognition results. Marketing materials and advertising strategy proposals can make extensive use of visually easy-to-understand infographics.

[1412] As a concrete example, consider a food company that wants to advertise a new, high-quality gourmet food product. By entering the company's name and the purpose "gourmet food advertising" into the app, the system collects and analyzes the company's information. At the same time, as the user sets up an advertising campaign, the emotional engine recognizes the user's emotional state. Based on this, an advertising banner emphasizing the product's high quality is generated, and advertising is recommended on social media platforms that are predicted to be particularly popular, such as Instagram.

[1413] An example prompt for a generative AI model might look like this:

[1414] Collect data and generate advertising content for the following companies' advertising campaigns:

[1415] Company name:XX Food

[1416] Advertising objective: Introducing a new line of high-quality gourmet food

[1417] Target market: Young people's food market

[1418] Competitors: YY Foods, ZZ Foods

[1419] Include ad effectiveness analysis based on user sentiment.

[1420] In this way, it becomes possible to automatically generate advertising content that incorporates user emotions and to quickly develop marketing strategies.

[1421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1422] Step 1:

[1423] The user inputs information such as the name of the company to be proposed to, advertising purpose, and target user demographics into the interface via their device. The input information is sent to the server. The input data includes the company name, advertising purpose, target demographics, etc., and subsequent data collection and analysis are based on this.

[1424] Step 2:

[1425] Based on the input name of the proposed company, the server uses web scraping technology and APIs (e.g., Google API, Facebook API) to collect data on the company's business details, competitor information, and company merchandise from the company's official website, external databases, market reports, etc. This allows for the acquisition of company overviews, product information, and market data.

[1426] Step 3:

[1427] The server analyzes the collected data using natural language processing technology. During the analysis process, the company's key keywords, comparisons with competitors, and the benefits of its products are extracted. This processing is performed using Python NLP libraries (e.g., spaCy, NLTK). The input data is the collected text information, and the output is the extracted keywords and competitive comparison information.

[1428] Step 4:

[1429] The server uses the smartphone's camera and microphone to recognize the user's emotions in real time. This recognition utilizes the emotion recognition functions of Google Cloud Vision and Amazon Rekognition. The input data is the user's facial expression images and voice data, and the output is analyzed emotional information. Based on this, the user's psychological state is evaluated.

[1430] Step 5:

[1431] Based on the analysis and emotion recognition results, the server automatically generates advertising content. Specifically, it selects appropriate keywords and messages and creates template-based banner ads using the Canva API. The input data includes the analysis results and emotion information, and the output is the completed banner ad.

[1432] Step 6:

[1433] The server recommends the optimal ad placement based on past ad performance data and market data. It uses a machine learning model to predict the most effective ad platform and provides recommendations to users. This process uses Python machine learning libraries (e.g., scikit-learn), with the input being past performance data and the output being recommended placements.

[1434] Step 7:

[1435] During the advertising campaign, the server collects performance data from the advertising platforms and analyzes their effectiveness. The analysis is performed using Google Analytics and Mixpanel, with the input data being the performance data obtained from each platform and the output being a dashboard of the analysis results.

[1436] Step 8:

[1437] The server automatically generates proposal materials and approaches for the target demographic based on the analyzed data and emotion recognition results. These materials include specific marketing strategies and advertising approaches. The input data are the analysis results and emotion information, and the output is proposal materials.

[1438] This makes it possible to generate advertising content that takes user emotions into consideration and quickly develop optimal advertising strategies.

[1439] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1440] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1441] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1442] [Fourth embodiment]

[1443] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1444] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1446] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1450] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1451] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1452] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1453] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1454] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1455] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1456] The present invention provides an advertising sales tool designed to improve the efficiency and effectiveness of Internet advertising operations. Specifically, the system collects data on clients' business activities, competitors, and the company's own products, analyzes this data, automatically generates advertising content, recommends optimal ad delivery destinations, and analyzes the effectiveness of advertising.

[1457] Program processing

[1458] Data Collection Process

[1459] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[1460] Examples:

[1461] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[1462] Data Analysis Process

[1463] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[1464] Examples:

[1465] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1466] Automated advertising content creation process

[1467] Based on the analysis results, the server automatically generates advertising content, specifically by selecting appropriate keywords and creating banner ads based on templates.

[1468] Examples:

[1469] The server generates a banner ad containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[1470] Advertising destination recommendation process

[1471] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[1472] Examples:

[1473] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[1474] Effects analysis process

[1475] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1476] Examples:

[1477] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1478] Automatic generation process for proposal materials and approach methods

[1479] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1480] Examples:

[1481] Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, and will propose an approach that combines social media campaigns and email marketing.

[1482] In this way, the system of the present invention highly automates all advertising operations, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, in order to maximize advertising effectiveness.

[1483] The processing flow will be explained below.

[1484] Step 1:

[1485] The user enters the name of the proposed company into the interface and sends an information gathering request.

[1486] For example, enter "Company A" and press the Start Collection button.

[1487] Step 2:

[1488] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[1489] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[1490] Step 3:

[1491] The server retrieves competitor information from external databases and market reports via API.

[1492] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[1493] Step 4:

[1494] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[1495] For example, obtain detailed specifications and past sales data for your company's "Product X."

[1496] Step 5:

[1497] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[1498] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[1499] Step 6:

[1500] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[1501] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[1502] Step 7:

[1503] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[1504] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[1505] Step 8:

[1506] The server selects keywords to be used for banner ads based on the results of data analysis.

[1507] For example, identify the main message to use in your banner: "Experience our quality products now!"

[1508] Step 9:

[1509] The server selects an appropriate design from pre-designed ad templates.

[1510] For example, choose a simple and elegant design to emphasize high quality.

[1511] Step 10:

[1512] The server combines the selected keywords with templates to automatically generate banner ads.

[1513] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[1514] Step 11:

[1515] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[1516] For example, determine whether LINE user engagement is high in a particular industry.

[1517] Step 12:

[1518] The server selects the most suitable advertising platform and recommends it to the device.

[1519] For example, we recommend that Company A distribute advertisements via LINE.

[1520] Step 13:

[1521] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[1522] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[1523] Step 14:

[1524] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[1525] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[1526] Step 15:

[1527] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[1528] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1529] Example 1

[1530] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1531] In conventional internet advertising, the processes of ad proposal, creation, distribution, effectiveness analysis, and optimization are all performed separately, resulting in inefficiency and requiring a lot of time and effort. Furthermore, there are issues with the accuracy of data collection and analysis, and the appropriateness of ad distribution, making it difficult to maximize advertising effectiveness. To solve these problems, a highly automated system that centralizes all advertising operations is required.

[1532] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1533] In this invention, the server includes means for collecting data on the business activities of clients, information on competitors, and the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating advertising content based on the analysis results, means for analyzing past advertising performance data and the latest market data and recommending optimal advertising distribution destinations, means for collecting performance data from advertising platforms during the advertising campaign period and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results. This makes it possible to centrally and efficiently execute the entire process from data collection to analysis, advertising creation, distribution, analysis, and proposals.

[1534] "Business details of the proposed company" refers to information about the business, services, and products that a specific company or organization primarily conducts.

[1535] "Competitor information" is data about other companies or entities operating in the same market or industry as the proposal recipient.

[1536] "Data on your company's products" refers to detailed information about the products and services your company offers.

[1537] "Means of data collection" refers to the technologies or tools used to collect specific information, including, for example, web scraping technologies and APIs.

[1538] "Analyzing the data" is the process of examining the collected information in detail to gain new knowledge and insights.

[1539] "Keyword and Competitive Comparison Tools" are methods for identifying key words and phrases based on collected data and analyzing them against competitors.

[1540] "Means for automatically generating advertising content" refers to technology or software that automatically creates advertising content such as images, text, and videos based on analysis results.

[1541] "Historical advertising performance data" refers to data showing the results of advertising campaigns that have been run to date, including clicks, conversion rates, and engagement.

[1542] "Latest market data" means the latest information on current market movements and trends.

[1543] "Means for recommending optimal ad delivery destinations" refers to technologies and methods that, based on analyzed data, suggest to users the optimal platforms and media for effectively delivering advertisements.

[1544] "Collecting performance data from advertising platforms during the advertising campaign" refers to the process of obtaining data regarding the performance of the advertisement from each advertising platform during the period the advertisement is running.

[1545] "Means for analyzing effectiveness" means a method for analyzing collected advertising performance data and evaluating the success of the advertising.

[1546] "Analyzed effectiveness results" are the specific results and evaluation results obtained after analyzing advertising performance data.

[1547] "Means for automatically generating proposal materials and approaches for target demographics" refers to technology or software that automatically creates proposal materials and specific marketing approaches suitable for a specific target demographic based on the analyzed results.

[1548] The present invention is a system configured to provide an advertising sales tool and to improve the efficiency and effectiveness of Internet advertising operations. Specific program processing and embodiments thereof will be described in detail below.

[1549] This system involves a series of processes: collecting data on the client's business, information on competitors, and the company's own products, analyzing this data to automatically generate advertising content, recommending optimal ad distribution destinations, and analyzing the effectiveness of the advertising. Based on the results of the effectiveness analysis, it then automatically generates proposal materials and specific approach methods optimized for the target demographic.

[1550] When the server receives the name of a proposed company from the user, it first collects data on the company's business, competitors, and products from the company's official website, external databases, market reports, etc. This is done using web scraping technology and data acquisition via API.

[1551] As a concrete example, when a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also obtains market data and customer reviews for competitors "Company B" and "Company C" from an external database. It also obtains detailed information about the company's "Product X" and past advertising campaign data from an internal database.

[1552] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[1553] As a concrete example, the server extracts keywords such as "high quality" and "reliability" from Company A's business activities, and analyzes that Company B has an advantage in "price competitiveness," while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1554] The server automatically generates advertising content based on the analysis results. Specifically, it selects appropriate keywords and creates banner ads based on templates.

[1555] As a specific example, the server generates a banner advertisement containing the message "Experience our high-quality products now!" and places it along with Company A's logo.

[1556] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[1557] As a specific example, the server determines from past advertising data that LINE user engagement is high in a specific industry and recommends that Company A distribute advertisements via LINE.

[1558] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1559] As a specific example, the server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1560] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approaches optimized for the target audience and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1561] As a concrete example, Server will create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1562] To demonstrate the features of this system, a generative AI model is used to generate advertising content. Below are examples of prompts that users can use to instruct the generative AI model:

[1563] Example prompt sentence:

[1564] Create an advertising banner to showcase Company A's high-quality products. Focus on the following points:

[1565] Reliability

[1566] high performance

[1567] Comprehensive after-sales service

[1568] Company A's logo should also be included in the banner.

[1569] In this way, the system according to the present invention centrally and efficiently executes the entire process from data collection and analysis, to advertisement creation, distribution, analysis, and proposals, aiming to improve the efficiency and effectiveness of advertising operations.

[1570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1571] Step 1: Enter the name of the company you are proposing to

[1572] The user accesses the system interface and inputs the name of the company to which the proposal is to be made.

[1573] Specific operation: The user enters "Company A" in the input field and clicks the Start Data Collection button.

[1574] Input: Name of the proposed company (e.g. Company A)

[1575] Output: Send the name of the proposed company to the server

[1576] Step 2: Start collecting data

[1577] The server starts collecting data based on the name of the proposed company received from the user.

[1578] Specific operation:

[1579] 1. Official website scraping: Access the official website of Company A and collect “Business Overview”, “Product Information”, and “Latest News”.

[1580] 2. Obtaining data from external databases: Use APIs to obtain market data and customer reviews from competitors "Company B" and "Company C."

[1581] 3. Collect information on your company's products from an internal database: Access the database within the system to obtain detailed information on your company's "Product X" and data on past advertising campaigns.

[1582] Input: Name of the proposed company (e.g. Company A), API of external database, query of internal database

[1583] Output: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[1584] Step 3: Data analysis

[1585] The server analyzes the collected data and extracts key keywords, competitive comparisons, and the benefits of the company's products.

[1586] Specific operation:

[1587] 1. Keyword extraction using NLP technology: Extract keywords such as "high quality" and "reliability" from Company A's business activities.

[1588] 2. Conduct competitive comparisons: Compare data from companies B and C with company A's data to identify competitive advantages.

[1589] 3. Analysis of the benefits of your company's products: Analyze how your company's product X's "high performance" and "excellent after-sales service" will work to your advantage in Company A's market.

[1590] Input: Data set of the proposed company (Company A), dataset of competitors (Company B, Company C), dataset of the company's own product (Product X)

[1591] Output: Keyword list, competitor comparison results, merit list of your company's products

[1592] Step 4: Automatic generation of advertising content

[1593] The server automatically generates advertising content based on the analysis results.

[1594] Specific operation:

[1595] 1. Keyword selection: Select selling points such as "high-quality products" from the analysis data.

[1596] 2. Place on banner template: Insert the message "Experience our high-quality products now!" into the template and place Company A's logo.

[1597] 3. Check the output: Create a preview of the ad banner and check the consistency of the text and design.

[1598] Input: Keyword list, template, company logo

[1599] Output: Advertising banner

[1600] Step 5: Recommend ad destinations

[1601] The server analyzes past advertising performance data and the latest market data to recommend the optimal advertising destinations.

[1602] Specific operation:

[1603] 1. Performance data collection: Extract past advertising data from the database.

[1604] 2. Analysis using machine learning models: Using machine learning models, we identify platforms (e.g., LINE) with high advertising engagement in specific industries.

[1605] 3. Proposal for ad distribution destination: Recommend ad distribution via LINE as the optimal ad distribution destination for Company A.

[1606] Input: Historical advertising performance data, latest market data

[1607] Output: Optimal ad distribution platform (e.g. LINE)

[1608] Step 6: Effectiveness analysis

[1609] The server collects performance data from the advertising platform during the advertising campaign and analyzes the effectiveness.

[1610] Specific operation:

[1611] 1. Use of API: Obtain click counts, conversion rates, and target demographic attribute data from advertising platforms (e.g., LINE API).

[1612] 2. Data aggregation and analysis: The acquired data is aggregated to derive high engagement among the target demographic (e.g., women aged 25-35).

[1613] 3. Visualization of results: Visualize the analysis results in graphs and charts to make them easy for users to understand.

[1614] Input: Ad performance data

[1615] Output: Effectiveness analysis results (e.g., high engagement among women aged 25-35)

[1616] Step 7: Automatic generation of proposal materials and approaches

[1617] Based on the results of the effectiveness analysis, the server automatically generates proposal materials and specific approach methods optimized for the target audience and provides them to the user.

[1618] Specific operation:

[1619] 1. Creating presentation materials: Automatically generate presentation materials that demonstrate marketing strategies targeting women aged 25-35.

[1620] 2. Proposal of approach method: Include in the proposal a specific approach method that combines social media campaigns and email marketing.

[1621] 3. Preview the final document: Review the generated proposal and make any necessary adjustments.

[1622] Input: Effect analysis results, template

[1623] Output: Proposal materials and specific approach

[1624] In this way, this system efficiently automates a series of processes, from detailed data collection and analysis, to ad generation, distribution recommendations, effectiveness analysis, and proposal document creation, and provides them to users.

[1625] (Application example 1)

[1626] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1627] In conventional internet advertising, business analysis, creation of advertising content, selection of distribution destinations, and effectiveness analysis are all done manually, which is time-consuming, labor-intensive, and inefficient.In addition, it is difficult to quickly make presentations and advertising proposals to client companies on-site, which places a heavy burden on sales representatives.

[1628] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1629] In this invention, the server includes means for collecting data on the business details of the proposal recipient company, information on competing companies, and data on the company's own products, means for analyzing the collected data and comparing keywords and competitors, means for automatically generating banner advertising content based on the analysis results, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results, and means for displaying proposal recipient company information, advertising content previews, recommended advertising destinations, and success rates using a portable display device. This automates the entire process from data collection and analysis, advertising content generation, advertising delivery recommendations, effectiveness analysis, and proposal material creation, and makes it possible to display related information on-site in real time.

[1630] "Business details of the proposed company" refers to basic information and activities of the business being proposed to.

[1631] "Competitor information" refers to information about other companies operating in the same market, including competitor product information, strategies, strengths and weaknesses.

[1632] "Data on our own products" refers to detailed information about the products and services we handle, including product features, prices, and past sales data.

[1633] "Collection methods" refers to the methods and tools used to obtain the required data from the internet or other sources, and may include web scraping techniques and APIs.

[1634] "Means of analyzing data" refers to methods and technologies for understanding and analyzing collected information, such as natural language processing technology and machine learning models.

[1635] "Means for automatically generating banner advertising content" refers to technology for automatically generating advertising banners based on collected and analyzed data.

[1636] "Means for recommending ad delivery destinations" refers to methods and tools for selecting and proposing optimal ad delivery destinations, including machine learning models that use past ad performance data and market trends.

[1637] "Performance data" refers to metrics and data used to measure the effectiveness of advertising campaigns, such as the number of ad clicks and conversion rates.

[1638] "Means for automatically generating proposal materials and approaches" refers to technology that automatically generates proposal materials and approaches optimized for the target audience based on the analysis results.

[1639] "Portable display device" refers to a portable display device, such as smart glasses.

[1640] "Ad content preview" refers to the function or process that allows you to check the content of the advertisement that will actually be displayed in advance.

[1641] "Probability of success" refers to a statistical metric used to indicate the likelihood of success of a proposal or advertising campaign.

[1642] The present invention provides a system for supporting advertising sales, and a specific embodiment thereof will be described. This system uses a portable display device to present various data in real time, enabling effective advertising proposals. Here, the description will focus on an embodiment using smart glasses.

[1643] First, the server collects data on the client company's business, competitors, and the client's products. Data collection is done using web scraping technology and data acquisition via API. Specific tools used include BeautifulSoup and Scrapy.

[1644] The collected data is then analyzed on a server. This analysis uses natural language processing (NLP) technology to extract keywords and information for competitive comparison. Software libraries used include spaCy and NLTK. Based on the analysis, the company's strengths and weaknesses, as well as the competitive advantages of its products, are revealed.

[1645] Based on the results of this analysis, the server automatically generates banner ad content. An ad template engine is used to generate the ads. Specifically, a template engine such as Jinja2 is used. For example, a banner ad containing the message "Experience our high-quality products now!" is generated and placed alongside the logo of the proposed company.

[1646] The server then uses a machine learning model to recommend the optimal ad delivery destination. By analyzing past ad performance data and market trends, it proposes the optimal platform and media. Specifically, it often uses TensorFlow or PyTorch, which are built on Python. For example, it determines "platforms with high user engagement in a specific industry" and recommends them to the proposed destination.

[1647] During the advertising campaign, the server collects and analyzes advertising performance data. This data is obtained via the advertising platform's API (e.g., LINE API). The server analyzes the number of clicks, conversion rate, target demographic attribute data, etc. to derive an effective advertising strategy.

[1648] Finally, based on the effectiveness results, the server automatically generates proposal materials and specific approaches for the target demographic. For example, it creates presentation materials proposing social media campaigns and email marketing as marketing strategies targeting women aged 25-35.

[1649] To support this process, smart glasses are used as portable display devices. The advertising assistant app installed on the smart glasses displays information about the companies being proposed to, previews of advertising content, recommended ad distribution destinations, and success rates in real time. Users can conduct effective sales activities while checking the displayed visual information.

[1650] For example, if a user targets "Company D," the smart glasses will display the following information:

[1651] 1. Company information such as "Company D: Providing high-quality food"

[1652] 2. Preview of the banner ad "Fresh Vegetable Campaign!"

[1653] 3. Recommendations for advertising outlets that say "Instagram advertising is effective"

[1654] 4. Presenting the probability of success: "This proposal has an 85% chance of success."

[1655] An example prompt might look like this:

[1656] "Please create a proposal for Company D. Company D provides high-quality food products and its main competitors are Companies E and F. Company D's main product is fresh vegetables, and past advertising campaigns have been successful on Instagram."

[1657] In this way, the system of the present invention automates the entire process from data collection and analysis, to generating advertising content, recommending advertising delivery, analyzing effectiveness, and creating proposal materials, allowing sales representatives to make advertising proposals efficiently.

[1658] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1659] Step 1:

[1660] The server receives the name of the proposed company. The input here is the name of the proposed company specified by the user, and the server starts collecting data based on this.

[1661] Specifically, when a user enters the name of "Company A" into the system, the server uses web scraping technology (e.g., BeautifulSoup) and APIs (e.g., Scrapy) to obtain data on Company A's business activities, information on competitors, and its own products from the company's official website and external databases.

[1662] Step 2:

[1663] The server stores the collected data and prepares it for analysis. The input data is the collected business details, information on competitors, and data on the company's own products.

[1664] Specifically, the server stores data retrieved from web pages and APIs in a database and uses NLP technology to convert it into an analyzable format.

[1665] Step 3:

[1666] The server analyzes the data. The input here is the saved data, and the output is the analysis results (keywords, competitor comparisons, strengths of the company's products, etc.).

[1667] Specifically, the server uses a natural language processing library (such as spaCy or NLTK) to extract the key keywords of the proposed company and its strengths and weaknesses compared to its competitors.

[1668] Step 4:

[1669] The server automatically generates advertising content based on the analysis results. The input is the analysis results, and the output is the generated banner advertising content.

[1670] Specifically, the server uses an advertising template engine (such as Jinja2) to generate a banner ad that reflects the keywords. For example, it creates a banner that includes the message "Experience our high-quality products now!"

[1671] Step 5:

[1672] The server recommends the optimal ad delivery destination. The input is the analysis results and past ad performance data, and the output is the recommended ad delivery destination.

[1673] Specifically, the server uses machine learning models (such as TensorFlow or PyTorch) to predict the optimal distribution destination based on past data, suggesting, for example, "platforms with high user engagement in specific industries."

[1674] Step 6:

[1675] The server collects and analyzes performance data for the advertising campaign, with the input being the data collected during the advertising campaign and the output being the analysis results.

[1676] Specifically, the server collects data such as click counts and conversion rates from the advertising platform (e.g., the LINE API), analyzes it, and identifies the attributes of the target demographic and the effectiveness of the advertisement.

[1677] Step 7:

[1678] The server automatically generates proposal materials and approaches for the target audience based on the analysis results. The input is the results of the effectiveness analysis, and the output is the generated proposal materials and approaches.

[1679] Specifically, the server generates presentation materials showing the optimal marketing strategy for the target audience, proposing a strategy that combines, for example, a social media campaign and email marketing.

[1680] Step 8:

[1681] The device (smart glasses) displays to the user information about the proposed company, a preview of the advertising content, recommendations for ad delivery destinations, and the probability of success. The input is data sent from the server, and the output is the information displayed on the glasses' display.

[1682] Specifically, the advertising assistant app installed on the device presents company information and ad previews as visual information, which the user can use to conduct sales activities.

[1683] The above steps enable a complete process from data collection and analysis, to generating advertising content, recommending distribution, analyzing effectiveness, creating proposal materials, and displaying the results in real time on-site.

[1684] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1685] This invention provides a system that recognizes user emotions and automatically generates customized advertising content based on them by combining an emotion engine with an advertising sales tool. This system realizes more effective advertising by incorporating user emotional information in the process of collecting and analyzing data on the client's business, competitors, and the company's own products.

[1686] Program processing

[1687] Data Collection Process

[1688] The server receives an information gathering request by having the user input the name of the proposed company into the interface, and then collects data on the company's business operations, competitors, and products from the company's official website, external databases, market reports, etc. The data is obtained using web scraping technology and APIs.

[1689] Examples:

[1690] When a user specifies "Company A," the server accesses Company A's official website and collects "Business Overview," "Product Information," and "Latest News." It also retrieves market data and customer reviews for competitors "Company B" and "Company C" from the database. It also retrieves detailed information about the company's "Product X" and past advertising campaign data from the internal database.

[1691] Data Analysis Process

[1692] The collected data is analyzed by a server, and key keywords for the client's business, competitive comparisons, and the benefits of the client's products are automatically extracted. This analysis uses natural language processing (NLP) technology.

[1693] Examples:

[1694] The server extracts keywords such as "high quality" and "reliability" from Company A's business activities and analyzes that Company B has an advantage in "price competitiveness" while Company A has an advantage in "quality." It also clearly shows that Company A's product X has an advantage in Company A's market due to its "high performance" and "excellent after-sales service."

[1695] User emotion recognition process by emotion engine

[1696] The server uses an emotion engine to recognize the user's emotions in real time based on the information provided by the user, thereby understanding the user's current psychological state and reflecting it in the analysis data.

[1697] Examples:

[1698] As users set up their ad campaigns, the emotion engine detects tension or excitement from their facial expressions and tone of voice, and if the user feels stressed, the system will recommend easier, more intuitive actions.

[1699] Automated advertising content creation process

[1700] Based on the analysis results and the recognition results of the emotion engine, the server automatically generates advertising content. Specifically, it selects appropriate keywords and creates banner ads based on templates. The tone and message of the ad are also adjusted according to the user's emotions.

[1701] Examples:

[1702] The server generates a banner ad with the message "Experience our quality products now!" and places it alongside the logo of Company A. If the user is excited, it uses more powerful fonts and colors.

[1703] Advertising destination recommendation process

[1704] The server analyzes past advertising performance data and the latest market data to recommend optimal ad placements to users. This recommendation is performed using an automated machine learning model.

[1705] Examples:

[1706] The server determines from past advertising data that LINE has high user engagement in a specific industry and recommends that Company A distribute advertisements via LINE.

[1707] Effects analysis process

[1708] The server collects performance data from the advertising platform during the advertising campaign and automatically analyzes its effectiveness. The analysis results are used to measure the attributes of the target audience and the effectiveness of the advertising.

[1709] Examples:

[1710] The server obtains the number of clicks, conversion rate, and target demographic attribute data for Company A's banner ads from the LINE API, and derives the result that the click rate is particularly high among women aged 25-35.

[1711] Automatic generation process for proposal materials and approach methods

[1712] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and specific approaches optimized for the target demographic and provides them to the user, allowing the user to quickly and effectively implement their marketing strategy.

[1713] Examples:

[1714] Sarver creates a presentation that outlines a marketing strategy targeting women aged 25-35, combining social media campaigns and email marketing. If users are nervous, he uses visually appealing infographics.

[1715] In this way, the system of the present invention automates processes that incorporate user emotional information, from data collection and analysis, to generating advertising content, recommending advertising distribution, analyzing effectiveness, and creating proposals, thereby realizing more efficient advertising operations and more effective marketing.

[1716] The processing flow will be explained below.

[1717] Step 1:

[1718] The user enters the name of the proposed company into the interface and sends an information gathering request.

[1719] For example, enter "Company A" and press the Start Collection button.

[1720] Step 2:

[1721] The server accesses the official website of the proposed company and automatically collects business details, product information, etc. using web scraping technology.

[1722] For example, obtain "business overview," "latest news," etc. from Company A's official website.

[1723] Step 3:

[1724] The server retrieves competitor information from external databases and market reports via API.

[1725] For example, obtain market share data and customer reviews of competitors "Company B" and "Company C" from a database.

[1726] Step 4:

[1727] The server retrieves information about the company's products, their features, and past advertising campaign data from an internal database.

[1728] For example, obtain detailed specifications and past sales data for your company's "Product X."

[1729] Step 5:

[1730] The server analyzes the business content collected using natural language processing (NLP) technology and extracts key keywords.

[1731] For example, keywords such as "high quality" and "reliability" are identified from the business activities of "Company A."

[1732] Step 6:

[1733] The server analyzes information on competing companies and automatically extracts points of comparison with the proposed company.

[1734] For example, it can be concluded that Company B has strong "price competitiveness," while Company A has an advantage in "quality."

[1735] Step 7:

[1736] The server analyzes the strengths of the company's products and extracts specific benefits for the companies to which the proposals are made.

[1737] For example, Company A emphasizes the "high performance" and "excellent after-sales service" of its products in its target market.

[1738] Step 8:

[1739] The server uses an emotion engine to recognize the user's emotions in real time.

[1740] For example, emotions are detected from the user's facial expressions and tone of voice.

[1741] Step 9:

[1742] The server selects keywords to be used for banner ads based on the results of data analysis and user emotional data.

[1743] For example, we recommend the message, "Experience our high-quality products now!"

[1744] Step 10:

[1745] The server selects an appropriate design from pre-designed ad templates.

[1746] For example, choose a simple and elegant design to emphasize high quality.

[1747] Step 11:

[1748] The server combines the selected keywords with templates to automatically generate banner ads.

[1749] For example, insert the text "Amazing quality, buy now!" into the banner and place Company A's logo.

[1750] Step 12:

[1751] The server uses machine learning models to evaluate the effectiveness of multiple advertising platforms based on past advertising performance data.

[1752] For example, determine whether LINE user engagement is high in a particular industry.

[1753] Step 13:

[1754] The server selects the most suitable advertising platform and recommends it to the device.

[1755] For example, we recommend that Company A distribute advertisements via LINE.

[1756] Step 14:

[1757] The server automatically collects performance data such as the number of views, clicks, and conversion rates from the advertising platform via API.

[1758] For example, obtain the number of clicks and conversion data for a specific banner from the LINE API.

[1759] Step 15:

[1760] The server measures the effectiveness of the advertisement based on the performance data collected, and evaluates the effectiveness of the advertisement for a specific target demographic.

[1761] For example, you may report that Company A's ads have a particularly high click-through rate among women aged 25-35.

[1762] Step 16:

[1763] Based on the results of the effect analysis and the recognition results of the emotion engine, the server automatically generates proposal materials and approaches suited to the target demographic and presents them to the user.

[1764] For example, you could create a presentation document outlining a marketing strategy targeting women aged 25-35, proposing an approach that combines social media campaigns and email marketing.

[1765] Example 2

[1766] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1767] Conventional advertising sales tools do not take user emotions into account when collecting and analyzing client company information and competitor data, which means that advertising content is not effectively targeted. This results in poor advertising performance and limited effectiveness of marketing strategies.

[1768] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the business activities of proposal recipients, information on competing companies, and the company's own products, means for analyzing the collected data and performing keyword and competitive comparisons, means for recognizing user emotions in real time and reflecting them in the analysis results, means for automatically generating advertising content based on the analysis results and emotion recognition data, means for recommending optimal advertising destinations, means for collecting advertising performance data and analyzing its effectiveness, and means for automatically generating proposal materials and approach methods for the target demographic based on the analyzed effectiveness results and emotion recognition data. This makes it possible to generate effective advertising content that incorporates user emotional information and optimize marketing strategies.

[1769] "Business details of the proposed company" is an outline of the main business and activities of the company that will be collected and analyzed.

[1770] "Competitor information" is data about other companies competing in the same market as the company to which the proposal is being made.

[1771] "Data on our own products" refers to information about the products and services offered by the company making the proposal.

[1772] "User emotion" refers to the psychological state of the user as recognized from the facial expression, tone of voice, etc. of the user using the system.

[1773] "Analysis results" refer to the results of analyzing collected data using natural language processing technology, etc.

[1774] "Advertising content" refers to promotional text, images, banners, etc. that are generated based o...

Claims

1. A means of collecting data on the business of the client, information on competitors, and data on the client's own products, A means of analyzing the collected data and conducting keyword and competitive comparisons; A means for automatically generating banner advertising content based on the analysis results; A means of recommending optimal ad delivery destinations, A means of collecting advertising performance data and analyzing its effectiveness; A means of automatically generating proposal materials and approaches for the target audience based on the analyzed results. A system including:

2. The system according to claim 1, further comprising a means for collecting business details of the proposed client using web scraping technology and an API.

3. 2. The system according to claim 1, further comprising means for analyzing the collected data on the business details of the proposal recipients, information on competitors, and the company's own products using natural language processing technology.

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