System

Abstract

To provide a system for achieving a community-based effective sales promotion activity.SOLUTION: Inputting, by a user, information of a commodity or service to be sold, a target customer segment, and a promotion budget, preprocessing, by a server, received information into a format suitable for a generated AI model, generating, by the generated AI model, a region-specific advertisement phrase based on the preprocessed information, storing, by the server, the generated advertisement phrase in association with an account of the user, previewing and fine-tuning, by the user, the generated advertisement phrase, distributing, by the server, the advertisement phrase fine-tuned by the user to a designated platform, inputting, by the user, a result of a promotion activity, and transmitting, by the user, sales performance to the server, and adding, by the server, the sales performance to the generated AI model as training data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional sales promotion activities often use advertising language that is standardized nationwide, without sufficient consideration of regional characteristics and customer preferences, resulting in limited sales effectiveness. In particular, the time and cost required to generate promotional language that is tailored to the characteristics and consumer behavior of each region is high, making it difficult to carry out efficient sales promotion activities. In addition, there was a lack of a system for generating more effective advertising language and sales promotion methods based on feedback from sales performance. There was a need to solve these problems and realize effective, locally-focused sales promotion activities. [Means for solving the problem]

[0005] The present invention realizes effective promotional activities that take into account regional characteristics and consumer behavior through a system that includes: a means for a user to input information about the product or service they are selling, their target customer base, and their promotional budget; a means for a server to preprocess the received information into a format suitable for a generative AI model; a means for the generative AI model to generate region-specific advertising text based on the preprocessed data; a means for the server to link the generated advertising text to the user's account and save it; a means for the user to preview and fine-tune the generated advertising text; a means for the server to distribute the advertising text fine-tuned by the user to a specified platform; a means for the user to input the results of promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generative AI model as learning data.

[0006] "User" refers to the end user who uses the system to input and operate information and advertising copy for products and services to be sold.

[0007] "Server" refers to a computer system that receives input data from users, generates advertising copy using a generative AI model, and stores and distributes the results.

[0008] "Device" refers to the device used by a User to input data and preview and fine-tune advertising copy.

[0009] "Generative AI model" refers to an artificial intelligence algorithm and trained model that generates region-specific advertising copy based on input data.

[0010] "Ad Copy" refers to the promotional text or copy generated by a generative AI model and delivered through preview and fine-tuning by a user.

[0011] "Sales performance data" refers to data that a user inputs and sends to the server as a result of promotional activities, and includes sales amounts, customer feedback, click rates, and the like.

[0012] "Preprocessing" refers to processes such as data cleaning and numerical normalization that convert data into a format that is easy for the generative AI model to analyze.

[0013] "Database" refers to a storage device for storing and managing user information, product information, generated advertising copy, and sales performance data within the system.

[0014] "Platform" refers to an external service, such as a social networking site or email marketing system, that delivers advertising copy in a format specified by the user. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] MODE FOR CARRYING OUT THE INVENTION

[0037] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[0038] System configuration

[0039] 1. User registration and data entry

[0040] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[0041] Terminal: Sends the information entered by the user to the server.

[0042] Server: Stores the received user information in a database and completes account registration.

[0043] 2. Enter product information and promotional data

[0044] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0045] Terminal: Sends data entered by the user to the server.

[0046] Server: Stores the received data in a database and prepares it for preprocessing.

[0047] 3. Data Preprocessing and Analysis

[0048] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[0049] 4. Generating advertising copy

[0050] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[0051] For example, "The latest trend! Get it now!" for young people in Tokyo, or "The latest home appliances that make housework easier, on special offer for a limited time!" for housewives in Osaka.

[0052] Server: Stores the generated ad copy in a database.

[0053] 5. Preview and fine-tune your ad copy

[0054] Device: User logs in to their account and previews the generated ad text.

[0055] Users: Preview the ad copy and make any necessary adjustments.

[0056] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0057] 6. Delivery of advertisements

[0058] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[0059] Device: Sends distribution settings to the server.

[0060] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[0061] 7. Collecting sales results and feedback

[0062] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0063] Terminal: Sends sales performance data to the server.

[0064] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0065] Specific examples

[0066] Example 1: If a user sells luxury sneakers in Tokyo, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as, "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user can preview the copy, make any necessary adjustments, and then distribute the ad via social media.

[0067] Example 2: A user selling home appliances in Osaka sets their target demographic as housewives in their 30s-50s, inputs their price range, and enters their promotional budget. The generative AI model uses this information to generate advertising copy such as, "Perfect for housewives in Osaka! The latest home appliances make housework easy!" The user can preview and fine-tune the copy and distribute it through email marketing.

[0068] In this way, the regional promotion proposal system generates advertising copy optimized for each region based on user-entered data, and distributes it to realize effective promotional activities.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[0072] Step 2:

[0073] The terminal transmits the user's input information to the server.

[0074] Step 3:

[0075] The server verifies the received user information and stores it in the database, completing the user account registration.

[0076] Step 4:

[0077] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[0078] Step 5:

[0079] The terminal transmits the user's input data to the server.

[0080] Step 6:

[0081] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[0082] Step 7:

[0083] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[0084] Step 8:

[0085] The server stores the generated ad copy in a database and associates it with the user's account.

[0086] Step 9:

[0087] The device will then display a preview of the generated ad copy to the user, who can review it and make any necessary adjustments.

[0088] Step 10:

[0089] The user checks the ad text that has been fine-tuned and selects the platform (social media, email, etc.) to distribute it.

[0090] Step 11:

[0091] The device sends the distribution settings to the server.

[0092] Step 12:

[0093] The server delivers the ad copy to the specified platform according to the delivery settings.

[0094] Step 13:

[0095] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0096] Step 14:

[0097] The terminal transmits the sales performance data to the server.

[0098] Step 15:

[0099] The server stores the received sales performance data in a database and adds it to the generative AI model as training data, enabling the model to generate advertising copy with even greater accuracy.

[0100] Example 1

[0101] 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."

[0102] In conventional sales promotion proposal systems, users had to individually research consumer behavior and preferences in each region and then create sales promotion copy based on that information, which was a very time-consuming process, making it difficult to generate effective advertising copy.Another issue was the difficulty of creating a feedback loop to evaluate the effectiveness of the generated advertising copy and reflect it in the next sales promotion.

[0103] 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.

[0104] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer base, and their sales promotion budget; a means for a terminal to send the information input by the user to the server; a means for the server to store the received information in a database and preprocess it into a format suitable for the generative AI model; a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; a means for the server to store the generated advertising copy by linking it to the user's account; a means for the terminal for the user to preview and fine-tune the generated advertising copy; a means for the server to distribute the user-adjusted advertising copy to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generative AI model as training data. This enables users to efficiently generate and distribute advertising copy that takes into account consumer behavior and preferences in each region, and to create a feedback loop that evaluates its effectiveness and reflects it in subsequent promotional activities.

[0105] "User" refers to a person who uses the system to input product or service information and review, fine-tune, and distribute the generated advertising copy.

[0106] "Terminal" refers to the device that a user uses to access the system and enter information, preview advertising copy, set up distribution, etc.

[0107] "Server" refers to the device that receives information sent by users and manages data storage, pre-processing, and the generation and distribution of advertising copy using generative AI models.

[0108] "Database" refers to a system connected to a server for storing user information, product information, generated advertising copy, sales performance data, etc.

[0109] A "generative AI model" refers to an artificial intelligence model that uses a specific algorithm to generate advertising copy based on input data.

[0110] "Preprocessing" refers to processes such as cleaning, numeric normalization, and tokenization to convert data into a format suitable for generative AI models.

[0111] "Ad copy" refers to the text message generated by the generative AI model to appeal to a specific region or target demographic.

[0112] "Preview" refers to a system function that allows users to check the generated advertising copy before it is distributed.

[0113] "Fine-tuning" refers to the operation by the user to make changes to the generated advertising copy and refine it into its final form.

[0114] "Platform" refers to external services such as social media and email used to distribute advertising copy.

[0115] "Sales performance data" refers to data such as sales figures, customer feedback, and click rates collected as a result of promotional activities.

[0116] A "feedback loop" refers to the process of retraining the generative AI model based on sales performance data and using it to generate advertising copy from the next time onwards.

[0117] MODE FOR CARRYING OUT THE INVENTION

[0118] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[0119] The main components of this system are as follows:

[0120] 1. User registration and data entry

[0121] User: A user launches a system application and creates an account by entering information such as a username, email address, and password. For example, a user enters "Yamada Taro," "taro@example.com," and "password123."

[0122] Terminal: The information entered by the user is sent to the server using an HTTP POST request.

[0123] Server: Stores the received user information in a database and completes the account registration.

[0124] 2. Enter product information and promotional data

[0125] User: Enter information about the product they want to sell (e.g., product name "luxury sneakers," category "fashion," price "15,000 yen," target customer demographic "young people in their 20s and 30s," promotional budget "100,000 yen").

[0126] Terminal: Sends data entered by the user to the server.

[0127] Server: Stores the received data in a database and prepares it for preprocessing.

[0128] 3. Data Preprocessing and Analysis

[0129] Server: Preprocesses product information and target demographic data into a format suitable for generative AI models. Preprocessing includes data cleaning, numeric normalization, and tokenization.

[0130] 4. Generating advertising copy

[0131] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy, taking into account local consumer behavior and preference data.

[0132] Example: Based on the information "luxury sneakers" and "young people in their 20s and 30s," the ad copy generated is "The latest trend! Get the luxury sneakers that are the talk of Tokyo!"

[0133] Server: Stores the generated ad copy in a database.

[0134] 5. Preview and fine-tune your ad copy

[0135] Device: User logs in to their account to preview the generated ad text.

[0136] User: Make any necessary adjustments to the previewed ad text, for example, changing "Tokyo" to "Shibuya."

[0137] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0138] 6. Delivery of advertisements

[0139] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) to deliver it to. For example, select "Instagram" and set the posting time.

[0140] Device: Sends distribution settings to the server.

[0141] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[0142] 7. Collecting sales results and feedback

[0143] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0144] For example, enter "Sales: 50, Click Rate: 15%, Feedback: Positive".

[0145] Terminal: Sends sales performance data to the server.

[0146] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0147] This allows users to efficiently generate region-specific advertising copy and furthermore reflect the effect of that copy in their next sales promotion activity.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] Step 1:

[0150] User: Launches the application and creates an account by entering information such as username, email address, and password.

[0151] Input: Initial registration information such as username, email address, and password.

[0152] What happens: A user enters information into a form and clicks the submit button.

[0153] Step 2:

[0154] Terminal: Sends the information entered by the user to the server.

[0155] Input: The initial registration information entered by the user.

[0156] Specific operation: The device sends the input form data as an HTTP POST request.

[0157] Output: User registration information sent to the server.

[0158] Step 3:

[0159] Server: Stores the received user information in a database and completes account registration.

[0160] Input: User registration information sent from the device.

[0161] Specific operation: The server creates a new user record in the database and returns a success response to the terminal.

[0162] Output: Saved user information, notification that account registration is complete.

[0163] Step 4:

[0164] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0165] Input: Promotional data such as product name, category, price, target customer, promotional budget, etc.

[0166] Specific behavior: A user enters product information and promotional data into an input form and clicks the submit button.

[0167] Step 5:

[0168] Terminal: Sends data entered by the user to the server.

[0169] Input: Product information and promotional data entered by the user.

[0170] Specific operation: The device sends the input form data as an HTTP POST request.

[0171] Output: Product information and promotional data sent to the server.

[0172] Step 6:

[0173] Server: Stores the received data in a database and prepares it for preprocessing.

[0174] Input: Product information and promotional data sent from your device.

[0175] Specific behavior: The server creates a product information record in the database and adds a pre-processing task to the queue.

[0176] Output: Stored product information, ready for preprocessing tasks.

[0177] Step 7:

[0178] Server: Preprocesses data such as product information and target customer demographics into a format suitable for the generative AI model.

[0179] Input: Product information and target customer data stored in the database.

[0180] What it does: The server removes incomplete and duplicate data, tokenizes text data, and normalizes numeric data.

[0181] Output: Preprocessed data.

[0182] Step 8:

[0183] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy.

[0184] Input: Preprocessed product information and target demographic data.

[0185] Specific operation: The server sends an API request to the generative AI model (e.g., GPT-3).

[0186] Output: The generated localized ad copy.

[0187] Step 9:

[0188] Server: Stores the generated ad copy in a database.

[0189] Input: Ad copy received from the generative AI model.

[0190] What happens: The server adds an ad copy record to the database.

[0191] Output: Saved ad copy.

[0192] Step 10:

[0193] Device: User logs in to their account to preview the generated ad text.

[0194] Input: Ad copy stored in the database.

[0195] Specific operation: The device retrieves advertising text data from the server and displays it to the user.

[0196] Output: The ad copy shown to the user.

[0197] Step 11:

[0198] User: Make any necessary adjustments to the previewed ad text.

[0199] Input: The ad copy you're previewing.

[0200] What happens: The user edits the ad copy and sends the changes from the device to the server.

[0201] Output: Tweaked ad copy.

[0202] Step 12:

[0203] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0204] Input: User-tuned ad copy.

[0205] What happens: The server updates the ad copy record in the database and sets up a delivery task.

[0206] Output: Ready to save and distribute.

[0207] Step 13:

[0208] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[0209] Input: Fine-tuned ad copy, distribution platform information.

[0210] Specific behavior: The user enters distribution settings and sends them to the server.

[0211] Output: Distribution setting data.

[0212] Step 14:

[0213] Device: Sends distribution settings to the server.

[0214] Input: User's delivery setting data.

[0215] Specific operation: The device sends the distribution settings to the server as an HTTP POST request.

[0216] Output: The distribution settings sent to the server.

[0217] Step 15:

[0218] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[0219] Input: Saved ad copy and delivery settings data.

[0220] What happens: The server uses an external API (e.g. Instagram API) to post the ad and record the delivery status.

[0221] Output: A log of the ads served and their delivery status.

[0222] Step 16:

[0223] User: Enter the results of the sales promotion activities and send them to the server as sales performance data.

[0224] Input: Sales performance data (e.g., sales numbers, click rates, customer feedback, etc.).

[0225] Specific operation: The user enters sales result data into the form and sends it to the server.

[0226] Output: Sales performance data sent to the server.

[0227] Step 17:

[0228] Terminal: Sends sales performance data to the server.

[0229] Input: Sales performance data entered by the user.

[0230] Specific operation: The terminal sends the sales result data to the server as an HTTP POST request.

[0231] Output: Sales performance data sent to the server.

[0232] Step 18:

[0233] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0234] Input: Sales performance data sent from the terminal.

[0235] What happens: The server creates a new sales record in the database and adds the task to the model's retraining batch.

[0236] Output: Stored sales performance data, updated generative AI model.

[0237] (Application example 1)

[0238] 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."

[0239] In today's advertising industry, the generation of region-specific advertising copy and a user-friendly interface are required to effectively promote products and services. Conventional systems have struggled to generate advertising copy that takes into account region-specific consumer behavior and preferences, and have not adequately developed a means for users to easily adjust and distribute advertising copy. Furthermore, it has been difficult to effectively collect performance data and reflect it in improving advertising copy. The present invention aims to solve these problems and realize the generation of region-specific advertising copy and its effective distribution.

[0240] 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.

[0241] In this invention, the server includes a means for a user to input information about the product or service being sold, the target customer demographic, and the sales promotion budget; a means for the server to preprocess the received information into a format suitable for the generative AI model; and a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data. This enables the generation of advertising copy that takes into account region-specific consumer behavior and preferences. The system also includes a means for a user to input data via voice input and eye tracking, and a means for fine-tuning the advertising copy based on voice instructions and gestures, providing a user-friendly interface and enabling the rapid and effective adjustment and distribution of advertising copy. Furthermore, the system includes a means for a user to input the results of promotional activities and send sales performance data to the server, allowing the collected performance data to be added as training data for the generative AI model, thereby continuously improving the accuracy and effectiveness of advertising copy.

[0242] "User" means any individual or entity that intends to sell goods or services using the System.

[0243] "Product or service information" means basic data about the product or service, including product name, category, price, etc.

[0244] A "target customer" is a specific consumer group for which a product or service is aimed, characterized by attributes such as age, gender, or region.

[0245] The "promotional budget" refers to the total budget allocated to advertising and marketing activities.

[0246] A "server" is a computer system used to receive, process, and store data from users.

[0247] A "generative AI model" refers to an artificial intelligence model that is trained to perform a specific task based on past data.

[0248] "Preprocessing" refers to processes such as data cleaning and normalization that are necessary for a generative AI model to efficiently analyze data.

[0249] "Advertising Copy" means a text message used to advertise a product or service.

[0250] "Preview" refers to a temporary display function that allows the user to check the generated advertising copy.

[0251] "Fine-tuning" refers to an operation in which a user manually corrects the details of the generated advertising copy.

[0252] A "designated platform" refers to the medium, such as social media or email, selected to deliver the advertising copy.

[0253] "Sales performance data" refers to data such as sales figures and customer feedback obtained as a result of promotional activities.

[0254] "Voice input" refers to a means of inputting a user's speech as data.

[0255] "Eye tracking" is a technology that tracks the movement of a user's eyes to input data or perform operations.

[0256] "Voice instructions" refers to a method in which a user issues commands to a system by voice.

[0257] "Gestures" are a method of operation in which you give instructions to a system using hand or body movements.

[0258] The present invention is a system for proposing sales promotions that are specialized for a region, and generates advertising copy that is specialized for each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance. A specific embodiment for this purpose is described below.

[0259] User Registration and Data Entry

[0260] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking and a voice input system. This makes it easy for users with visual impairments or who are unfamiliar with keyboard operation to create an account.

[0261] Entering product information and promotional data

[0262] Users input information about the product they want to sell (product name, category, price, etc.), target customer demographic, and sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. This information is received by the server and stored in a database.

[0263] Data preprocessing and analysis

[0264] The server preprocesses the received data, such as product information, target customer demographics, and budget, into a format suitable for the AI ​​model. It performs data cleaning and normalization to prepare the data in an optimal state for input into the model. This improves the accuracy of the AI ​​model and enables the generation of effective advertising copy.

[0265] Generating ad copy

[0266] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[0267] Examples of prompt sentences include "Input prompt for generative AI model: Tokyo, 20s-30s, latest trends, luxury sneakers" and "Input prompt for generative AI model: Osaka, 30s-50s, housewife, household appliances."

[0268] Preview and fine-tune your ad copy

[0269] The generated ad copy is saved in the user's account, and the user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures, allowing users to easily optimize their ad copy and achieve high advertising effectiveness.

[0270] Advertisement delivery and sales data collection

[0271] Finally, the advertising copy fine-tuned by the user is distributed by the server to the specified platform (such as social media or email). The user inputs sales performance data (sales figures, customer feedback, etc.) obtained as a result of the promotional activities and sends it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of the advertising copy are continuously improved.

[0272] As described above, this system generates region-specific advertising copy and is designed to realize effective sales promotion activities. It has a user-friendly interface and a feedback loop based on performance data, making it possible to optimize advertising effectiveness.

[0273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0274] Step 1:

[0275] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking or voice input systems. The input information is sent by the device to a server, which stores it in a database.

[0276] Input: Username, Email Address, Password

[0277] Output: User information stored in the database

[0278] Step 2:

[0279] Users input information about the product they want to sell (product name, category, price, etc.), their target customer base, and their sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. The input information is sent by the device to the server, which then stores it in a database.

[0280] Input: Product name, category, price, target customer, promotion budget

[0281] Output: Product information stored in the database

[0282] Step 3:

[0283] The server preprocesses the received data, such as product information, target customer demographics, and promotional budgets, into a format suitable for the generative AI model. It performs processes such as data cleaning and normalization to prepare the data in an optimal state for input into the model. This preprocessing allows the model to analyze the data efficiently.

[0284] Input: Product information, target customer demographics, promotional budget

[0285] Output: Preprocessed data in a format that can be input into a generative AI model

[0286] Step 4:

[0287] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[0288] Input: Preprocessed data

[0289] Output: Localized ad copy

[0290] Step 5:

[0291] The generated ad copy is saved in the user's account by the server. The user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures. This allows users to easily optimize the ad copy and achieve high advertising effectiveness.

[0292] Input: Generated ad copy

[0293] Output: Tweaked ad copy

[0294] Step 6:

[0295] The server delivers the ad copy fine-tuned by the user to the specified platform (SNS, email, etc.), thereby effectively delivering locally-specific ads.

[0296] Input: Tweaked ad copy

[0297] Output: Delivery of advertising text to the specified platform

[0298] Step 7:

[0299] Users input sales performance data (sales figures, customer feedback, etc.) obtained as a result of their promotional activities and send it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of advertising copy is continuously improved.

[0300] Input: Sales performance data (sales numbers, customer feedback)

[0301] Output: Sales performance data added as training data for the generative AI model

[0302] 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.

[0303] MODE FOR CARRYING OUT THE INVENTION

[0304] The present invention relates to a region-specific sales promotion proposal system, and in particular to a system that generates more effective and attractive advertising copy and improves sales performance by combining an emotion engine that recognizes user emotions.

[0305] System configuration

[0306] 1. User registration and data entry

[0307] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[0308] Terminal: Sends the information entered by the user to the server.

[0309] Server: Stores the received user information in a database and completes account registration.

[0310] 2. Enter product information and promotional data

[0311] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0312] Terminal: Sends data entered by the user to the server.

[0313] Server: Stores the received information in a database and prepares it for preprocessing.

[0314] 3. Data Preprocessing and Analysis

[0315] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[0316] 4. Generating advertising copy

[0317] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[0318] Server: Stores the generated ad copy in a database.

[0319] 5. Preview and fine-tune your ad copy

[0320] On the device: The user logs in to their account and previews the generated ad copy. The emotion engine then analyzes the user's visual and audio information to interpret their emotions.

[0321] Emotion engine: Automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and conversely, if the user is calm, it will suggest more toned-down language.

[0322] Users: can see the preview and either adopt the suggested wording or manually adjust it.

[0323] 6. Delivery of advertisements

[0324] User: Check the ad copy to be delivered and select the platform (social media, email, etc.) to deliver it.

[0325] Device: Sends distribution settings to the server.

[0326] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[0327] 7. Collecting sales results and feedback

[0328] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0329] Terminal: Sends sales performance data to the server.

[0330] Server: Stores the received sales performance data in a database and adds it to the generative AI model as training data.

[0331] Emotion Engine: User feedback is analyzed for emotion and reflected in sales performance data, allowing the generative AI model to improve advertising copy by taking user emotions into account.

[0332] Specific examples

[0333] Example 1: If a user sells luxury sneakers in Tokyo, they set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user reviews the ad on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests similarly emphasized copy. The user then adopts this and distributes the ad on social media.

[0334] Example 2: If a user sells home appliances in Osaka, they set their target demographic to housewives in their 30s-50s and input their price range and promotional budget. The generative AI model uses this information to generate advertising copy such as "Perfect for housewives in Osaka! The latest home appliances make housework easy!" If the emotion engine recognizes a "calm" state, the user can fine-tune the automatically suggested calm copy and distribute the ad through email marketing.

[0335] In this way, by combining an emotion engine that recognizes user emotions, the present invention enables more effective generation and fine-tuning of advertising copy, thereby realizing locally specialized sales promotion activities.

[0336] The processing flow will be explained below.

[0337] Step 1:

[0338] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[0339] Step 2:

[0340] The terminal transmits the user's input information to the server.

[0341] Step 3:

[0342] The server verifies the received user information and stores it in the database, completing the user account registration.

[0343] Step 4:

[0344] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[0345] Step 5:

[0346] The terminal transmits the user's input data to the server.

[0347] Step 6:

[0348] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[0349] Step 7:

[0350] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[0351] Step 8:

[0352] The server stores the generated ad copy in a database and associates it with the user's account.

[0353] Step 9:

[0354] The device displays a preview of the generated ad copy to the user, while the emotion engine analyzes the user's visual and audio information to read their emotions.

[0355] Step 10:

[0356] The emotion engine automatically generates suggestions for fine-tuning ad copy based on the user's emotional state. For example, it suggests emphasizing words when the user is "excited" and calming words when the user is "calm."

[0357] Step 11:

[0358] The user reviews the preview and either adopts the suggested wording or adjusts it manually.

[0359] Step 12:

[0360] The user checks the ad copy to be delivered and selects the platform (SNS, email, etc.) on which to deliver it.

[0361] Step 13:

[0362] The device sends the distribution settings to the server.

[0363] Step 14:

[0364] The server delivers the ad copy to the specified platform according to the settings.

[0365] Step 15:

[0366] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0367] Step 16:

[0368] The terminal transmits the sales performance data to the server.

[0369] Step 17:

[0370] The sales performance data received by the server is stored in a database and added to the generative AI model as training data.

[0371] Step 18:

[0372] The emotion engine analyzes user feedback and reflects it in sales performance data, allowing for improvements to be made by taking user emotions into account when generating the next ad copy.

[0373] Example 2

[0374] 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."

[0375] Conventional sales promotion proposal systems have the problem of limited advertising effectiveness because they are unable to generate or fine-tune advertising copy that takes user emotions into account. Furthermore, it is difficult to automatically generate region-specific advertising copy, which increases the effort required for users to manually adjust it.

[0376] The specification processing by the specification 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 a user to input information on a product or service to be sold, a target customer demographic, and a sales promotion budget; means for a terminal to transmit the information input by the user to the server; means for the server to store the received information in a database and complete account registration; means for the server to preprocess the received information into a format suitable for the generative AI model based on the information received; means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; means for the server to store the generated advertising copy in a database; means for a user to preview and fine-tune the generated advertising copy; means for an emotion engine to analyze the user's emotional state and suggest fine-tuning the advertising copy; means for the server to distribute the advertising copy fine-tuned by the user to a specified platform; means for a user to input the results of sales promotion activities and transmit sales performance data to the server; and means for the server to add the sales performance data to the generative AI model as learning data to improve the quality of the advertising copy. This allows for effective generation and fine-tuning of advertising copy taking into account user emotions, and also makes it possible to propose sales promotions that are specific to the region.

[0377] "User" refers to any person or company that intends to use the System to sell goods or services.

[0378] "Terminal" refers to a device (e.g., PC, smartphone, tablet) that a user uses to input information and communicate with a server.

[0379] "Server" refers to the computer system that processes the received information, generates advertising copy using the generative AI model, and stores and distributes the data.

[0380] "Database" refers to a system in which a server stores user information, product information, generated advertising copy, sales performance data, etc.

[0381] "Generative AI models" refer to artificial intelligence algorithms or systems that generate region-specific advertising copy based on pre-processed data.

[0382] "Preview" refers to the screen display or interface that allows a user to preview the generated ad copy and make any necessary adjustments.

[0383] "Fine-tuning" refers to the act of modifying the content or expression of generated advertising copy based on suggestions from the user or the system.

[0384] An "emotion engine" refers to technology or software that analyzes a user's visual information and voice to recognize their emotional state and reflect the results in adjusting advertising copy.

[0385] "Promotional activities" refers to marketing and promotional activities carried out to promote the sale of products and services.

[0386] "Sales performance data" refers to data such as sales figures obtained as a result of promotional activities, customer feedback, and click rates.

[0387] The present invention is a localized promotion suggestion system that detects user emotions and generates and fine-tunes advertising copy based on the emotions. The system is implemented using the following specific hardware and software:

[0388] System Components

[0389] 1. User: Launches the application, creates an account, and uses the system. The user enters information about the product or service to be sold, the target customer demographic, and the promotional budget.

[0390] 2. Terminal: Using a device such as a PC, smartphone, or tablet, the user's input information is sent to the server.

[0391] 3. Server: The server manages the entire system, including the database, generative AI model, emotion engine, etc., and processes information. Specifically, it operates with the following configuration:

[0392] Data preprocessing and storage

[0393] The server receives product information and target customer demographic data sent by users and stores it in a database (e.g., MySQL). It then preprocesses the data into a format suitable for generative AI models (e.g., GPT-4). Preprocessing includes data cleaning, missing value imputation, and numerical normalization.

[0394] Generating ad copy

[0395] The server uses the preprocessed data to create prompts, which are then fed into a generative AI model to generate localized ad copy. The generated ad copy is then stored in a database. For example, the following prompts are used:

[0396] Example prompt:

[0397] "Generate advertising copy to sell high-end sneakers to young people in their 20s and 30s in Tokyo."

[0398] Fine-tuning with the Emotion Engine

[0399] When a user previews the generated ad copy, an emotion engine (e.g., Affectiva SDK) analyzes the user's visual and audio information to recognize their emotional state. The emotion engine then suggests fine-tuning the ad copy based on the user's emotions.

[0400] Ad copy delivery

[0401] After the user has reviewed the ad copy and made any necessary adjustments, the server distributes the final ad copy to the specified distribution platform (e.g., social media, email marketing, etc.) The distribution status of the ad is monitored in real time.

[0402] Sales performance collection and feedback

[0403] Users input sales performance data (sales figures, customer feedback, click rates, etc.) as a result of their promotional activities and send it to the server. The server stores this data in a database and adds it to the generative AI model as training data. Furthermore, the emotion engine analyzes user feedback and reflects it in the sales performance data to improve the quality of advertising copy.

[0404] Specific examples

[0405] For example, if a user sells high-end sneakers in Tokyo, the following process would occur:

[0406] 1. The user sets the target customer demographic as young people in their 20s and 30s, and enters the price range and promotional budget.

[0407] 2. Based on this information, the server inputs a prompt into the generative AI model, generating advertising text such as, "The latest trend! Get your hands on the luxury sneakers that are the talk of Tokyo!"

[0408] 3. The user checks the ad copy on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests more emphatic copy.

[0409] 4. Users adopt it and distribute ads on social media.

[0410] As described above, the present invention can generate and fine-tune advertising copy taking into account user emotions, and can effectively carry out regionally specific sales promotion activities.

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

[0412] Step 1:

[0413] User: Launches the application and opens the page for creating a new account. The user enters initial registration information such as username, email address, and password, and presses the "Submit" button.

[0414] Input: Username, Email Address, Password

[0415] Output: Registration information sent

[0416] Step 2:

[0417] Terminal: Sends the registration information entered by the user to the server.

[0418] Input: Username, Email Address, Password

[0419] Output: Registration information arrives at the server

[0420] Step 3:

[0421] Server: Save the received user information in a database (e.g. MySQL) and register a new account.

[0422] Input: Registration information

[0423] Output: User information is saved in the database

[0424] What it does: Uses an SQL statement to insert user information into the database.

[0425] Step 4:

[0426] Server: Generates a notification that the user has successfully registered for an account.

[0427] Input: User information

[0428] Output: Registration completion notification

[0429] Specific behavior: Calls the notification generation routine to create a registration completion message.

[0430] Step 5:

[0431] Server: Sends notification data to the device.

[0432] Input: Registration completion notification

[0433] Output: Notification data arrives on the device

[0434] Specific operation: Uses the network sending API to send a registration completion notification to the device.

[0435] Step 6:

[0436] Terminal: Display a message to the user confirming registration.

[0437] Input: Notification data

[0438] Output: A message appears saying registration is complete.

[0439] Specific behavior: Calls a routine that displays a notification message in the user interface.

[0440] Step 7:

[0441] User: Log in to your account and open the product information entry page. Enter the product name, category, price, target customer, and promotional budget, then click the "Submit" button.

[0442] Input: Product name, category, price, target customer, promotion budget

[0443] Output: Product information is sent

[0444] Step 8:

[0445] Terminal: Sends the product information entered by the user to the server.

[0446] Input: Product name, category, price, target customer, promotion budget

[0447] Output: Product information arrives at the server

[0448] Step 9:

[0449] Server: Store the received product information in a database.

[0450] Input: Product information

[0451] Output: Product information saved in the database

[0452] Specific behavior: Uses SQL statements to insert product information into the database.

[0453] Step 10:

[0454] Server: Retrieves product information and target customer data from the database and performs preprocessing.

[0455] Input: Product information, target customer information

[0456] Output: Preprocessed data

[0457] Specific operations: Perform data cleaning, missing value imputation, numerical normalization, etc.

[0458] Step 11:

[0459] Server: Creates prompts using preprocessed data and inputs them into the generative AI model.

[0460] Input: Preprocessed data

[0461] Output: Ad copy

[0462] Specific operation: Prompt text is generated based on the preprocessed data and passed to a generative AI model to generate advertising copy.

[0463] Step 12:

[0464] Server: Stores the generated ad copy in a database.

[0465] Input: Ad copy

[0466] Output: Ad copy saved in database

[0467] What it does: Uses an SQL statement to insert ad copy into the database.

[0468] Step 13:

[0469] Users: Log in to your account and open the preview screen to view the generated ad text.

[0470] Input: User information

[0471] Output: Preview of ad copy

[0472] Step 14:

[0473] Device: The generated ad copy and preview screen are displayed to the user.

[0474] Input: Ad copy

[0475] Output: Preview screen

[0476] What it does: Calls a routine that displays ad copy and previews in the user interface.

[0477] Step 15:

[0478] Emotion engine: Analyzes the user's visual and audio information during the preview and recognizes the user's emotions.

[0479] Input: User's visual information, audio data

[0480] Output: Emotional state data

[0481] What it does: Analyzes emotions using image and audio analysis algorithms.

[0482] Step 16:

[0483] Emotion engine: Suggests ad copy tweaks based on the user's emotional state.

[0484] Input: Emotional state data, advertising text

[0485] Output: Tweak suggestions

[0486] What it does: Analyzes emotional state and generates suggested revisions to ad copy based on that.

[0487] Step 17:

[0488] User: Review the suggested ad copy and manually adjust it as needed.

[0489] Input: Tweak suggestions, ad copy

[0490] Output: Final adjusted ad copy

[0491] Step 18:

[0492] Device: Sends the adjustments to the server.

[0493] Input: Final ad copy

[0494] Output: The adjustments are sent to the server

[0495] Step 19:

[0496] Server: Saves the final adjusted ad copy in the database.

[0497] Input: Final ad copy

[0498] Output: Adjusted ad copy saved in database

[0499] What it does: Uses an SQL statement to insert the final ad copy into the database.

[0500] Step 20:

[0501] User: Select the ad copy you want to deliver and choose the delivery platform (SNS, email, etc.).

[0502] Input: Finalized ad copy, distribution platform

[0503] Output: Distribution setting data

[0504] Step 21:

[0505] Device: Sends distribution settings to the server.

[0506] Input: Distribution setting data

[0507] Output: Distribution settings are sent to the server

[0508] Step 22:

[0509] Server: Serves ads to the specified platform.

[0510] Input: Delivery setting data, finalized ad wording

[0511] Output: Ad is served

[0512] What it does: Uses network APIs to send ad copy to distribution platforms.

[0513] Step 23:

[0514] User: Enters sales performance data as a result of promotional activities and sends it to the server.

[0515] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[0516] Output: Sales performance data arrives at the server

[0517] Step 24:

[0518] Server: Stores the received sales data in a database.

[0519] Input: Sales performance data

[0520] Output: Sales performance data is saved in the database

[0521] Specific behavior: Uses SQL statements to insert sales performance data into the database.

[0522] Step 25:

[0523] Emotion engine: Analyzes user feedback emotionally and reflects it in sales performance data.

[0524] Input: Sales performance data, feedback data

[0525] Output: Sentiment analysis data

[0526] What it does: Analyzes emotions using image and audio analysis algorithms.

[0527] Step 26:

[0528] Server: Sales performance data along with the analysis results are added as learning data to the generated AI model to improve the quality of advertising wording.

[0529] Input: Sales performance data, sentiment analysis data

[0530] Output: An updated generative AI model

[0531] Specific operation: Use a learning algorithm to update the parameters of the generative AI model.

[0532] (Application example 2)

[0533] 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."

[0534] Conventional advertising systems lacked the ability to analyze user sentiment and fine-tune ad copy when generating region-specific ad copy. This resulted in ad copy that was less likely to resonate with target customers, resulting in insufficient effectiveness of sales promotion activities. Furthermore, when users fine-tuned the generated ad copy, the effectiveness of the suggested copy was often not optimized based on user sentiment, making fine-tuning the ad copy complicated and time-consuming. Furthermore, when sales performance data was added as training data for the AI ​​model, the results of sentiment analysis were not reflected, making it difficult to use the results in generating the next ad copy.

[0535] 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.

[0536] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer demographic, and their promotional budget; a means for the server to preprocess the received information into a format suitable for the generation AI model; a means for the generation AI model to generate region-specific advertising text based on the preprocessed data; a means for the server to save the generated advertising text by linking it to the user's account; a means for the user to preview and fine-tune the generated advertising text; a means for the user to fine-tune the generated advertising text using a sentiment analysis engine that analyzes user sentiment; a means for the server to distribute the user-tuned advertising text to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generation AI model as training data. This allows the results of the user's sentiment analysis to be reflected in the generation of region-specific advertising text, enabling the generation and fine-tuning of more effective advertising text that appeals to the target customer demographic. Furthermore, by reflecting the results of the sentiment analysis in the sales performance data, the accuracy of the next advertising text generation can be improved.

[0537] "User" refers to an individual or corporation that uses a system or application.

[0538] "Product or service information" refers to information including data such as the name, category, and price of the product being sold.

[0539] "Target customer demographic" refers to the demographic of customers who are likely to purchase a particular product or service.

[0540] "Promotional budget" refers to the total amount of funds allocated for carrying out promotional activities.

[0541] "Server" refers to a central location that stores, processes, and distributes data.

[0542] A "generative AI model" refers to an artificial intelligence algorithm that generates advertising copy and other information based on input data.

[0543] "Preprocessing" refers to the process of converting data into a format suitable for a generative AI model.

[0544] "Region-specific advertising language" refers to advertising content that is suited to the preferences and behavior of consumers in a specific region.

[0545] An "account" refers to authentication information used to identify a user and associate individual data and settings with the user.

[0546] "Preview" refers to a display that allows you to check the generated advertising copy before it is actually delivered.

[0547] "Tweaking" refers to making small changes to optimize ad wording or settings.

[0548] An "emotion analysis engine" refers to a device or software that has the function of analyzing emotions from data such as a user's facial expressions and voice.

[0549] "Platform" refers to a medium, such as social media or email marketing, used to deliver advertisements.

[0550] "Sales performance data" refers to data including sales and customer feedback obtained as a result of actual sales promotion activities.

[0551] "Training data" refers to the dataset that a generative AI model uses to improve its performance.

[0552] The present invention is a system for generating region-specific advertising copy and fine-tuning it by analyzing user emotions. Specific embodiments of the system are described below.

[0553] 1. Overview of the entire system

[0554] The system consists of a user's device, a server, a generative AI model, a sentiment analysis engine, and an ad distribution platform. This system can generate and fine-tune effective advertising copy based on data entered by the user, such as product information and target customer demographics.

[0555] 2. System Configuration

[0556] Terminal

[0557] It provides a means for users to input product and service information, target customer demographics, and sales promotion budgets. This information is sent to the server, which will be described later.

[0558] server

[0559] The server uses the following software and hardware:

[0560] Database: Stores user and product information.

[0561] Generative AI model: An artificial intelligence algorithm for generating localized ad copy.

[0562] Sentiment analysis engine: Analyzes user sentiment and fine-tunes ad copy.

[0563] 3. Data Flow and Processing

[0564] User Data Entry

[0565] Users input detailed information about the products or services they want to sell, their target customer demographic, and their sales budget through the terminal. For example, if a user wants to sell high-end sneakers to people in their 20s and 30s, they enter this information into the terminal and send it to the server.

[0566] Pretreatment

[0567] The server preprocesses the received information, converting it into a format that can be properly processed by the generative AI model. Data cleaning and numerical normalization are performed at this stage.

[0568] Generating ad copy

[0569] The generative AI model then uses the pre-processed data to generate localized ad copy, taking into account local consumer behavior and preference data.

[0570] Sentiment analysis and ad copy fine-tuning

[0571] When a user previews the generated ad copy, a sentiment analysis engine analyzes the user's visual and audio data and suggests tweaks based on their emotions. For example, if the user is excited, the engine suggests making the ad copy more emphatic.

[0572] Ad serving

[0573] The finely tuned advertising copy is then distributed via the server to the specified platform (such as social media or email marketing).

[0574] Collection and feedback of sales performance data

[0575] Users input the results of their promotional activities and send them to the server as sales performance data. The server adds this data to the generative AI model as learning data and uses it to generate advertising copy from the next time onwards.

[0576] 4. Examples and prompts

[0577] Examples:

[0578] For example, if a user is selling luxury sneakers, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of the town!", and if the sentiment analysis engine recognizes an "excited" state, it will suggest emphasized copy.

[0579] Example prompt sentence:

[0580] "Generate ad copy that includes the keyword 'latest trend' for young people in their 20s and 30s who want to sell high-end sneakers."

[0581] This system generates effective advertising copy that is region-specific and based on sentiment analysis, and makes it possible to utilize the results of users' promotional activities in generating the next advertising copy.

[0582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0583] Step 1:

[0584] The user starts up the device and creates an account. During the initial registration, they enter information such as their username, email address, and password. This information is sent to the server and stored in a database.

[0585] Input: Username, Email Address, Password

[0586] Output: Confirmation of account registration, save user information to database

[0587] Step 2:

[0588] The user inputs information about the product they want to sell (product name, category, price, etc.), the target customer demographic, and the sales promotion budget. This information is sent from the terminal to the server and stored in a database.

[0589] Input: Product name, category, price, target customer, promotion budget

[0590] Output: Product information saved in a database

[0591] Step 3:

[0592] The server preprocesses the received data, such as product information and target customer demographics, into a format suitable for the generative AI model. Preprocessing involves data cleaning and numerical normalization.

[0593] Input: Product information, target customer demographic, promotional budget

[0594] Output: Preprocessed data

[0595] Step 4:

[0596] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy, which takes into account consumer behavior and preference data for each region to create effective advertising copy.

[0597] Input: Preprocessed data

[0598] Output: Generated ad copy

[0599] Step 5:

[0600] The generated advertising copy is stored by the server and linked to the user's account.

[0601] Input: Generated ad copy

[0602] Output: Ad copy saved in the user account

[0603] Step 6:

[0604] The user logs in to their account on their device and previews the generated ad copy.

[0605] Input: User credentials

[0606] Output: Preview of ad copy

[0607] Step 7:

[0608] The emotion analysis engine analyzes the user's visual and audio data and automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and if they are calm, it will suggest more calm language.

[0609] Input: User's emotional data (visual information, audio data)

[0610] Output: Sentiment-based ad copy suggestions

[0611] Step 8:

[0612] The user reviews the preview and either accepts the sentiment engine's suggestions or manually adjusts the ad copy.

[0613] Input: Sentiment engine suggested text, user manual adjustment

[0614] Output: Finalized ad copy

[0615] Step 9:

[0616] The server delivers the confirmed advertising text to the specified platform (SNS, email, etc.).

[0617] Input: Confirmed ad copy, distribution platform information

[0618] Output: Served ad copy

[0619] Step 10:

[0620] The user inputs the results of the sales promotion activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0621] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[0622] Output: Sales performance data saved in database

[0623] Step 11:

[0624] The server adds the received sales performance data to the generation AI model as learning data, improving the accuracy of future advertising copy generation.

[0625] Input: Sales performance data

[0626] Output: Sales performance data added as training data

[0627] This step-by-step process enables the system to generate and deliver effective advertising copy that is localized and based on sentiment analysis.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] [Second embodiment]

[0632] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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."

[0644] MODE FOR CARRYING OUT THE INVENTION

[0645] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[0646] System configuration

[0647] 1. User registration and data entry

[0648] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[0649] Terminal: Sends the information entered by the user to the server.

[0650] Server: Stores the received user information in a database and completes account registration.

[0651] 2. Enter product information and promotional data

[0652] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0653] Terminal: Sends data entered by the user to the server.

[0654] Server: Stores the received data in a database and prepares it for preprocessing.

[0655] 3. Data Preprocessing and Analysis

[0656] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[0657] 4. Generating advertising copy

[0658] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[0659] For example, "The latest trend! Get it now!" for young people in Tokyo, or "The latest home appliances that make housework easier, on special offer for a limited time!" for housewives in Osaka.

[0660] Server: Stores the generated ad copy in a database.

[0661] 5. Preview and fine-tune your ad copy

[0662] Device: User logs in to their account and previews the generated ad text.

[0663] Users: Preview the ad copy and make any necessary adjustments.

[0664] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0665] 6. Delivery of advertisements

[0666] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[0667] Device: Sends distribution settings to the server.

[0668] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[0669] 7. Collecting sales results and feedback

[0670] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0671] Terminal: Sends sales performance data to the server.

[0672] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0673] Specific examples

[0674] Example 1: If a user sells luxury sneakers in Tokyo, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as, "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user can preview the copy, make any necessary adjustments, and then distribute the ad via social media.

[0675] Example 2: A user selling home appliances in Osaka sets their target demographic as housewives in their 30s-50s, inputs their price range, and enters their promotional budget. The generative AI model uses this information to generate advertising copy such as, "Perfect for housewives in Osaka! The latest home appliances make housework easy!" The user can preview and fine-tune the copy and distribute it through email marketing.

[0676] In this way, the regional promotion proposal system generates advertising copy optimized for each region based on user-entered data, and distributes it to realize effective promotional activities.

[0677] The processing flow will be explained below.

[0678] Step 1:

[0679] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[0680] Step 2:

[0681] The terminal transmits the user's input information to the server.

[0682] Step 3:

[0683] The server verifies the received user information and stores it in the database, completing the user account registration.

[0684] Step 4:

[0685] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[0686] Step 5:

[0687] The terminal transmits the user's input data to the server.

[0688] Step 6:

[0689] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[0690] Step 7:

[0691] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[0692] Step 8:

[0693] The server stores the generated ad copy in a database and associates it with the user's account.

[0694] Step 9:

[0695] The device will then display a preview of the generated ad copy to the user, who can review it and make any necessary adjustments.

[0696] Step 10:

[0697] The user checks the ad text that has been fine-tuned and selects the platform (social media, email, etc.) to distribute it.

[0698] Step 11:

[0699] The device sends the distribution settings to the server.

[0700] Step 12:

[0701] The server delivers the ad copy to the specified platform according to the delivery settings.

[0702] Step 13:

[0703] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0704] Step 14:

[0705] The terminal transmits the sales performance data to the server.

[0706] Step 15:

[0707] The server stores the received sales performance data in a database and adds it to the generative AI model as training data, enabling the model to generate advertising copy with even greater accuracy.

[0708] Example 1

[0709] 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."

[0710] In conventional sales promotion proposal systems, users had to individually research consumer behavior and preferences in each region and then create sales promotion copy based on that information, which was a very time-consuming process, making it difficult to generate effective advertising copy.Another issue was the difficulty of creating a feedback loop to evaluate the effectiveness of the generated advertising copy and reflect it in the next sales promotion.

[0711] 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.

[0712] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer base, and their sales promotion budget; a means for a terminal to send the information input by the user to the server; a means for the server to store the received information in a database and preprocess it into a format suitable for the generative AI model; a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; a means for the server to store the generated advertising copy by linking it to the user's account; a means for the terminal for the user to preview and fine-tune the generated advertising copy; a means for the server to distribute the user-adjusted advertising copy to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generative AI model as training data. This enables users to efficiently generate and distribute advertising copy that takes into account consumer behavior and preferences in each region, and to create a feedback loop that evaluates its effectiveness and reflects it in subsequent promotional activities.

[0713] "User" refers to a person who uses the system to input product or service information and review, fine-tune, and distribute the generated advertising copy.

[0714] "Terminal" refers to the device that a user uses to access the system and enter information, preview advertising copy, set up distribution, etc.

[0715] "Server" refers to the device that receives information sent by users and manages data storage, pre-processing, and the generation and distribution of advertising copy using generative AI models.

[0716] "Database" refers to a system connected to a server for storing user information, product information, generated advertising copy, sales performance data, etc.

[0717] A "generative AI model" refers to an artificial intelligence model that uses a specific algorithm to generate advertising copy based on input data.

[0718] "Preprocessing" refers to processes such as cleaning, numeric normalization, and tokenization to convert data into a format suitable for generative AI models.

[0719] "Ad copy" refers to the text message generated by the generative AI model to appeal to a specific region or target demographic.

[0720] "Preview" refers to a system function that allows users to check the generated advertising copy before it is distributed.

[0721] "Fine-tuning" refers to the operation by the user to make changes to the generated advertising copy and refine it into its final form.

[0722] "Platform" refers to external services such as social media and email used to distribute advertising copy.

[0723] "Sales performance data" refers to data such as sales figures, customer feedback, and click rates collected as a result of promotional activities.

[0724] A "feedback loop" refers to the process of retraining the generative AI model based on sales performance data and using it to generate advertising copy from the next time onwards.

[0725] MODE FOR CARRYING OUT THE INVENTION

[0726] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[0727] The main components of this system are as follows:

[0728] 1. User registration and data entry

[0729] User: A user launches a system application and creates an account by entering information such as a username, email address, and password. For example, a user enters "Yamada Taro," "taro@example.com," and "password123."

[0730] Terminal: The information entered by the user is sent to the server using an HTTP POST request.

[0731] Server: Stores the received user information in a database and completes the account registration.

[0732] 2. Enter product information and promotional data

[0733] User: Enter information about the product they want to sell (e.g., product name "luxury sneakers," category "fashion," price "15,000 yen," target customer demographic "young people in their 20s and 30s," promotional budget "100,000 yen").

[0734] Terminal: Sends data entered by the user to the server.

[0735] Server: Stores the received data in a database and prepares it for preprocessing.

[0736] 3. Data Preprocessing and Analysis

[0737] Server: Preprocesses product information and target demographic data into a format suitable for generative AI models. Preprocessing includes data cleaning, numeric normalization, and tokenization.

[0738] 4. Generating advertising copy

[0739] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy, taking into account local consumer behavior and preference data.

[0740] Example: Based on the information "luxury sneakers" and "young people in their 20s and 30s," the ad copy generated is "The latest trend! Get the luxury sneakers that are the talk of Tokyo!"

[0741] Server: Stores the generated ad copy in a database.

[0742] 5. Preview and fine-tune your ad copy

[0743] Device: User logs in to their account to preview the generated ad text.

[0744] User: Make any necessary adjustments to the previewed ad text, for example, changing "Tokyo" to "Shibuya."

[0745] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0746] 6. Delivery of advertisements

[0747] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) to deliver it to. For example, select "Instagram" and set the posting time.

[0748] Device: Sends distribution settings to the server.

[0749] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[0750] 7. Collecting sales results and feedback

[0751] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0752] For example, enter "Sales: 50, Click Rate: 15%, Feedback: Positive".

[0753] Terminal: Sends sales performance data to the server.

[0754] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0755] This allows users to efficiently generate region-specific advertising copy and furthermore reflect the effect of that copy in their next sales promotion activity.

[0756] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0757] Step 1:

[0758] User: Launches the application and creates an account by entering information such as username, email address, and password.

[0759] Input: Initial registration information such as username, email address, and password.

[0760] What happens: A user enters information into a form and clicks the submit button.

[0761] Step 2:

[0762] Terminal: Sends the information entered by the user to the server.

[0763] Input: The initial registration information entered by the user.

[0764] Specific operation: The device sends the input form data as an HTTP POST request.

[0765] Output: User registration information sent to the server.

[0766] Step 3:

[0767] Server: Stores the received user information in a database and completes account registration.

[0768] Input: User registration information sent from the device.

[0769] Specific operation: The server creates a new user record in the database and returns a success response to the terminal.

[0770] Output: Saved user information, notification that account registration is complete.

[0771] Step 4:

[0772] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0773] Input: Promotional data such as product name, category, price, target customer, promotional budget, etc.

[0774] Specific behavior: A user enters product information and promotional data into an input form and clicks the submit button.

[0775] Step 5:

[0776] Terminal: Sends data entered by the user to the server.

[0777] Input: Product information and promotional data entered by the user.

[0778] Specific operation: The device sends the input form data as an HTTP POST request.

[0779] Output: Product information and promotional data sent to the server.

[0780] Step 6:

[0781] Server: Stores the received data in a database and prepares it for preprocessing.

[0782] Input: Product information and promotional data sent from your device.

[0783] Specific behavior: The server creates a product information record in the database and adds a pre-processing task to the queue.

[0784] Output: Stored product information, ready for preprocessing tasks.

[0785] Step 7:

[0786] Server: Preprocesses data such as product information and target customer demographics into a format suitable for the generative AI model.

[0787] Input: Product information and target customer data stored in the database.

[0788] What it does: The server removes incomplete and duplicate data, tokenizes text data, and normalizes numeric data.

[0789] Output: Preprocessed data.

[0790] Step 8:

[0791] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy.

[0792] Input: Preprocessed product information and target demographic data.

[0793] Specific operation: The server sends an API request to the generative AI model (e.g., GPT-3).

[0794] Output: The generated localized ad copy.

[0795] Step 9:

[0796] Server: Stores the generated ad copy in a database.

[0797] Input: Ad copy received from the generative AI model.

[0798] What happens: The server adds an ad copy record to the database.

[0799] Output: Saved ad copy.

[0800] Step 10:

[0801] Device: User logs in to their account to preview the generated ad text.

[0802] Input: Ad copy stored in the database.

[0803] Specific operation: The device retrieves advertising text data from the server and displays it to the user.

[0804] Output: The ad copy shown to the user.

[0805] Step 11:

[0806] User: Make any necessary adjustments to the previewed ad text.

[0807] Input: The ad copy you're previewing.

[0808] What happens: The user edits the ad copy and sends the changes from the device to the server.

[0809] Output: Tweaked ad copy.

[0810] Step 12:

[0811] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[0812] Input: User-tuned ad copy.

[0813] What happens: The server updates the ad copy record in the database and sets up a delivery task.

[0814] Output: Ready to save and distribute.

[0815] Step 13:

[0816] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[0817] Input: Fine-tuned ad copy, distribution platform information.

[0818] Specific behavior: The user enters distribution settings and sends them to the server.

[0819] Output: Distribution setting data.

[0820] Step 14:

[0821] Device: Sends distribution settings to the server.

[0822] Input: User's delivery setting data.

[0823] Specific operation: The device sends the distribution settings to the server as an HTTP POST request.

[0824] Output: The distribution settings sent to the server.

[0825] Step 15:

[0826] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[0827] Input: Saved ad copy and delivery settings data.

[0828] What happens: The server uses an external API (e.g. Instagram API) to post the ad and record the delivery status.

[0829] Output: A log of the ads served and their delivery status.

[0830] Step 16:

[0831] User: Enter the results of the sales promotion activities and send them to the server as sales performance data.

[0832] Input: Sales performance data (e.g., sales numbers, click rates, customer feedback, etc.).

[0833] Specific operation: The user enters sales result data into the form and sends it to the server.

[0834] Output: Sales performance data sent to the server.

[0835] Step 17:

[0836] Terminal: Sends sales performance data to the server.

[0837] Input: Sales performance data entered by the user.

[0838] Specific operation: The terminal sends the sales result data to the server as an HTTP POST request.

[0839] Output: Sales performance data sent to the server.

[0840] Step 18:

[0841] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[0842] Input: Sales performance data sent from the terminal.

[0843] What happens: The server creates a new sales record in the database and adds the task to the model's retraining batch.

[0844] Output: Stored sales performance data, updated generative AI model.

[0845] (Application example 1)

[0846] 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."

[0847] In today's advertising industry, the generation of region-specific advertising copy and a user-friendly interface are required to effectively promote products and services. Conventional systems have struggled to generate advertising copy that takes into account region-specific consumer behavior and preferences, and have not adequately developed a means for users to easily adjust and distribute advertising copy. Furthermore, it has been difficult to effectively collect performance data and reflect it in improving advertising copy. The present invention aims to solve these problems and realize the generation of region-specific advertising copy and its effective distribution.

[0848] 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.

[0849] In this invention, the server includes a means for a user to input information about the product or service being sold, the target customer demographic, and the sales promotion budget; a means for the server to preprocess the received information into a format suitable for the generative AI model; and a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data. This enables the generation of advertising copy that takes into account region-specific consumer behavior and preferences. The system also includes a means for a user to input data via voice input and eye tracking, and a means for fine-tuning the advertising copy based on voice instructions and gestures, providing a user-friendly interface and enabling the rapid and effective adjustment and distribution of advertising copy. Furthermore, the system includes a means for a user to input the results of promotional activities and send sales performance data to the server, allowing the collected performance data to be added as training data for the generative AI model, thereby continuously improving the accuracy and effectiveness of advertising copy.

[0850] "User" means any individual or entity that intends to sell goods or services using the System.

[0851] "Product or service information" means basic data about the product or service, including product name, category, price, etc.

[0852] A "target customer" is a specific consumer group for which a product or service is aimed, characterized by attributes such as age, gender, or region.

[0853] The "promotional budget" refers to the total budget allocated to advertising and marketing activities.

[0854] A "server" is a computer system used to receive, process, and store data from users.

[0855] A "generative AI model" refers to an artificial intelligence model that is trained to perform a specific task based on past data.

[0856] "Preprocessing" refers to processes such as data cleaning and normalization that are necessary for a generative AI model to efficiently analyze data.

[0857] "Advertising Copy" means a text message used to advertise a product or service.

[0858] "Preview" refers to a temporary display function that allows the user to check the generated advertising copy.

[0859] "Fine-tuning" refers to an operation in which a user manually corrects the details of the generated advertising copy.

[0860] A "designated platform" refers to the medium, such as social media or email, selected to deliver the advertising copy.

[0861] "Sales performance data" refers to data such as sales figures and customer feedback obtained as a result of promotional activities.

[0862] "Voice input" refers to a means of inputting a user's speech as data.

[0863] "Eye tracking" is a technology that tracks the movement of a user's eyes to input data or perform operations.

[0864] "Voice instructions" refers to a method in which a user issues commands to a system by voice.

[0865] "Gestures" are a method of operation in which you give instructions to a system using hand or body movements.

[0866] The present invention is a system for proposing sales promotions that are specialized for a region, and generates advertising copy that is specialized for each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance. A specific embodiment for this purpose is described below.

[0867] User Registration and Data Entry

[0868] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking and a voice input system. This makes it easy for users with visual impairments or who are unfamiliar with keyboard operation to create an account.

[0869] Entering product information and promotional data

[0870] Users input information about the product they want to sell (product name, category, price, etc.), target customer demographic, and sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. This information is received by the server and stored in a database.

[0871] Data preprocessing and analysis

[0872] The server preprocesses the received data, such as product information, target customer demographics, and budget, into a format suitable for the AI ​​model. It performs data cleaning and normalization to prepare the data in an optimal state for input into the model. This improves the accuracy of the AI ​​model and enables the generation of effective advertising copy.

[0873] Generating ad copy

[0874] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[0875] Examples of prompt sentences include "Input prompt for generative AI model: Tokyo, 20s-30s, latest trends, luxury sneakers" and "Input prompt for generative AI model: Osaka, 30s-50s, housewife, household appliances."

[0876] Preview and fine-tune your ad copy

[0877] The generated ad copy is saved in the user's account, and the user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures, allowing users to easily optimize their ad copy and achieve high advertising effectiveness.

[0878] Advertisement delivery and sales data collection

[0879] Finally, the advertising copy fine-tuned by the user is distributed by the server to the specified platform (such as social media or email). The user inputs sales performance data (sales figures, customer feedback, etc.) obtained as a result of the promotional activities and sends it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of the advertising copy are continuously improved.

[0880] As described above, this system generates region-specific advertising copy and is designed to realize effective sales promotion activities. It has a user-friendly interface and a feedback loop based on performance data, making it possible to optimize advertising effectiveness.

[0881] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0882] Step 1:

[0883] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking or voice input systems. The input information is sent by the device to a server, which stores it in a database.

[0884] Input: Username, Email Address, Password

[0885] Output: User information stored in the database

[0886] Step 2:

[0887] Users input information about the product they want to sell (product name, category, price, etc.), their target customer base, and their sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. The input information is sent by the device to the server, which then stores it in a database.

[0888] Input: Product name, category, price, target customer, promotion budget

[0889] Output: Product information stored in the database

[0890] Step 3:

[0891] The server preprocesses the received data, such as product information, target customer demographics, and promotional budgets, into a format suitable for the generative AI model. It performs processes such as data cleaning and normalization to prepare the data in an optimal state for input into the model. This preprocessing allows the model to analyze the data efficiently.

[0892] Input: Product information, target customer demographics, promotional budget

[0893] Output: Preprocessed data in a format that can be input into a generative AI model

[0894] Step 4:

[0895] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[0896] Input: Preprocessed data

[0897] Output: Localized ad copy

[0898] Step 5:

[0899] The generated ad copy is saved in the user's account by the server. The user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures. This allows users to easily optimize the ad copy and achieve high advertising effectiveness.

[0900] Input: Generated ad copy

[0901] Output: Tweaked ad copy

[0902] Step 6:

[0903] The server delivers the ad copy fine-tuned by the user to the specified platform (SNS, email, etc.), thereby effectively delivering locally-specific ads.

[0904] Input: Tweaked ad copy

[0905] Output: Delivery of advertising text to the specified platform

[0906] Step 7:

[0907] Users input sales performance data (sales figures, customer feedback, etc.) obtained as a result of their promotional activities and send it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of advertising copy is continuously improved.

[0908] Input: Sales performance data (sales numbers, customer feedback)

[0909] Output: Sales performance data added as training data for the generative AI model

[0910] 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.

[0911] MODE FOR CARRYING OUT THE INVENTION

[0912] The present invention relates to a region-specific sales promotion proposal system, and in particular to a system that generates more effective and attractive advertising copy and improves sales performance by combining an emotion engine that recognizes user emotions.

[0913] System configuration

[0914] 1. User registration and data entry

[0915] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[0916] Terminal: Sends the information entered by the user to the server.

[0917] Server: Stores the received user information in a database and completes account registration.

[0918] 2. Enter product information and promotional data

[0919] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[0920] Terminal: Sends data entered by the user to the server.

[0921] Server: Stores the received information in a database and prepares it for preprocessing.

[0922] 3. Data Preprocessing and Analysis

[0923] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[0924] 4. Generating advertising copy

[0925] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[0926] Server: Stores the generated ad copy in a database.

[0927] 5. Preview and fine-tune your ad copy

[0928] On the device: The user logs in to their account and previews the generated ad copy. The emotion engine then analyzes the user's visual and audio information to interpret their emotions.

[0929] Emotion engine: Automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and conversely, if the user is calm, it will suggest more toned-down language.

[0930] Users: can see the preview and either adopt the suggested wording or manually adjust it.

[0931] 6. Delivery of advertisements

[0932] User: Check the ad copy to be delivered and select the platform (social media, email, etc.) to deliver it.

[0933] Device: Sends distribution settings to the server.

[0934] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[0935] 7. Collecting sales results and feedback

[0936] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0937] Terminal: Sends sales performance data to the server.

[0938] Server: Stores the received sales performance data in a database and adds it to the generative AI model as training data.

[0939] Emotion Engine: User feedback is analyzed for emotion and reflected in sales performance data, allowing the generative AI model to improve advertising copy by taking user emotions into account.

[0940] Specific examples

[0941] Example 1: If a user sells luxury sneakers in Tokyo, they set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user reviews the ad on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests similarly emphasized copy. The user then adopts this and distributes the ad on social media.

[0942] Example 2: If a user sells home appliances in Osaka, they set their target demographic to housewives in their 30s-50s and input their price range and promotional budget. The generative AI model uses this information to generate advertising copy such as "Perfect for housewives in Osaka! The latest home appliances make housework easy!" If the emotion engine recognizes a "calm" state, the user can fine-tune the automatically suggested calm copy and distribute the ad through email marketing.

[0943] In this way, by combining an emotion engine that recognizes user emotions, the present invention enables more effective generation and fine-tuning of advertising copy, thereby realizing locally specialized sales promotion activities.

[0944] The processing flow will be explained below.

[0945] Step 1:

[0946] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[0947] Step 2:

[0948] The terminal transmits the user's input information to the server.

[0949] Step 3:

[0950] The server verifies the received user information and stores it in the database, completing the user account registration.

[0951] Step 4:

[0952] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[0953] Step 5:

[0954] The terminal transmits the user's input data to the server.

[0955] Step 6:

[0956] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[0957] Step 7:

[0958] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[0959] Step 8:

[0960] The server stores the generated ad copy in a database and associates it with the user's account.

[0961] Step 9:

[0962] The device displays a preview of the generated ad copy to the user, while the emotion engine analyzes the user's visual and audio information to read their emotions.

[0963] Step 10:

[0964] The emotion engine automatically generates suggestions for fine-tuning ad copy based on the user's emotional state. For example, it suggests emphasizing words when the user is "excited" and calming words when the user is "calm."

[0965] Step 11:

[0966] The user reviews the preview and either adopts the suggested wording or adjusts it manually.

[0967] Step 12:

[0968] The user checks the ad copy to be delivered and selects the platform (SNS, email, etc.) on which to deliver it.

[0969] Step 13:

[0970] The device sends the distribution settings to the server.

[0971] Step 14:

[0972] The server delivers the ad copy to the specified platform according to the settings.

[0973] Step 15:

[0974] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[0975] Step 16:

[0976] The terminal transmits the sales performance data to the server.

[0977] Step 17:

[0978] The sales performance data received by the server is stored in a database and added to the generative AI model as training data.

[0979] Step 18:

[0980] The emotion engine analyzes user feedback and reflects it in sales performance data, allowing for improvements to be made by taking user emotions into account when generating the next ad copy.

[0981] Example 2

[0982] 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."

[0983] Conventional sales promotion proposal systems have the problem of limited advertising effectiveness because they are unable to generate or fine-tune advertising copy that takes user emotions into account. Furthermore, it is difficult to automatically generate region-specific advertising copy, which increases the effort required for users to manually adjust it.

[0984] The specification processing by the specification 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 a user to input information on a product or service to be sold, a target customer demographic, and a sales promotion budget; means for a terminal to transmit the information input by the user to the server; means for the server to store the received information in a database and complete account registration; means for the server to preprocess the received information into a format suitable for the generative AI model based on the information received; means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; means for the server to store the generated advertising copy in a database; means for a user to preview and fine-tune the generated advertising copy; means for an emotion engine to analyze the user's emotional state and suggest fine-tuning the advertising copy; means for the server to distribute the advertising copy fine-tuned by the user to a specified platform; means for a user to input the results of sales promotion activities and transmit sales performance data to the server; and means for the server to add the sales performance data to the generative AI model as learning data to improve the quality of the advertising copy. This allows for effective generation and fine-tuning of advertising copy taking into account user emotions, and also makes it possible to propose sales promotions that are specific to the region.

[0985] "User" refers to any person or company that intends to use the System to sell goods or services.

[0986] "Terminal" refers to a device (e.g., PC, smartphone, tablet) that a user uses to input information and communicate with a server.

[0987] "Server" refers to the computer system that processes the received information, generates advertising copy using the generative AI model, and stores and distributes the data.

[0988] "Database" refers to a system in which a server stores user information, product information, generated advertising copy, sales performance data, etc.

[0989] "Generative AI models" refer to artificial intelligence algorithms or systems that generate region-specific advertising copy based on pre-processed data.

[0990] "Preview" refers to the screen display or interface that allows a user to preview the generated ad copy and make any necessary adjustments.

[0991] "Fine-tuning" refers to the act of modifying the content or expression of generated advertising copy based on suggestions from the user or the system.

[0992] An "emotion engine" refers to technology or software that analyzes a user's visual information and voice to recognize their emotional state and reflect the results in adjusting advertising copy.

[0993] "Promotional activities" refers to marketing and promotional activities carried out to promote the sale of products and services.

[0994] "Sales performance data" refers to data such as sales figures obtained as a result of promotional activities, customer feedback, and click rates.

[0995] The present invention is a localized promotion suggestion system that detects user emotions and generates and fine-tunes advertising copy based on the emotions. The system is implemented using the following specific hardware and software:

[0996] System Components

[0997] 1. User: Launches the application, creates an account, and uses the system. The user enters information about the product or service to be sold, the target customer demographic, and the promotional budget.

[0998] 2. Terminal: Using a device such as a PC, smartphone, or tablet, the user's input information is sent to the server.

[0999] 3. Server: The server manages the entire system, including the database, generative AI model, emotion engine, etc., and processes information. Specifically, it operates with the following configuration:

[1000] Data preprocessing and storage

[1001] The server receives product information and target customer demographic data sent by users and stores it in a database (e.g., MySQL). It then preprocesses the data into a format suitable for generative AI models (e.g., GPT-4). Preprocessing includes data cleaning, missing value imputation, and numerical normalization.

[1002] Generating ad copy

[1003] The server uses the preprocessed data to create prompts, which are then fed into a generative AI model to generate localized ad copy. The generated ad copy is then stored in a database. For example, the following prompts are used:

[1004] Example prompt:

[1005] "Generate advertising copy to sell high-end sneakers to young people in their 20s and 30s in Tokyo."

[1006] Fine-tuning with the Emotion Engine

[1007] When a user previews the generated ad copy, an emotion engine (e.g., Affectiva SDK) analyzes the user's visual and audio information to recognize their emotional state. The emotion engine then suggests fine-tuning the ad copy based on the user's emotions.

[1008] Ad copy delivery

[1009] After the user has reviewed the ad copy and made any necessary adjustments, the server distributes the final ad copy to the specified distribution platform (e.g., social media, email marketing, etc.) The distribution status of the ad is monitored in real time.

[1010] Sales performance collection and feedback

[1011] Users input sales performance data (sales figures, customer feedback, click rates, etc.) as a result of their promotional activities and send it to the server. The server stores this data in a database and adds it to the generative AI model as training data. Furthermore, the emotion engine analyzes user feedback and reflects it in the sales performance data to improve the quality of advertising copy.

[1012] Specific examples

[1013] For example, if a user sells high-end sneakers in Tokyo, the following process would occur:

[1014] 1. The user sets the target customer demographic as young people in their 20s and 30s, and enters the price range and promotional budget.

[1015] 2. Based on this information, the server inputs a prompt into the generative AI model, generating advertising text such as, "The latest trend! Get your hands on the luxury sneakers that are the talk of Tokyo!"

[1016] 3. The user checks the ad copy on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests more emphatic copy.

[1017] 4. Users adopt it and distribute ads on social media.

[1018] As described above, the present invention can generate and fine-tune advertising copy taking into account user emotions, and can effectively carry out regionally specific sales promotion activities.

[1019] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1020] Step 1:

[1021] User: Launches the application and opens the page for creating a new account. The user enters initial registration information such as username, email address, and password, and presses the "Submit" button.

[1022] Input: Username, Email Address, Password

[1023] Output: Registration information sent

[1024] Step 2:

[1025] Terminal: Sends the registration information entered by the user to the server.

[1026] Input: Username, Email Address, Password

[1027] Output: Registration information arrives at the server

[1028] Step 3:

[1029] Server: Save the received user information in a database (e.g. MySQL) and register a new account.

[1030] Input: Registration information

[1031] Output: User information is saved in the database

[1032] What it does: Uses an SQL statement to insert user information into the database.

[1033] Step 4:

[1034] Server: Generates a notification that the user has successfully registered for an account.

[1035] Input: User information

[1036] Output: Registration completion notification

[1037] Specific behavior: Calls the notification generation routine to create a registration completion message.

[1038] Step 5:

[1039] Server: Sends notification data to the device.

[1040] Input: Registration completion notification

[1041] Output: Notification data arrives on the device

[1042] Specific operation: Uses the network sending API to send a registration completion notification to the device.

[1043] Step 6:

[1044] Terminal: Display a message to the user confirming registration.

[1045] Input: Notification data

[1046] Output: A message appears saying registration is complete.

[1047] Specific behavior: Calls a routine that displays a notification message in the user interface.

[1048] Step 7:

[1049] User: Log in to your account and open the product information entry page. Enter the product name, category, price, target customer, and promotional budget, then click the "Submit" button.

[1050] Input: Product name, category, price, target customer, promotion budget

[1051] Output: Product information is sent

[1052] Step 8:

[1053] Terminal: Sends the product information entered by the user to the server.

[1054] Input: Product name, category, price, target customer, promotion budget

[1055] Output: Product information arrives at the server

[1056] Step 9:

[1057] Server: Store the received product information in a database.

[1058] Input: Product information

[1059] Output: Product information saved in the database

[1060] Specific behavior: Uses SQL statements to insert product information into the database.

[1061] Step 10:

[1062] Server: Retrieves product information and target customer data from the database and performs preprocessing.

[1063] Input: Product information, target customer information

[1064] Output: Preprocessed data

[1065] Specific operations: Perform data cleaning, missing value imputation, numerical normalization, etc.

[1066] Step 11:

[1067] Server: Creates prompts using preprocessed data and inputs them into the generative AI model.

[1068] Input: Preprocessed data

[1069] Output: Ad copy

[1070] Specific operation: Prompt text is generated based on the preprocessed data and passed to a generative AI model to generate advertising copy.

[1071] Step 12:

[1072] Server: Stores the generated ad copy in a database.

[1073] Input: Ad copy

[1074] Output: Ad copy saved in database

[1075] What it does: Uses an SQL statement to insert ad copy into the database.

[1076] Step 13:

[1077] Users: Log in to your account and open the preview screen to view the generated ad text.

[1078] Input: User information

[1079] Output: Preview of ad copy

[1080] Step 14:

[1081] Device: The generated ad copy and preview screen are displayed to the user.

[1082] Input: Ad copy

[1083] Output: Preview screen

[1084] What it does: Calls a routine that displays ad copy and previews in the user interface.

[1085] Step 15:

[1086] Emotion engine: Analyzes the user's visual and audio information during the preview and recognizes the user's emotions.

[1087] Input: User's visual information, audio data

[1088] Output: Emotional state data

[1089] What it does: Analyzes emotions using image and audio analysis algorithms.

[1090] Step 16:

[1091] Emotion engine: Suggests ad copy tweaks based on the user's emotional state.

[1092] Input: Emotional state data, advertising text

[1093] Output: Tweak suggestions

[1094] What it does: Analyzes emotional state and generates suggested revisions to ad copy based on that.

[1095] Step 17:

[1096] User: Review the suggested ad copy and manually adjust it as needed.

[1097] Input: Tweak suggestions, ad copy

[1098] Output: Final adjusted ad copy

[1099] Step 18:

[1100] Device: Sends the adjustments to the server.

[1101] Input: Final ad copy

[1102] Output: The adjustments are sent to the server

[1103] Step 19:

[1104] Server: Saves the final adjusted ad copy in the database.

[1105] Input: Final ad copy

[1106] Output: Adjusted ad copy saved in database

[1107] What it does: Uses an SQL statement to insert the final ad copy into the database.

[1108] Step 20:

[1109] User: Select the ad copy you want to deliver and choose the delivery platform (SNS, email, etc.).

[1110] Input: Finalized ad copy, distribution platform

[1111] Output: Distribution setting data

[1112] Step 21:

[1113] Device: Sends distribution settings to the server.

[1114] Input: Distribution setting data

[1115] Output: Distribution settings are sent to the server

[1116] Step 22:

[1117] Server: Serves ads to the specified platform.

[1118] Input: Delivery setting data, finalized ad wording

[1119] Output: Ad is served

[1120] What it does: Uses network APIs to send ad copy to distribution platforms.

[1121] Step 23:

[1122] User: Enters sales performance data as a result of promotional activities and sends it to the server.

[1123] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[1124] Output: Sales performance data arrives at the server

[1125] Step 24:

[1126] Server: Stores the received sales data in a database.

[1127] Input: Sales performance data

[1128] Output: Sales performance data is saved in the database

[1129] Specific behavior: Uses SQL statements to insert sales performance data into the database.

[1130] Step 25:

[1131] Emotion engine: Analyzes user feedback emotionally and reflects it in sales performance data.

[1132] Input: Sales performance data, feedback data

[1133] Output: Sentiment analysis data

[1134] What it does: Analyzes emotions using image and audio analysis algorithms.

[1135] Step 26:

[1136] Server: Sales performance data along with the analysis results are added as learning data to the generated AI model to improve the quality of advertising wording.

[1137] Input: Sales performance data, sentiment analysis data

[1138] Output: An updated generative AI model

[1139] Specific operation: Use a learning algorithm to update the parameters of the generative AI model.

[1140] (Application example 2)

[1141] 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."

[1142] Conventional advertising systems lacked the ability to analyze user sentiment and fine-tune ad copy when generating region-specific ad copy. This resulted in ad copy that was less likely to resonate with target customers, resulting in insufficient effectiveness of sales promotion activities. Furthermore, when users fine-tuned the generated ad copy, the effectiveness of the suggested copy was often not optimized based on user sentiment, making fine-tuning the ad copy complicated and time-consuming. Furthermore, when sales performance data was added as training data for the AI ​​model, the results of sentiment analysis were not reflected, making it difficult to use the results in generating the next ad copy.

[1143] 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.

[1144] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer demographic, and their promotional budget; a means for the server to preprocess the received information into a format suitable for the generation AI model; a means for the generation AI model to generate region-specific advertising text based on the preprocessed data; a means for the server to save the generated advertising text by linking it to the user's account; a means for the user to preview and fine-tune the generated advertising text; a means for the user to fine-tune the generated advertising text using a sentiment analysis engine that analyzes user sentiment; a means for the server to distribute the user-tuned advertising text to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generation AI model as training data. This allows the results of the user's sentiment analysis to be reflected in the generation of region-specific advertising text, enabling the generation and fine-tuning of more effective advertising text that appeals to the target customer demographic. Furthermore, by reflecting the results of the sentiment analysis in the sales performance data, the accuracy of the next advertising text generation can be improved.

[1145] "User" refers to an individual or corporation that uses a system or application.

[1146] "Product or service information" refers to information including data such as the name, category, and price of the product being sold.

[1147] "Target customer demographic" refers to the demographic of customers who are likely to purchase a particular product or service.

[1148] "Promotional budget" refers to the total amount of funds allocated for carrying out promotional activities.

[1149] "Server" refers to a central location that stores, processes, and distributes data.

[1150] A "generative AI model" refers to an artificial intelligence algorithm that generates advertising copy and other information based on input data.

[1151] "Preprocessing" refers to the process of converting data into a format suitable for a generative AI model.

[1152] "Region-specific advertising language" refers to advertising content that is suited to the preferences and behavior of consumers in a specific region.

[1153] An "account" refers to authentication information used to identify a user and associate individual data and settings with the user.

[1154] "Preview" refers to a display that allows you to check the generated advertising copy before it is actually delivered.

[1155] "Tweaking" refers to making small changes to optimize ad wording or settings.

[1156] An "emotion analysis engine" refers to a device or software that has the function of analyzing emotions from data such as a user's facial expressions and voice.

[1157] "Platform" refers to a medium, such as social media or email marketing, used to deliver advertisements.

[1158] "Sales performance data" refers to data including sales and customer feedback obtained as a result of actual sales promotion activities.

[1159] "Training data" refers to the dataset that a generative AI model uses to improve its performance.

[1160] The present invention is a system for generating region-specific advertising copy and fine-tuning it by analyzing user emotions. Specific embodiments of the system are described below.

[1161] 1. Overview of the entire system

[1162] The system consists of a user's device, a server, a generative AI model, a sentiment analysis engine, and an ad distribution platform. This system can generate and fine-tune effective advertising copy based on data entered by the user, such as product information and target customer demographics.

[1163] 2. System Configuration

[1164] Terminal

[1165] It provides a means for users to input product and service information, target customer demographics, and sales promotion budgets. This information is sent to the server, which will be described later.

[1166] server

[1167] The server uses the following software and hardware:

[1168] Database: Stores user and product information.

[1169] Generative AI model: An artificial intelligence algorithm for generating localized ad copy.

[1170] Sentiment analysis engine: Analyzes user sentiment and fine-tunes ad copy.

[1171] 3. Data Flow and Processing

[1172] User Data Entry

[1173] Users input detailed information about the products or services they want to sell, their target customer demographic, and their sales budget through the terminal. For example, if a user wants to sell high-end sneakers to people in their 20s and 30s, they enter this information into the terminal and send it to the server.

[1174] Pretreatment

[1175] The server preprocesses the received information, converting it into a format that can be properly processed by the generative AI model. Data cleaning and numerical normalization are performed at this stage.

[1176] Generating ad copy

[1177] The generative AI model then uses the pre-processed data to generate localized ad copy, taking into account local consumer behavior and preference data.

[1178] Sentiment analysis and ad copy fine-tuning

[1179] When a user previews the generated ad copy, a sentiment analysis engine analyzes the user's visual and audio data and suggests tweaks based on their emotions. For example, if the user is excited, the engine suggests making the ad copy more emphatic.

[1180] Ad serving

[1181] The finely tuned advertising copy is then distributed via the server to the specified platform (such as social media or email marketing).

[1182] Collection and feedback of sales performance data

[1183] Users input the results of their promotional activities and send them to the server as sales performance data. The server adds this data to the generative AI model as learning data and uses it to generate advertising copy from the next time onwards.

[1184] 4. Examples and prompts

[1185] Examples:

[1186] For example, if a user is selling luxury sneakers, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of the town!", and if the sentiment analysis engine recognizes an "excited" state, it will suggest emphasized copy.

[1187] Example prompt sentence:

[1188] "Generate ad copy that includes the keyword 'latest trend' for young people in their 20s and 30s who want to sell high-end sneakers."

[1189] This system generates effective advertising copy that is region-specific and based on sentiment analysis, and makes it possible to utilize the results of users' promotional activities in generating the next advertising copy.

[1190] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1191] Step 1:

[1192] The user starts up the device and creates an account. During the initial registration, they enter information such as their username, email address, and password. This information is sent to the server and stored in a database.

[1193] Input: Username, Email Address, Password

[1194] Output: Confirmation of account registration, save user information to database

[1195] Step 2:

[1196] The user inputs information about the product they want to sell (product name, category, price, etc.), the target customer demographic, and the sales promotion budget. This information is sent from the terminal to the server and stored in a database.

[1197] Input: Product name, category, price, target customer, promotion budget

[1198] Output: Product information saved in a database

[1199] Step 3:

[1200] The server preprocesses the received data, such as product information and target customer demographics, into a format suitable for the generative AI model. Preprocessing involves data cleaning and numerical normalization.

[1201] Input: Product information, target customer demographic, promotional budget

[1202] Output: Preprocessed data

[1203] Step 4:

[1204] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy, which takes into account consumer behavior and preference data for each region to create effective advertising copy.

[1205] Input: Preprocessed data

[1206] Output: Generated ad copy

[1207] Step 5:

[1208] The generated advertising copy is stored by the server and linked to the user's account.

[1209] Input: Generated ad copy

[1210] Output: Ad copy saved in the user account

[1211] Step 6:

[1212] The user logs in to their account on their device and previews the generated ad copy.

[1213] Input: User credentials

[1214] Output: Preview of ad copy

[1215] Step 7:

[1216] The emotion analysis engine analyzes the user's visual and audio data and automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and if they are calm, it will suggest more calm language.

[1217] Input: User's emotional data (visual information, audio data)

[1218] Output: Sentiment-based ad copy suggestions

[1219] Step 8:

[1220] The user reviews the preview and either accepts the sentiment engine's suggestions or manually adjusts the ad copy.

[1221] Input: Sentiment engine suggested text, user manual adjustment

[1222] Output: Finalized ad copy

[1223] Step 9:

[1224] The server delivers the confirmed advertising text to the specified platform (SNS, email, etc.).

[1225] Input: Confirmed ad copy, distribution platform information

[1226] Output: Served ad copy

[1227] Step 10:

[1228] The user inputs the results of the sales promotion activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1229] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[1230] Output: Sales performance data saved in database

[1231] Step 11:

[1232] The server adds the received sales performance data to the generation AI model as learning data, improving the accuracy of future advertising copy generation.

[1233] Input: Sales performance data

[1234] Output: Sales performance data added as training data

[1235] This step-by-step process enables the system to generate and deliver effective advertising copy that is localized and based on sentiment analysis.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] [Third embodiment]

[1240] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1241] 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.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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).

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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."

[1252] MODE FOR CARRYING OUT THE INVENTION

[1253] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[1254] System configuration

[1255] 1. User registration and data entry

[1256] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[1257] Terminal: Sends the information entered by the user to the server.

[1258] Server: Stores the received user information in a database and completes account registration.

[1259] 2. Enter product information and promotional data

[1260] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[1261] Terminal: Sends data entered by the user to the server.

[1262] Server: Stores the received data in a database and prepares it for preprocessing.

[1263] 3. Data Preprocessing and Analysis

[1264] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[1265] 4. Generating advertising copy

[1266] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[1267] For example, "The latest trend! Get it now!" for young people in Tokyo, or "The latest home appliances that make housework easier, on special offer for a limited time!" for housewives in Osaka.

[1268] Server: Stores the generated ad copy in a database.

[1269] 5. Preview and fine-tune your ad copy

[1270] Device: User logs in to their account and previews the generated ad text.

[1271] Users: Preview the ad copy and make any necessary adjustments.

[1272] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[1273] 6. Delivery of advertisements

[1274] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[1275] Device: Sends distribution settings to the server.

[1276] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[1277] 7. Collecting sales results and feedback

[1278] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1279] Terminal: Sends sales performance data to the server.

[1280] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[1281] Specific examples

[1282] Example 1: If a user sells luxury sneakers in Tokyo, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as, "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user can preview the copy, make any necessary adjustments, and then distribute the ad via social media.

[1283] Example 2: A user selling home appliances in Osaka sets their target demographic as housewives in their 30s-50s, inputs their price range, and enters their promotional budget. The generative AI model uses this information to generate advertising copy such as, "Perfect for housewives in Osaka! The latest home appliances make housework easy!" The user can preview and fine-tune the copy and distribute it through email marketing.

[1284] In this way, the regional promotion proposal system generates advertising copy optimized for each region based on user-entered data, and distributes it to realize effective promotional activities.

[1285] The processing flow will be explained below.

[1286] Step 1:

[1287] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[1288] Step 2:

[1289] The terminal transmits the user's input information to the server.

[1290] Step 3:

[1291] The server verifies the received user information and stores it in the database, completing the user account registration.

[1292] Step 4:

[1293] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[1294] Step 5:

[1295] The terminal transmits the user's input data to the server.

[1296] Step 6:

[1297] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[1298] Step 7:

[1299] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[1300] Step 8:

[1301] The server stores the generated ad copy in a database and associates it with the user's account.

[1302] Step 9:

[1303] The device will then display a preview of the generated ad copy to the user, who can review it and make any necessary adjustments.

[1304] Step 10:

[1305] The user checks the ad text that has been fine-tuned and selects the platform (social media, email, etc.) to distribute it.

[1306] Step 11:

[1307] The device sends the distribution settings to the server.

[1308] Step 12:

[1309] The server delivers the ad copy to the specified platform according to the delivery settings.

[1310] Step 13:

[1311] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1312] Step 14:

[1313] The terminal transmits the sales performance data to the server.

[1314] Step 15:

[1315] The server stores the received sales performance data in a database and adds it to the generative AI model as training data, enabling the model to generate advertising copy with even greater accuracy.

[1316] Example 1

[1317] 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."

[1318] In conventional sales promotion proposal systems, users had to individually research consumer behavior and preferences in each region and then create sales promotion copy based on that information, which was a very time-consuming process, making it difficult to generate effective advertising copy.Another issue was the difficulty of creating a feedback loop to evaluate the effectiveness of the generated advertising copy and reflect it in the next sales promotion.

[1319] 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.

[1320] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer base, and their sales promotion budget; a means for a terminal to send the information input by the user to the server; a means for the server to store the received information in a database and preprocess it into a format suitable for the generative AI model; a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; a means for the server to store the generated advertising copy by linking it to the user's account; a means for the terminal for the user to preview and fine-tune the generated advertising copy; a means for the server to distribute the user-adjusted advertising copy to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generative AI model as training data. This enables users to efficiently generate and distribute advertising copy that takes into account consumer behavior and preferences in each region, and to create a feedback loop that evaluates its effectiveness and reflects it in subsequent promotional activities.

[1321] "User" refers to a person who uses the system to input product or service information and review, fine-tune, and distribute the generated advertising copy.

[1322] "Terminal" refers to the device that a user uses to access the system and enter information, preview advertising copy, set up distribution, etc.

[1323] "Server" refers to the device that receives information sent by users and manages data storage, pre-processing, and the generation and distribution of advertising copy using generative AI models.

[1324] "Database" refers to a system connected to a server for storing user information, product information, generated advertising copy, sales performance data, etc.

[1325] A "generative AI model" refers to an artificial intelligence model that uses a specific algorithm to generate advertising copy based on input data.

[1326] "Preprocessing" refers to processes such as cleaning, numeric normalization, and tokenization to convert data into a format suitable for generative AI models.

[1327] "Ad copy" refers to the text message generated by the generative AI model to appeal to a specific region or target demographic.

[1328] "Preview" refers to a system function that allows users to check the generated advertising copy before it is distributed.

[1329] "Fine-tuning" refers to the operation by the user to make changes to the generated advertising copy and refine it into its final form.

[1330] "Platform" refers to external services such as social media and email used to distribute advertising copy.

[1331] "Sales performance data" refers to data such as sales figures, customer feedback, and click rates collected as a result of promotional activities.

[1332] A "feedback loop" refers to the process of retraining the generative AI model based on sales performance data and using it to generate advertising copy from the next time onwards.

[1333] MODE FOR CARRYING OUT THE INVENTION

[1334] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[1335] The main components of this system are as follows:

[1336] 1. User registration and data entry

[1337] User: A user launches a system application and creates an account by entering information such as a username, email address, and password. For example, a user enters "Yamada Taro," "taro@example.com," and "password123."

[1338] Terminal: The information entered by the user is sent to the server using an HTTP POST request.

[1339] Server: Stores the received user information in a database and completes the account registration.

[1340] 2. Enter product information and promotional data

[1341] User: Enter information about the product they want to sell (e.g., product name "luxury sneakers," category "fashion," price "15,000 yen," target customer demographic "young people in their 20s and 30s," promotional budget "100,000 yen").

[1342] Terminal: Sends data entered by the user to the server.

[1343] Server: Stores the received data in a database and prepares it for preprocessing.

[1344] 3. Data Preprocessing and Analysis

[1345] Server: Preprocesses product information and target demographic data into a format suitable for generative AI models. Preprocessing includes data cleaning, numeric normalization, and tokenization.

[1346] 4. Generating advertising copy

[1347] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy, taking into account local consumer behavior and preference data.

[1348] Example: Based on the information "luxury sneakers" and "young people in their 20s and 30s," the ad copy generated is "The latest trend! Get the luxury sneakers that are the talk of Tokyo!"

[1349] Server: Stores the generated ad copy in a database.

[1350] 5. Preview and fine-tune your ad copy

[1351] Device: User logs in to their account to preview the generated ad text.

[1352] User: Make any necessary adjustments to the previewed ad text, for example, changing "Tokyo" to "Shibuya."

[1353] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[1354] 6. Delivery of advertisements

[1355] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) to deliver it to. For example, select "Instagram" and set the posting time.

[1356] Device: Sends distribution settings to the server.

[1357] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[1358] 7. Collecting sales results and feedback

[1359] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1360] For example, enter "Sales: 50, Click Rate: 15%, Feedback: Positive".

[1361] Terminal: Sends sales performance data to the server.

[1362] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[1363] This allows users to efficiently generate region-specific advertising copy and furthermore reflect the effect of that copy in their next sales promotion activity.

[1364] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1365] Step 1:

[1366] User: Launches the application and creates an account by entering information such as username, email address, and password.

[1367] Input: Initial registration information such as username, email address, and password.

[1368] What happens: A user enters information into a form and clicks the submit button.

[1369] Step 2:

[1370] Terminal: Sends the information entered by the user to the server.

[1371] Input: The initial registration information entered by the user.

[1372] Specific operation: The device sends the input form data as an HTTP POST request.

[1373] Output: User registration information sent to the server.

[1374] Step 3:

[1375] Server: Stores the received user information in a database and completes account registration.

[1376] Input: User registration information sent from the device.

[1377] Specific operation: The server creates a new user record in the database and returns a success response to the terminal.

[1378] Output: Saved user information, notification that account registration is complete.

[1379] Step 4:

[1380] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[1381] Input: Promotional data such as product name, category, price, target customer, promotional budget, etc.

[1382] Specific behavior: A user enters product information and promotional data into an input form and clicks the submit button.

[1383] Step 5:

[1384] Terminal: Sends data entered by the user to the server.

[1385] Input: Product information and promotional data entered by the user.

[1386] Specific operation: The device sends the input form data as an HTTP POST request.

[1387] Output: Product information and promotional data sent to the server.

[1388] Step 6:

[1389] Server: Stores the received data in a database and prepares it for preprocessing.

[1390] Input: Product information and promotional data sent from your device.

[1391] Specific behavior: The server creates a product information record in the database and adds a pre-processing task to the queue.

[1392] Output: Stored product information, ready for preprocessing tasks.

[1393] Step 7:

[1394] Server: Preprocesses data such as product information and target customer demographics into a format suitable for the generative AI model.

[1395] Input: Product information and target customer data stored in the database.

[1396] What it does: The server removes incomplete and duplicate data, tokenizes text data, and normalizes numeric data.

[1397] Output: Preprocessed data.

[1398] Step 8:

[1399] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy.

[1400] Input: Preprocessed product information and target demographic data.

[1401] Specific operation: The server sends an API request to the generative AI model (e.g., GPT-3).

[1402] Output: The generated localized ad copy.

[1403] Step 9:

[1404] Server: Stores the generated ad copy in a database.

[1405] Input: Ad copy received from the generative AI model.

[1406] What happens: The server adds an ad copy record to the database.

[1407] Output: Saved ad copy.

[1408] Step 10:

[1409] Device: User logs in to their account to preview the generated ad text.

[1410] Input: Ad copy stored in the database.

[1411] Specific operation: The device retrieves advertising text data from the server and displays it to the user.

[1412] Output: The ad copy shown to the user.

[1413] Step 11:

[1414] User: Make any necessary adjustments to the previewed ad text.

[1415] Input: The ad copy you're previewing.

[1416] What happens: The user edits the ad copy and sends the changes from the device to the server.

[1417] Output: Tweaked ad copy.

[1418] Step 12:

[1419] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[1420] Input: User-tuned ad copy.

[1421] What happens: The server updates the ad copy record in the database and sets up a delivery task.

[1422] Output: Ready to save and distribute.

[1423] Step 13:

[1424] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[1425] Input: Fine-tuned ad copy, distribution platform information.

[1426] Specific behavior: The user enters distribution settings and sends them to the server.

[1427] Output: Distribution setting data.

[1428] Step 14:

[1429] Device: Sends distribution settings to the server.

[1430] Input: User's delivery setting data.

[1431] Specific operation: The device sends the distribution settings to the server as an HTTP POST request.

[1432] Output: The distribution settings sent to the server.

[1433] Step 15:

[1434] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[1435] Input: Saved ad copy and delivery settings data.

[1436] What happens: The server uses an external API (e.g. Instagram API) to post the ad and record the delivery status.

[1437] Output: A log of the ads served and their delivery status.

[1438] Step 16:

[1439] User: Enter the results of the sales promotion activities and send them to the server as sales performance data.

[1440] Input: Sales performance data (e.g., sales numbers, click rates, customer feedback, etc.).

[1441] Specific operation: The user enters sales result data into the form and sends it to the server.

[1442] Output: Sales performance data sent to the server.

[1443] Step 17:

[1444] Terminal: Sends sales performance data to the server.

[1445] Input: Sales performance data entered by the user.

[1446] Specific operation: The terminal sends the sales result data to the server as an HTTP POST request.

[1447] Output: Sales performance data sent to the server.

[1448] Step 18:

[1449] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[1450] Input: Sales performance data sent from the terminal.

[1451] What happens: The server creates a new sales record in the database and adds the task to the model's retraining batch.

[1452] Output: Stored sales performance data, updated generative AI model.

[1453] (Application example 1)

[1454] 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."

[1455] In today's advertising industry, the generation of region-specific advertising copy and a user-friendly interface are required to effectively promote products and services. Conventional systems have struggled to generate advertising copy that takes into account region-specific consumer behavior and preferences, and have not adequately developed a means for users to easily adjust and distribute advertising copy. Furthermore, it has been difficult to effectively collect performance data and reflect it in improving advertising copy. The present invention aims to solve these problems and realize the generation of region-specific advertising copy and its effective distribution.

[1456] 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.

[1457] In this invention, the server includes a means for a user to input information about the product or service being sold, the target customer demographic, and the sales promotion budget; a means for the server to preprocess the received information into a format suitable for the generative AI model; and a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data. This enables the generation of advertising copy that takes into account region-specific consumer behavior and preferences. The system also includes a means for a user to input data via voice input and eye tracking, and a means for fine-tuning the advertising copy based on voice instructions and gestures, providing a user-friendly interface and enabling the rapid and effective adjustment and distribution of advertising copy. Furthermore, the system includes a means for a user to input the results of promotional activities and send sales performance data to the server, allowing the collected performance data to be added as training data for the generative AI model, thereby continuously improving the accuracy and effectiveness of advertising copy.

[1458] "User" means any individual or entity that intends to sell goods or services using the System.

[1459] "Product or service information" means basic data about the product or service, including product name, category, price, etc.

[1460] A "target customer" is a specific consumer group for which a product or service is aimed, characterized by attributes such as age, gender, or region.

[1461] The "promotional budget" refers to the total budget allocated to advertising and marketing activities.

[1462] A "server" is a computer system used to receive, process, and store data from users.

[1463] A "generative AI model" refers to an artificial intelligence model that is trained to perform a specific task based on past data.

[1464] "Preprocessing" refers to processes such as data cleaning and normalization that are necessary for a generative AI model to efficiently analyze data.

[1465] "Advertising Copy" means a text message used to advertise a product or service.

[1466] "Preview" refers to a temporary display function that allows the user to check the generated advertising copy.

[1467] "Fine-tuning" refers to an operation in which a user manually corrects the details of the generated advertising copy.

[1468] A "designated platform" refers to the medium, such as social media or email, selected to deliver the advertising copy.

[1469] "Sales performance data" refers to data such as sales figures and customer feedback obtained as a result of promotional activities.

[1470] "Voice input" refers to a means of inputting a user's speech as data.

[1471] "Eye tracking" is a technology that tracks the movement of a user's eyes to input data or perform operations.

[1472] "Voice instructions" refers to a method in which a user issues commands to a system by voice.

[1473] "Gestures" are a method of operation in which you give instructions to a system using hand or body movements.

[1474] The present invention is a system for proposing sales promotions that are specialized for a region, and generates advertising copy that is specialized for each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance. A specific embodiment for this purpose is described below.

[1475] User Registration and Data Entry

[1476] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking and a voice input system. This makes it easy for users with visual impairments or who are unfamiliar with keyboard operation to create an account.

[1477] Entering product information and promotional data

[1478] Users input information about the product they want to sell (product name, category, price, etc.), target customer demographic, and sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. This information is received by the server and stored in a database.

[1479] Data preprocessing and analysis

[1480] The server preprocesses the received data, such as product information, target customer demographics, and budget, into a format suitable for the AI ​​model. It performs data cleaning and normalization to prepare the data in an optimal state for input into the model. This improves the accuracy of the AI ​​model and enables the generation of effective advertising copy.

[1481] Generating ad copy

[1482] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[1483] Examples of prompt sentences include "Input prompt for generative AI model: Tokyo, 20s-30s, latest trends, luxury sneakers" and "Input prompt for generative AI model: Osaka, 30s-50s, housewife, household appliances."

[1484] Preview and fine-tune your ad copy

[1485] The generated ad copy is saved in the user's account, and the user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures, allowing users to easily optimize their ad copy and achieve high advertising effectiveness.

[1486] Advertisement delivery and sales data collection

[1487] Finally, the advertising copy fine-tuned by the user is distributed by the server to the specified platform (such as social media or email). The user inputs sales performance data (sales figures, customer feedback, etc.) obtained as a result of the promotional activities and sends it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of the advertising copy are continuously improved.

[1488] As described above, this system generates region-specific advertising copy and is designed to realize effective sales promotion activities. It has a user-friendly interface and a feedback loop based on performance data, making it possible to optimize advertising effectiveness.

[1489] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1490] Step 1:

[1491] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking or voice input systems. The input information is sent by the device to a server, which stores it in a database.

[1492] Input: Username, Email Address, Password

[1493] Output: User information stored in the database

[1494] Step 2:

[1495] Users input information about the product they want to sell (product name, category, price, etc.), their target customer base, and their sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. The input information is sent by the device to the server, which then stores it in a database.

[1496] Input: Product name, category, price, target customer, promotion budget

[1497] Output: Product information stored in the database

[1498] Step 3:

[1499] The server preprocesses the received data, such as product information, target customer demographics, and promotional budgets, into a format suitable for the generative AI model. It performs processes such as data cleaning and normalization to prepare the data in an optimal state for input into the model. This preprocessing allows the model to analyze the data efficiently.

[1500] Input: Product information, target customer demographics, promotional budget

[1501] Output: Preprocessed data in a format that can be input into a generative AI model

[1502] Step 4:

[1503] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[1504] Input: Preprocessed data

[1505] Output: Localized ad copy

[1506] Step 5:

[1507] The generated ad copy is saved in the user's account by the server. The user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures. This allows users to easily optimize the ad copy and achieve high advertising effectiveness.

[1508] Input: Generated ad copy

[1509] Output: Tweaked ad copy

[1510] Step 6:

[1511] The server delivers the ad copy fine-tuned by the user to the specified platform (SNS, email, etc.), thereby effectively delivering locally-specific ads.

[1512] Input: Tweaked ad copy

[1513] Output: Delivery of advertising text to the specified platform

[1514] Step 7:

[1515] Users input sales performance data (sales figures, customer feedback, etc.) obtained as a result of their promotional activities and send it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of advertising copy is continuously improved.

[1516] Input: Sales performance data (sales numbers, customer feedback)

[1517] Output: Sales performance data added as training data for the generative AI model

[1518] 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.

[1519] MODE FOR CARRYING OUT THE INVENTION

[1520] The present invention relates to a region-specific sales promotion proposal system, and in particular to a system that generates more effective and attractive advertising copy and improves sales performance by combining an emotion engine that recognizes user emotions.

[1521] System configuration

[1522] 1. User registration and data entry

[1523] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[1524] Terminal: Sends the information entered by the user to the server.

[1525] Server: Stores the received user information in a database and completes account registration.

[1526] 2. Enter product information and promotional data

[1527] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[1528] Terminal: Sends data entered by the user to the server.

[1529] Server: Stores the received information in a database and prepares it for preprocessing.

[1530] 3. Data Preprocessing and Analysis

[1531] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[1532] 4. Generating advertising copy

[1533] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[1534] Server: Stores the generated ad copy in a database.

[1535] 5. Preview and fine-tune your ad copy

[1536] On the device: The user logs in to their account and previews the generated ad copy. The emotion engine then analyzes the user's visual and audio information to interpret their emotions.

[1537] Emotion engine: Automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and conversely, if the user is calm, it will suggest more toned-down language.

[1538] Users: can see the preview and either adopt the suggested wording or manually adjust it.

[1539] 6. Delivery of advertisements

[1540] User: Check the ad copy to be delivered and select the platform (social media, email, etc.) to deliver it.

[1541] Device: Sends distribution settings to the server.

[1542] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[1543] 7. Collecting sales results and feedback

[1544] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1545] Terminal: Sends sales performance data to the server.

[1546] Server: Stores the received sales performance data in a database and adds it to the generative AI model as training data.

[1547] Emotion Engine: User feedback is analyzed for emotion and reflected in sales performance data, allowing the generative AI model to improve advertising copy by taking user emotions into account.

[1548] Specific examples

[1549] Example 1: If a user sells luxury sneakers in Tokyo, they set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user reviews the ad on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests similarly emphasized copy. The user then adopts this and distributes the ad on social media.

[1550] Example 2: If a user sells home appliances in Osaka, they set their target demographic to housewives in their 30s-50s and input their price range and promotional budget. The generative AI model uses this information to generate advertising copy such as "Perfect for housewives in Osaka! The latest home appliances make housework easy!" If the emotion engine recognizes a "calm" state, the user can fine-tune the automatically suggested calm copy and distribute the ad through email marketing.

[1551] In this way, by combining an emotion engine that recognizes user emotions, the present invention enables more effective generation and fine-tuning of advertising copy, thereby realizing locally specialized sales promotion activities.

[1552] The processing flow will be explained below.

[1553] Step 1:

[1554] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[1555] Step 2:

[1556] The terminal transmits the user's input information to the server.

[1557] Step 3:

[1558] The server verifies the received user information and stores it in the database, completing the user account registration.

[1559] Step 4:

[1560] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[1561] Step 5:

[1562] The terminal transmits the user's input data to the server.

[1563] Step 6:

[1564] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[1565] Step 7:

[1566] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[1567] Step 8:

[1568] The server stores the generated ad copy in a database and associates it with the user's account.

[1569] Step 9:

[1570] The device displays a preview of the generated ad copy to the user, while the emotion engine analyzes the user's visual and audio information to read their emotions.

[1571] Step 10:

[1572] The emotion engine automatically generates suggestions for fine-tuning ad copy based on the user's emotional state. For example, it suggests emphasizing words when the user is "excited" and calming words when the user is "calm."

[1573] Step 11:

[1574] The user reviews the preview and either adopts the suggested wording or adjusts it manually.

[1575] Step 12:

[1576] The user checks the ad copy to be delivered and selects the platform (SNS, email, etc.) on which to deliver it.

[1577] Step 13:

[1578] The device sends the distribution settings to the server.

[1579] Step 14:

[1580] The server delivers the ad copy to the specified platform according to the settings.

[1581] Step 15:

[1582] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1583] Step 16:

[1584] The terminal transmits the sales performance data to the server.

[1585] Step 17:

[1586] The sales performance data received by the server is stored in a database and added to the generative AI model as training data.

[1587] Step 18:

[1588] The emotion engine analyzes user feedback and reflects it in sales performance data, allowing for improvements to be made by taking user emotions into account when generating the next ad copy.

[1589] Example 2

[1590] 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."

[1591] Conventional sales promotion proposal systems have the problem of limited advertising effectiveness because they are unable to generate or fine-tune advertising copy that takes user emotions into account. Furthermore, it is difficult to automatically generate region-specific advertising copy, which increases the effort required for users to manually adjust it.

[1592] The specification processing by the specification 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 a user to input information on a product or service to be sold, a target customer demographic, and a sales promotion budget; means for a terminal to transmit the information input by the user to the server; means for the server to store the received information in a database and complete account registration; means for the server to preprocess the received information into a format suitable for the generative AI model based on the information received; means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; means for the server to store the generated advertising copy in a database; means for a user to preview and fine-tune the generated advertising copy; means for an emotion engine to analyze the user's emotional state and suggest fine-tuning the advertising copy; means for the server to distribute the advertising copy fine-tuned by the user to a specified platform; means for a user to input the results of sales promotion activities and transmit sales performance data to the server; and means for the server to add the sales performance data to the generative AI model as learning data to improve the quality of the advertising copy. This allows for effective generation and fine-tuning of advertising copy taking into account user emotions, and also makes it possible to propose sales promotions that are specific to the region.

[1593] "User" refers to any person or company that intends to use the System to sell goods or services.

[1594] "Terminal" refers to a device (e.g., PC, smartphone, tablet) that a user uses to input information and communicate with a server.

[1595] "Server" refers to the computer system that processes the received information, generates advertising copy using the generative AI model, and stores and distributes the data.

[1596] "Database" refers to a system in which a server stores user information, product information, generated advertising copy, sales performance data, etc.

[1597] "Generative AI models" refer to artificial intelligence algorithms or systems that generate region-specific advertising copy based on pre-processed data.

[1598] "Preview" refers to the screen display or interface that allows a user to preview the generated ad copy and make any necessary adjustments.

[1599] "Fine-tuning" refers to the act of modifying the content or expression of generated advertising copy based on suggestions from the user or the system.

[1600] An "emotion engine" refers to technology or software that analyzes a user's visual information and voice to recognize their emotional state and reflect the results in adjusting advertising copy.

[1601] "Promotional activities" refers to marketing and promotional activities carried out to promote the sale of products and services.

[1602] "Sales performance data" refers to data such as sales figures obtained as a result of promotional activities, customer feedback, and click rates.

[1603] The present invention is a localized promotion suggestion system that detects user emotions and generates and fine-tunes advertising copy based on the emotions. The system is implemented using the following specific hardware and software:

[1604] System Components

[1605] 1. User: Launches the application, creates an account, and uses the system. The user enters information about the product or service to be sold, the target customer demographic, and the promotional budget.

[1606] 2. Terminal: Using a device such as a PC, smartphone, or tablet, the user's input information is sent to the server.

[1607] 3. Server: The server manages the entire system, including the database, generative AI model, emotion engine, etc., and processes information. Specifically, it operates with the following configuration:

[1608] Data preprocessing and storage

[1609] The server receives product information and target customer demographic data sent by users and stores it in a database (e.g., MySQL). It then preprocesses the data into a format suitable for generative AI models (e.g., GPT-4). Preprocessing includes data cleaning, missing value imputation, and numerical normalization.

[1610] Generating ad copy

[1611] The server uses the preprocessed data to create prompts, which are then fed into a generative AI model to generate localized ad copy. The generated ad copy is then stored in a database. For example, the following prompts are used:

[1612] Example prompt:

[1613] "Generate advertising copy to sell high-end sneakers to young people in their 20s and 30s in Tokyo."

[1614] Fine-tuning with the Emotion Engine

[1615] When a user previews the generated ad copy, an emotion engine (e.g., Affectiva SDK) analyzes the user's visual and audio information to recognize their emotional state. The emotion engine then suggests fine-tuning the ad copy based on the user's emotions.

[1616] Ad copy delivery

[1617] After the user has reviewed the ad copy and made any necessary adjustments, the server distributes the final ad copy to the specified distribution platform (e.g., social media, email marketing, etc.) The distribution status of the ad is monitored in real time.

[1618] Sales performance collection and feedback

[1619] Users input sales performance data (sales figures, customer feedback, click rates, etc.) as a result of their promotional activities and send it to the server. The server stores this data in a database and adds it to the generative AI model as training data. Furthermore, the emotion engine analyzes user feedback and reflects it in the sales performance data to improve the quality of advertising copy.

[1620] Specific examples

[1621] For example, if a user sells high-end sneakers in Tokyo, the following process would occur:

[1622] 1. The user sets the target customer demographic as young people in their 20s and 30s, and enters the price range and promotional budget.

[1623] 2. Based on this information, the server inputs a prompt into the generative AI model, generating advertising text such as, "The latest trend! Get your hands on the luxury sneakers that are the talk of Tokyo!"

[1624] 3. The user checks the ad copy on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests more emphatic copy.

[1625] 4. Users adopt it and distribute ads on social media.

[1626] As described above, the present invention can generate and fine-tune advertising copy taking into account user emotions, and can effectively carry out regionally specific sales promotion activities.

[1627] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1628] Step 1:

[1629] User: Launches the application and opens the page for creating a new account. The user enters initial registration information such as username, email address, and password, and presses the "Submit" button.

[1630] Input: Username, Email Address, Password

[1631] Output: Registration information sent

[1632] Step 2:

[1633] Terminal: Sends the registration information entered by the user to the server.

[1634] Input: Username, Email Address, Password

[1635] Output: Registration information arrives at the server

[1636] Step 3:

[1637] Server: Save the received user information in a database (e.g. MySQL) and register a new account.

[1638] Input: Registration information

[1639] Output: User information is saved in the database

[1640] What it does: Uses an SQL statement to insert user information into the database.

[1641] Step 4:

[1642] Server: Generates a notification that the user has successfully registered for an account.

[1643] Input: User information

[1644] Output: Registration completion notification

[1645] Specific behavior: Calls the notification generation routine to create a registration completion message.

[1646] Step 5:

[1647] Server: Sends notification data to the device.

[1648] Input: Registration completion notification

[1649] Output: Notification data arrives on the device

[1650] Specific operation: Uses the network sending API to send a registration completion notification to the device.

[1651] Step 6:

[1652] Terminal: Display a message to the user confirming registration.

[1653] Input: Notification data

[1654] Output: A message appears saying registration is complete.

[1655] Specific behavior: Calls a routine that displays a notification message in the user interface.

[1656] Step 7:

[1657] User: Log in to your account and open the product information entry page. Enter the product name, category, price, target customer, and promotional budget, then click the "Submit" button.

[1658] Input: Product name, category, price, target customer, promotion budget

[1659] Output: Product information is sent

[1660] Step 8:

[1661] Terminal: Sends the product information entered by the user to the server.

[1662] Input: Product name, category, price, target customer, promotion budget

[1663] Output: Product information arrives at the server

[1664] Step 9:

[1665] Server: Store the received product information in a database.

[1666] Input: Product information

[1667] Output: Product information saved in the database

[1668] Specific behavior: Uses SQL statements to insert product information into the database.

[1669] Step 10:

[1670] Server: Retrieves product information and target customer data from the database and performs preprocessing.

[1671] Input: Product information, target customer information

[1672] Output: Preprocessed data

[1673] Specific operations: Perform data cleaning, missing value imputation, numerical normalization, etc.

[1674] Step 11:

[1675] Server: Creates prompts using preprocessed data and inputs them into the generative AI model.

[1676] Input: Preprocessed data

[1677] Output: Ad copy

[1678] Specific operation: Prompt text is generated based on the preprocessed data and passed to a generative AI model to generate advertising copy.

[1679] Step 12:

[1680] Server: Stores the generated ad copy in a database.

[1681] Input: Ad copy

[1682] Output: Ad copy saved in database

[1683] What it does: Uses an SQL statement to insert ad copy into the database.

[1684] Step 13:

[1685] Users: Log in to your account and open the preview screen to view the generated ad text.

[1686] Input: User information

[1687] Output: Preview of ad copy

[1688] Step 14:

[1689] Device: The generated ad copy and preview screen are displayed to the user.

[1690] Input: Ad copy

[1691] Output: Preview screen

[1692] What it does: Calls a routine that displays ad copy and previews in the user interface.

[1693] Step 15:

[1694] Emotion engine: Analyzes the user's visual and audio information during the preview and recognizes the user's emotions.

[1695] Input: User's visual information, audio data

[1696] Output: Emotional state data

[1697] What it does: Analyzes emotions using image and audio analysis algorithms.

[1698] Step 16:

[1699] Emotion engine: Suggests ad copy tweaks based on the user's emotional state.

[1700] Input: Emotional state data, advertising text

[1701] Output: Tweak suggestions

[1702] What it does: Analyzes emotional state and generates suggested revisions to ad copy based on that.

[1703] Step 17:

[1704] User: Review the suggested ad copy and manually adjust it as needed.

[1705] Input: Tweak suggestions, ad copy

[1706] Output: Final adjusted ad copy

[1707] Step 18:

[1708] Device: Sends the adjustments to the server.

[1709] Input: Final ad copy

[1710] Output: The adjustments are sent to the server

[1711] Step 19:

[1712] Server: Saves the final adjusted ad copy in the database.

[1713] Input: Final ad copy

[1714] Output: Adjusted ad copy saved in database

[1715] What it does: Uses an SQL statement to insert the final ad copy into the database.

[1716] Step 20:

[1717] User: Select the ad copy you want to deliver and choose the delivery platform (SNS, email, etc.).

[1718] Input: Finalized ad copy, distribution platform

[1719] Output: Distribution setting data

[1720] Step 21:

[1721] Device: Sends distribution settings to the server.

[1722] Input: Distribution setting data

[1723] Output: Distribution settings are sent to the server

[1724] Step 22:

[1725] Server: Serves ads to the specified platform.

[1726] Input: Delivery setting data, finalized ad wording

[1727] Output: Ad is served

[1728] What it does: Uses network APIs to send ad copy to distribution platforms.

[1729] Step 23:

[1730] User: Enters sales performance data as a result of promotional activities and sends it to the server.

[1731] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[1732] Output: Sales performance data arrives at the server

[1733] Step 24:

[1734] Server: Stores the received sales data in a database.

[1735] Input: Sales performance data

[1736] Output: Sales performance data is saved in the database

[1737] Specific behavior: Uses SQL statements to insert sales performance data into the database.

[1738] Step 25:

[1739] Emotion engine: Analyzes user feedback emotionally and reflects it in sales performance data.

[1740] Input: Sales performance data, feedback data

[1741] Output: Sentiment analysis data

[1742] What it does: Analyzes emotions using image and audio analysis algorithms.

[1743] Step 26:

[1744] Server: Sales performance data along with the analysis results are added as learning data to the generated AI model to improve the quality of advertising wording.

[1745] Input: Sales performance data, sentiment analysis data

[1746] Output: An updated generative AI model

[1747] Specific operation: Use a learning algorithm to update the parameters of the generative AI model.

[1748] (Application example 2)

[1749] 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."

[1750] Conventional advertising systems lacked the ability to analyze user sentiment and fine-tune ad copy when generating region-specific ad copy. This resulted in ad copy that was less likely to resonate with target customers, resulting in insufficient effectiveness of sales promotion activities. Furthermore, when users fine-tuned the generated ad copy, the effectiveness of the suggested copy was often not optimized based on user sentiment, making fine-tuning the ad copy complicated and time-consuming. Furthermore, when sales performance data was added as training data for the AI ​​model, the results of sentiment analysis were not reflected, making it difficult to use the results in generating the next ad copy.

[1751] 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.

[1752] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer demographic, and their promotional budget; a means for the server to preprocess the received information into a format suitable for the generation AI model; a means for the generation AI model to generate region-specific advertising text based on the preprocessed data; a means for the server to save the generated advertising text by linking it to the user's account; a means for the user to preview and fine-tune the generated advertising text; a means for the user to fine-tune the generated advertising text using a sentiment analysis engine that analyzes user sentiment; a means for the server to distribute the user-tuned advertising text to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generation AI model as training data. This allows the results of the user's sentiment analysis to be reflected in the generation of region-specific advertising text, enabling the generation and fine-tuning of more effective advertising text that appeals to the target customer demographic. Furthermore, by reflecting the results of the sentiment analysis in the sales performance data, the accuracy of the next advertising text generation can be improved.

[1753] "User" refers to an individual or corporation that uses a system or application.

[1754] "Product or service information" refers to information including data such as the name, category, and price of the product being sold.

[1755] "Target customer demographic" refers to the demographic of customers who are likely to purchase a particular product or service.

[1756] "Promotional budget" refers to the total amount of funds allocated for carrying out promotional activities.

[1757] "Server" refers to a central location that stores, processes, and distributes data.

[1758] A "generative AI model" refers to an artificial intelligence algorithm that generates advertising copy and other information based on input data.

[1759] "Preprocessing" refers to the process of converting data into a format suitable for a generative AI model.

[1760] "Region-specific advertising language" refers to advertising content that is suited to the preferences and behavior of consumers in a specific region.

[1761] An "account" refers to authentication information used to identify a user and associate individual data and settings with the user.

[1762] "Preview" refers to a display that allows you to check the generated advertising copy before it is actually delivered.

[1763] "Tweaking" refers to making small changes to optimize ad wording or settings.

[1764] An "emotion analysis engine" refers to a device or software that has the function of analyzing emotions from data such as a user's facial expressions and voice.

[1765] "Platform" refers to a medium, such as social media or email marketing, used to deliver advertisements.

[1766] "Sales performance data" refers to data including sales and customer feedback obtained as a result of actual sales promotion activities.

[1767] "Training data" refers to the dataset that a generative AI model uses to improve its performance.

[1768] The present invention is a system for generating region-specific advertising copy and fine-tuning it by analyzing user emotions. Specific embodiments of the system are described below.

[1769] 1. Overview of the entire system

[1770] The system consists of a user's device, a server, a generative AI model, a sentiment analysis engine, and an ad distribution platform. This system can generate and fine-tune effective advertising copy based on data entered by the user, such as product information and target customer demographics.

[1771] 2. System Configuration

[1772] Terminal

[1773] It provides a means for users to input product and service information, target customer demographics, and sales promotion budgets. This information is sent to the server, which will be described later.

[1774] server

[1775] The server uses the following software and hardware:

[1776] Database: Stores user and product information.

[1777] Generative AI model: An artificial intelligence algorithm for generating localized ad copy.

[1778] Sentiment analysis engine: Analyzes user sentiment and fine-tunes ad copy.

[1779] 3. Data Flow and Processing

[1780] User Data Entry

[1781] Users input detailed information about the products or services they want to sell, their target customer demographic, and their sales budget through the terminal. For example, if a user wants to sell high-end sneakers to people in their 20s and 30s, they enter this information into the terminal and send it to the server.

[1782] Pretreatment

[1783] The server preprocesses the received information, converting it into a format that can be properly processed by the generative AI model. Data cleaning and numerical normalization are performed at this stage.

[1784] Generating ad copy

[1785] The generative AI model then uses the pre-processed data to generate localized ad copy, taking into account local consumer behavior and preference data.

[1786] Sentiment analysis and ad copy fine-tuning

[1787] When a user previews the generated ad copy, a sentiment analysis engine analyzes the user's visual and audio data and suggests tweaks based on their emotions. For example, if the user is excited, the engine suggests making the ad copy more emphatic.

[1788] Ad serving

[1789] The finely tuned advertising copy is then distributed via the server to the specified platform (such as social media or email marketing).

[1790] Collection and feedback of sales performance data

[1791] Users input the results of their promotional activities and send them to the server as sales performance data. The server adds this data to the generative AI model as learning data and uses it to generate advertising copy from the next time onwards.

[1792] 4. Examples and prompts

[1793] Examples:

[1794] For example, if a user is selling luxury sneakers, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of the town!", and if the sentiment analysis engine recognizes an "excited" state, it will suggest emphasized copy.

[1795] Example prompt sentence:

[1796] "Generate ad copy that includes the keyword 'latest trend' for young people in their 20s and 30s who want to sell high-end sneakers."

[1797] This system generates effective advertising copy that is region-specific and based on sentiment analysis, and makes it possible to utilize the results of users' promotional activities in generating the next advertising copy.

[1798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1799] Step 1:

[1800] The user starts up the device and creates an account. During the initial registration, they enter information such as their username, email address, and password. This information is sent to the server and stored in a database.

[1801] Input: Username, Email Address, Password

[1802] Output: Confirmation of account registration, save user information to database

[1803] Step 2:

[1804] The user inputs information about the product they want to sell (product name, category, price, etc.), the target customer demographic, and the sales promotion budget. This information is sent from the terminal to the server and stored in a database.

[1805] Input: Product name, category, price, target customer, promotion budget

[1806] Output: Product information saved in a database

[1807] Step 3:

[1808] The server preprocesses the received data, such as product information and target customer demographics, into a format suitable for the generative AI model. Preprocessing involves data cleaning and numerical normalization.

[1809] Input: Product information, target customer demographic, promotional budget

[1810] Output: Preprocessed data

[1811] Step 4:

[1812] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy, which takes into account consumer behavior and preference data for each region to create effective advertising copy.

[1813] Input: Preprocessed data

[1814] Output: Generated ad copy

[1815] Step 5:

[1816] The generated advertising copy is stored by the server and linked to the user's account.

[1817] Input: Generated ad copy

[1818] Output: Ad copy saved in the user account

[1819] Step 6:

[1820] The user logs in to their account on their device and previews the generated ad copy.

[1821] Input: User credentials

[1822] Output: Preview of ad copy

[1823] Step 7:

[1824] The emotion analysis engine analyzes the user's visual and audio data and automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and if they are calm, it will suggest more calm language.

[1825] Input: User's emotional data (visual information, audio data)

[1826] Output: Sentiment-based ad copy suggestions

[1827] Step 8:

[1828] The user reviews the preview and either accepts the sentiment engine's suggestions or manually adjusts the ad copy.

[1829] Input: Sentiment engine suggested text, user manual adjustment

[1830] Output: Finalized ad copy

[1831] Step 9:

[1832] The server delivers the confirmed advertising text to the specified platform (SNS, email, etc.).

[1833] Input: Confirmed ad copy, distribution platform information

[1834] Output: Served ad copy

[1835] Step 10:

[1836] The user inputs the results of the sales promotion activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1837] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[1838] Output: Sales performance data saved in database

[1839] Step 11:

[1840] The server adds the received sales performance data to the generation AI model as learning data, improving the accuracy of future advertising copy generation.

[1841] Input: Sales performance data

[1842] Output: Sales performance data added as training data

[1843] This step-by-step process enables the system to generate and deliver effective advertising copy that is localized and based on sentiment analysis.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] [Fourth embodiment]

[1848] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1849] 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.

[1850] 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).

[1851] 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.

[1852] 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.

[1853] 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).

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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."

[1861] MODE FOR CARRYING OUT THE INVENTION

[1862] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[1863] System configuration

[1864] 1. User registration and data entry

[1865] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[1866] Terminal: Sends the information entered by the user to the server.

[1867] Server: Stores the received user information in a database and completes account registration.

[1868] 2. Enter product information and promotional data

[1869] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[1870] Terminal: Sends data entered by the user to the server.

[1871] Server: Stores the received data in a database and prepares it for preprocessing.

[1872] 3. Data Preprocessing and Analysis

[1873] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[1874] 4. Generating advertising copy

[1875] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[1876] For example, "The latest trend! Get it now!" for young people in Tokyo, or "The latest home appliances that make housework easier, on special offer for a limited time!" for housewives in Osaka.

[1877] Server: Stores the generated ad copy in a database.

[1878] 5. Preview and fine-tune your ad copy

[1879] Device: User logs in to their account and previews the generated ad text.

[1880] Users: Preview the ad copy and make any necessary adjustments.

[1881] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[1882] 6. Delivery of advertisements

[1883] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[1884] Device: Sends distribution settings to the server.

[1885] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[1886] 7. Collecting sales results and feedback

[1887] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1888] Terminal: Sends sales performance data to the server.

[1889] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[1890] Specific examples

[1891] Example 1: If a user sells luxury sneakers in Tokyo, they can set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as, "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user can preview the copy, make any necessary adjustments, and then distribute the ad via social media.

[1892] Example 2: A user selling home appliances in Osaka sets their target demographic as housewives in their 30s-50s, inputs their price range, and enters their promotional budget. The generative AI model uses this information to generate advertising copy such as, "Perfect for housewives in Osaka! The latest home appliances make housework easy!" The user can preview and fine-tune the copy and distribute it through email marketing.

[1893] In this way, the regional promotion proposal system generates advertising copy optimized for each region based on user-entered data, and distributes it to realize effective promotional activities.

[1894] The processing flow will be explained below.

[1895] Step 1:

[1896] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[1897] Step 2:

[1898] The terminal transmits the user's input information to the server.

[1899] Step 3:

[1900] The server verifies the received user information and stores it in the database, completing the user account registration.

[1901] Step 4:

[1902] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[1903] Step 5:

[1904] The terminal transmits the user's input data to the server.

[1905] Step 6:

[1906] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[1907] Step 7:

[1908] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[1909] Step 8:

[1910] The server stores the generated ad copy in a database and associates it with the user's account.

[1911] Step 9:

[1912] The device will then display a preview of the generated ad copy to the user, who can review it and make any necessary adjustments.

[1913] Step 10:

[1914] The user checks the ad text that has been fine-tuned and selects the platform (social media, email, etc.) to distribute it.

[1915] Step 11:

[1916] The device sends the distribution settings to the server.

[1917] Step 12:

[1918] The server delivers the ad copy to the specified platform according to the delivery settings.

[1919] Step 13:

[1920] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1921] Step 14:

[1922] The terminal transmits the sales performance data to the server.

[1923] Step 15:

[1924] The server stores the received sales performance data in a database and adds it to the generative AI model as training data, enabling the model to generate advertising copy with even greater accuracy.

[1925] Example 1

[1926] 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."

[1927] In conventional sales promotion proposal systems, users had to individually research consumer behavior and preferences in each region and then create sales promotion copy based on that information, which was a very time-consuming process, making it difficult to generate effective advertising copy.Another issue was the difficulty of creating a feedback loop to evaluate the effectiveness of the generated advertising copy and reflect it in the next sales promotion.

[1928] 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.

[1929] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer base, and their sales promotion budget; a means for a terminal to send the information input by the user to the server; a means for the server to store the received information in a database and preprocess it into a format suitable for the generative AI model; a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; a means for the server to store the generated advertising copy by linking it to the user's account; a means for the terminal for the user to preview and fine-tune the generated advertising copy; a means for the server to distribute the user-adjusted advertising copy to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generative AI model as training data. This enables users to efficiently generate and distribute advertising copy that takes into account consumer behavior and preferences in each region, and to create a feedback loop that evaluates its effectiveness and reflects it in subsequent promotional activities.

[1930] "User" refers to a person who uses the system to input product or service information and review, fine-tune, and distribute the generated advertising copy.

[1931] "Terminal" refers to the device that a user uses to access the system and enter information, preview advertising copy, set up distribution, etc.

[1932] "Server" refers to the device that receives information sent by users and manages data storage, pre-processing, and the generation and distribution of advertising copy using generative AI models.

[1933] "Database" refers to a system connected to a server for storing user information, product information, generated advertising copy, sales performance data, etc.

[1934] A "generative AI model" refers to an artificial intelligence model that uses a specific algorithm to generate advertising copy based on input data.

[1935] "Preprocessing" refers to processes such as cleaning, numeric normalization, and tokenization to convert data into a format suitable for generative AI models.

[1936] "Ad copy" refers to the text message generated by the generative AI model to appeal to a specific region or target demographic.

[1937] "Preview" refers to a system function that allows users to check the generated advertising copy before it is distributed.

[1938] "Fine-tuning" refers to the operation by the user to make changes to the generated advertising copy and refine it into its final form.

[1939] "Platform" refers to external services such as social media and email used to distribute advertising copy.

[1940] "Sales performance data" refers to data such as sales figures, customer feedback, and click rates collected as a result of promotional activities.

[1941] A "feedback loop" refers to the process of retraining the generative AI model based on sales performance data and using it to generate advertising copy from the next time onwards.

[1942] MODE FOR CARRYING OUT THE INVENTION

[1943] The present invention relates to a region-specific sales promotion proposal system, specifically a system that generates advertising copy specific to each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance.

[1944] The main components of this system are as follows:

[1945] 1. User registration and data entry

[1946] User: A user launches a system application and creates an account by entering information such as a username, email address, and password. For example, a user enters "Yamada Taro," "taro@example.com," and "password123."

[1947] Terminal: The information entered by the user is sent to the server using an HTTP POST request.

[1948] Server: Stores the received user information in a database and completes the account registration.

[1949] 2. Enter product information and promotional data

[1950] User: Enter information about the product they want to sell (e.g., product name "luxury sneakers," category "fashion," price "15,000 yen," target customer demographic "young people in their 20s and 30s," promotional budget "100,000 yen").

[1951] Terminal: Sends data entered by the user to the server.

[1952] Server: Stores the received data in a database and prepares it for preprocessing.

[1953] 3. Data Preprocessing and Analysis

[1954] Server: Preprocesses product information and target demographic data into a format suitable for generative AI models. Preprocessing includes data cleaning, numeric normalization, and tokenization.

[1955] 4. Generating advertising copy

[1956] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy, taking into account local consumer behavior and preference data.

[1957] Example: Based on the information "luxury sneakers" and "young people in their 20s and 30s," the ad copy generated is "The latest trend! Get the luxury sneakers that are the talk of Tokyo!"

[1958] Server: Stores the generated ad copy in a database.

[1959] 5. Preview and fine-tune your ad copy

[1960] Device: User logs in to their account to preview the generated ad text.

[1961] User: Make any necessary adjustments to the previewed ad text, for example, changing "Tokyo" to "Shibuya."

[1962] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[1963] 6. Delivery of advertisements

[1964] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) to deliver it to. For example, select "Instagram" and set the posting time.

[1965] Device: Sends distribution settings to the server.

[1966] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[1967] 7. Collecting sales results and feedback

[1968] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[1969] For example, enter "Sales: 50, Click Rate: 15%, Feedback: Positive".

[1970] Terminal: Sends sales performance data to the server.

[1971] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[1972] This allows users to efficiently generate region-specific advertising copy and furthermore reflect the effect of that copy in their next sales promotion activity.

[1973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1974] Step 1:

[1975] User: Launches the application and creates an account by entering information such as username, email address, and password.

[1976] Input: Initial registration information such as username, email address, and password.

[1977] What happens: A user enters information into a form and clicks the submit button.

[1978] Step 2:

[1979] Terminal: Sends the information entered by the user to the server.

[1980] Input: The initial registration information entered by the user.

[1981] Specific operation: The device sends the input form data as an HTTP POST request.

[1982] Output: User registration information sent to the server.

[1983] Step 3:

[1984] Server: Stores the received user information in a database and completes account registration.

[1985] Input: User registration information sent from the device.

[1986] Specific operation: The server creates a new user record in the database and returns a success response to the terminal.

[1987] Output: Saved user information, notification that account registration is complete.

[1988] Step 4:

[1989] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[1990] Input: Promotional data such as product name, category, price, target customer, promotional budget, etc.

[1991] Specific behavior: A user enters product information and promotional data into an input form and clicks the submit button.

[1992] Step 5:

[1993] Terminal: Sends data entered by the user to the server.

[1994] Input: Product information and promotional data entered by the user.

[1995] Specific operation: The device sends the input form data as an HTTP POST request.

[1996] Output: Product information and promotional data sent to the server.

[1997] Step 6:

[1998] Server: Stores the received data in a database and prepares it for preprocessing.

[1999] Input: Product information and promotional data sent from your device.

[2000] Specific behavior: The server creates a product information record in the database and adds a pre-processing task to the queue.

[2001] Output: Stored product information, ready for preprocessing tasks.

[2002] Step 7:

[2003] Server: Preprocesses data such as product information and target customer demographics into a format suitable for the generative AI model.

[2004] Input: Product information and target customer data stored in the database.

[2005] What it does: The server removes incomplete and duplicate data, tokenizes text data, and normalizes numeric data.

[2006] Output: Preprocessed data.

[2007] Step 8:

[2008] Server: The pre-processed data is fed into a generative AI model to generate localized ad copy.

[2009] Input: Preprocessed product information and target demographic data.

[2010] Specific operation: The server sends an API request to the generative AI model (e.g., GPT-3).

[2011] Output: The generated localized ad copy.

[2012] Step 9:

[2013] Server: Stores the generated ad copy in a database.

[2014] Input: Ad copy received from the generative AI model.

[2015] What happens: The server adds an ad copy record to the database.

[2016] Output: Saved ad copy.

[2017] Step 10:

[2018] Device: User logs in to their account to preview the generated ad text.

[2019] Input: Ad copy stored in the database.

[2020] Specific operation: The device retrieves advertising text data from the server and displays it to the user.

[2021] Output: The ad copy shown to the user.

[2022] Step 11:

[2023] User: Make any necessary adjustments to the previewed ad text.

[2024] Input: The ad copy you're previewing.

[2025] What happens: The user edits the ad copy and sends the changes from the device to the server.

[2026] Output: Tweaked ad copy.

[2027] Step 12:

[2028] Server: Stores the fine-tuned ad copy and prepares it for delivery.

[2029] Input: User-tuned ad copy.

[2030] What happens: The server updates the ad copy record in the database and sets up a delivery task.

[2031] Output: Ready to save and distribute.

[2032] Step 13:

[2033] User: Check the ad copy to be delivered and specify the platform (e.g., social media, email, etc.) on which to deliver it.

[2034] Input: Fine-tuned ad copy, distribution platform information.

[2035] Specific behavior: The user enters distribution settings and sends them to the server.

[2036] Output: Distribution setting data.

[2037] Step 14:

[2038] Device: Sends distribution settings to the server.

[2039] Input: User's delivery setting data.

[2040] Specific operation: The device sends the distribution settings to the server as an HTTP POST request.

[2041] Output: The distribution settings sent to the server.

[2042] Step 15:

[2043] Server: Deliver ads to the specified platform and monitor delivery status in real time.

[2044] Input: Saved ad copy and delivery settings data.

[2045] What happens: The server uses an external API (e.g. Instagram API) to post the ad and record the delivery status.

[2046] Output: A log of the ads served and their delivery status.

[2047] Step 16:

[2048] User: Enter the results of the sales promotion activities and send them to the server as sales performance data.

[2049] Input: Sales performance data (e.g., sales numbers, click rates, customer feedback, etc.).

[2050] Specific operation: The user enters sales result data into the form and sends it to the server.

[2051] Output: Sales performance data sent to the server.

[2052] Step 17:

[2053] Terminal: Sends sales performance data to the server.

[2054] Input: Sales performance data entered by the user.

[2055] Specific operation: The terminal sends the sales result data to the server as an HTTP POST request.

[2056] Output: Sales performance data sent to the server.

[2057] Step 18:

[2058] Server: Stores sales performance data in a database and adds it to the generative AI model as training data.

[2059] Input: Sales performance data sent from the terminal.

[2060] What happens: The server creates a new sales record in the database and adds the task to the model's retraining batch.

[2061] Output: Stored sales performance data, updated generative AI model.

[2062] (Application example 1)

[2063] 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."

[2064] In today's advertising industry, the generation of region-specific advertising copy and a user-friendly interface are required to effectively promote products and services. Conventional systems have struggled to generate advertising copy that takes into account region-specific consumer behavior and preferences, and have not adequately developed a means for users to easily adjust and distribute advertising copy. Furthermore, it has been difficult to effectively collect performance data and reflect it in improving advertising copy. The present invention aims to solve these problems and realize the generation of region-specific advertising copy and its effective distribution.

[2065] 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.

[2066] In this invention, the server includes a means for a user to input information about the product or service being sold, the target customer demographic, and the sales promotion budget; a means for the server to preprocess the received information into a format suitable for the generative AI model; and a means for the generative AI model to generate region-specific advertising copy based on the preprocessed data. This enables the generation of advertising copy that takes into account region-specific consumer behavior and preferences. The system also includes a means for a user to input data via voice input and eye tracking, and a means for fine-tuning the advertising copy based on voice instructions and gestures, providing a user-friendly interface and enabling the rapid and effective adjustment and distribution of advertising copy. Furthermore, the system includes a means for a user to input the results of promotional activities and send sales performance data to the server, allowing the collected performance data to be added as training data for the generative AI model, thereby continuously improving the accuracy and effectiveness of advertising copy.

[2067] "User" means any individual or entity that intends to sell goods or services using the System.

[2068] "Product or service information" means basic data about the product or service, including product name, category, price, etc.

[2069] A "target customer" is a specific consumer group for which a product or service is aimed, characterized by attributes such as age, gender, or region.

[2070] The "promotional budget" refers to the total budget allocated to advertising and marketing activities.

[2071] A "server" is a computer system used to receive, process, and store data from users.

[2072] A "generative AI model" refers to an artificial intelligence model that is trained to perform a specific task based on past data.

[2073] "Preprocessing" refers to processes such as data cleaning and normalization that are necessary for a generative AI model to efficiently analyze data.

[2074] "Advertising Copy" means a text message used to advertise a product or service.

[2075] "Preview" refers to a temporary display function that allows the user to check the generated advertising copy.

[2076] "Fine-tuning" refers to an operation in which a user manually corrects the details of the generated advertising copy.

[2077] A "designated platform" refers to the medium, such as social media or email, selected to deliver the advertising copy.

[2078] "Sales performance data" refers to data such as sales figures and customer feedback obtained as a result of promotional activities.

[2079] "Voice input" refers to a means of inputting a user's speech as data.

[2080] "Eye tracking" is a technology that tracks the movement of a user's eyes to input data or perform operations.

[2081] "Voice instructions" refers to a method in which a user issues commands to a system by voice.

[2082] "Gestures" are a method of operation in which you give instructions to a system using hand or body movements.

[2083] The present invention is a system for proposing sales promotions that are specialized for a region, and generates advertising copy that is specialized for each region based on information about products and services sold by users, and further improves the effectiveness of advertising based on sales performance. A specific embodiment for this purpose is described below.

[2084] User Registration and Data Entry

[2085] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking and a voice input system. This makes it easy for users with visual impairments or who are unfamiliar with keyboard operation to create an account.

[2086] Entering product information and promotional data

[2087] Users input information about the product they want to sell (product name, category, price, etc.), target customer demographic, and sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. This information is received by the server and stored in a database.

[2088] Data preprocessing and analysis

[2089] The server preprocesses the received data, such as product information, target customer demographics, and budget, into a format suitable for the AI ​​model. It performs data cleaning and normalization to prepare the data in an optimal state for input into the model. This improves the accuracy of the AI ​​model and enables the generation of effective advertising copy.

[2090] Generating ad copy

[2091] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[2092] Examples of prompt sentences include "Input prompt for generative AI model: Tokyo, 20s-30s, latest trends, luxury sneakers" and "Input prompt for generative AI model: Osaka, 30s-50s, housewife, household appliances."

[2093] Preview and fine-tune your ad copy

[2094] The generated ad copy is saved in the user's account, and the user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures, allowing users to easily optimize their ad copy and achieve high advertising effectiveness.

[2095] Advertisement delivery and sales data collection

[2096] Finally, the advertising copy fine-tuned by the user is distributed by the server to the specified platform (such as social media or email). The user inputs sales performance data (sales figures, customer feedback, etc.) obtained as a result of the promotional activities and sends it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of the advertising copy are continuously improved.

[2097] As described above, this system generates region-specific advertising copy and is designed to realize effective sales promotion activities. It has a user-friendly interface and a feedback loop based on performance data, making it possible to optimize advertising effectiveness.

[2098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2099] Step 1:

[2100] Users create an account using a smartphone or head-mounted display (HMD), and input information such as username, email address, and password using eye tracking or voice input systems. The input information is sent by the device to a server, which stores it in a database.

[2101] Input: Username, Email Address, Password

[2102] Output: User information stored in the database

[2103] Step 2:

[2104] Users input information about the product they want to sell (product name, category, price, etc.), their target customer base, and their sales promotion budget using eye tracking or voice input. This reduces the burden on users and allows for intuitive input operations. The input information is sent by the device to the server, which then stores it in a database.

[2105] Input: Product name, category, price, target customer, promotion budget

[2106] Output: Product information stored in the database

[2107] Step 3:

[2108] The server preprocesses the received data, such as product information, target customer demographics, and promotional budgets, into a format suitable for the generative AI model. It performs processes such as data cleaning and normalization to prepare the data in an optimal state for input into the model. This preprocessing allows the model to analyze the data efficiently.

[2109] Input: Product information, target customer demographics, promotional budget

[2110] Output: Preprocessed data in a format that can be input into a generative AI model

[2111] Step 4:

[2112] The server inputs the preprocessed data into a generative AI model to generate region-specific advertising copy. The generative AI model creates advertising copy by taking into account consumer behavior and preference data for each region. For example, advertising copy such as "The latest trend! Get it now!" for young people in Tokyo and "The latest home appliances that make housework easier, on sale now!" for housewives in Osaka is generated.

[2113] Input: Preprocessed data

[2114] Output: Localized ad copy

[2115] Step 5:

[2116] The generated ad copy is saved in the user's account by the server. The user can preview it on their smartphone or HMD. The ad copy can be fine-tuned using voice commands or gestures. This allows users to easily optimize the ad copy and achieve high advertising effectiveness.

[2117] Input: Generated ad copy

[2118] Output: Tweaked ad copy

[2119] Step 6:

[2120] The server delivers the ad copy fine-tuned by the user to the specified platform (SNS, email, etc.), thereby effectively delivering locally-specific ads.

[2121] Input: Tweaked ad copy

[2122] Output: Delivery of advertising text to the specified platform

[2123] Step 7:

[2124] Users input sales performance data (sales figures, customer feedback, etc.) obtained as a result of their promotional activities and send it to the server. This data is added as learning data for the generative AI model, and the accuracy and effectiveness of advertising copy is continuously improved.

[2125] Input: Sales performance data (sales numbers, customer feedback)

[2126] Output: Sales performance data added as training data for the generative AI model

[2127] 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.

[2128] MODE FOR CARRYING OUT THE INVENTION

[2129] The present invention relates to a region-specific sales promotion proposal system, and in particular to a system that generates more effective and attractive advertising copy and improves sales performance by combining an emotion engine that recognizes user emotions.

[2130] System configuration

[2131] 1. User registration and data entry

[2132] User: Launch the application and create an account. During initial registration, enter information such as username, email address, and password.

[2133] Terminal: Sends the information entered by the user to the server.

[2134] Server: Stores the received user information in a database and completes account registration.

[2135] 2. Enter product information and promotional data

[2136] User: Enter information about the product you want to sell (product name, category, price, etc.), target customer demographic, and promotional budget.

[2137] Terminal: Sends data entered by the user to the server.

[2138] Server: Stores the received information in a database and prepares it for preprocessing.

[2139] 3. Data Preprocessing and Analysis

[2140] Server: Preprocesses data such as product information and target customer demographics into a format suitable for generative AI models, including data cleaning and numerical normalization.

[2141] 4. Generating advertising copy

[2142] Server: The preprocessed data is input into a generative AI model to generate region-specific ad copy. The generative AI model takes into account consumer behavior and preference data for each region to create effective ad copy.

[2143] Server: Stores the generated ad copy in a database.

[2144] 5. Preview and fine-tune your ad copy

[2145] On the device: The user logs in to their account and previews the generated ad copy. The emotion engine then analyzes the user's visual and audio information to interpret their emotions.

[2146] Emotion engine: Automatically suggests fine-tuning ad copy based on the user's emotional state. For example, if the user is excited, it will suggest more emphatic language, and conversely, if the user is calm, it will suggest more toned-down language.

[2147] Users: can see the preview and either adopt the suggested wording or manually adjust it.

[2148] 6. Delivery of advertisements

[2149] User: Check the ad copy to be delivered and select the platform (social media, email, etc.) to deliver it.

[2150] Device: Sends distribution settings to the server.

[2151] Server: Delivers ads to the specified platform. It is also possible to monitor the ad delivery status in real time.

[2152] 7. Collecting sales results and feedback

[2153] User: Enters the results of promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[2154] Terminal: Sends sales performance data to the server.

[2155] Server: Stores the received sales performance data in a database and adds it to the generative AI model as training data.

[2156] Emotion Engine: User feedback is analyzed for emotion and reflected in sales performance data, allowing the generative AI model to improve advertising copy by taking user emotions into account.

[2157] Specific examples

[2158] Example 1: If a user sells luxury sneakers in Tokyo, they set their target demographic to young people in their 20s and 30s, input their price range, and enter their promotional budget. The generative AI model uses this information to generate advertising copy such as "The latest trend! Get the luxury sneakers that are the talk of Tokyo!" The user reviews the ad on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests similarly emphasized copy. The user then adopts this and distributes the ad on social media.

[2159] Example 2: If a user sells home appliances in Osaka, they set their target demographic to housewives in their 30s-50s and input their price range and promotional budget. The generative AI model uses this information to generate advertising copy such as "Perfect for housewives in Osaka! The latest home appliances make housework easy!" If the emotion engine recognizes a "calm" state, the user can fine-tune the automatically suggested calm copy and distribute the ad through email marketing.

[2160] In this way, by combining an emotion engine that recognizes user emotions, the present invention enables more effective generation and fine-tuning of advertising copy, thereby realizing locally specialized sales promotion activities.

[2161] The processing flow will be explained below.

[2162] Step 1:

[2163] A user launches an application and creates a new account. The user enters their information (username, email address, password, etc.).

[2164] Step 2:

[2165] The terminal transmits the user's input information to the server.

[2166] Step 3:

[2167] The server verifies the received user information and stores it in the database, completing the user account registration.

[2168] Step 4:

[2169] The user logs in to their account and enters information about the product or service they are selling (product name, category, price, etc.), their target customer base, and their promotional budget.

[2170] Step 5:

[2171] The terminal transmits the user's input data to the server.

[2172] Step 6:

[2173] The server stores the received product information and promotional data in a database, after which it preprocesses the data into a format suitable for the generative AI model.

[2174] Step 7:

[2175] The server inputs the preprocessed data into a generative AI model to generate advertising copy that takes into account consumer behavior and preferences in each region.

[2176] Step 8:

[2177] The server stores the generated ad copy in a database and associates it with the user's account.

[2178] Step 9:

[2179] The device displays a preview of the generated ad copy to the user, while the emotion engine analyzes the user's visual and audio information to read their emotions.

[2180] Step 10:

[2181] The emotion engine automatically generates suggestions for fine-tuning ad copy based on the user's emotional state. For example, it suggests emphasizing words when the user is "excited" and calming words when the user is "calm."

[2182] Step 11:

[2183] The user reviews the preview and either adopts the suggested wording or adjusts it manually.

[2184] Step 12:

[2185] The user checks the ad copy to be delivered and selects the platform (SNS, email, etc.) on which to deliver it.

[2186] Step 13:

[2187] The device sends the distribution settings to the server.

[2188] Step 14:

[2189] The server delivers the ad copy to the specified platform according to the settings.

[2190] Step 15:

[2191] The user inputs the results of the promotional activities and sends them to the server as sales performance data, including sales figures, customer feedback, click rates, etc.

[2192] Step 16:

[2193] The terminal transmits the sales performance data to the server.

[2194] Step 17:

[2195] The sales performance data received by the server is stored in a database and added to the generative AI model as training data.

[2196] Step 18:

[2197] The emotion engine analyzes user feedback and reflects it in sales performance data, allowing for improvements to be made by taking user emotions into account when generating the next ad copy.

[2198] Example 2

[2199] 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."

[2200] Conventional sales promotion proposal systems have the problem of limited advertising effectiveness because they are unable to generate or fine-tune advertising copy that takes user emotions into account. Furthermore, it is difficult to automatically generate region-specific advertising copy, which increases the effort required for users to manually adjust it.

[2201] The specification processing by the specification 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 a user to input information on a product or service to be sold, a target customer demographic, and a sales promotion budget; means for a terminal to transmit the information input by the user to the server; means for the server to store the received information in a database and complete account registration; means for the server to preprocess the received information into a format suitable for the generative AI model based on the information received; means for the generative AI model to generate region-specific advertising copy based on the preprocessed data; means for the server to store the generated advertising copy in a database; means for a user to preview and fine-tune the generated advertising copy; means for an emotion engine to analyze the user's emotional state and suggest fine-tuning the advertising copy; means for the server to distribute the advertising copy fine-tuned by the user to a specified platform; means for a user to input the results of sales promotion activities and transmit sales performance data to the server; and means for the server to add the sales performance data to the generative AI model as learning data to improve the quality of the advertising copy. This allows for effective generation and fine-tuning of advertising copy taking into account user emotions, and also makes it possible to propose sales promotions that are specific to the region.

[2202] "User" refers to any person or company that intends to use the System to sell goods or services.

[2203] "Terminal" refers to a device (e.g., PC, smartphone, tablet) that a user uses to input information and communicate with a server.

[2204] "Server" refers to the computer system that processes the received information, generates advertising copy using the generative AI model, and stores and distributes the data.

[2205] "Database" refers to a system in which a server stores user information, product information, generated advertising copy, sales performance data, etc.

[2206] "Generative AI models" refer to artificial intelligence algorithms or systems that generate region-specific advertising copy based on pre-processed data.

[2207] "Preview" refers to the screen display or interface that allows a user to preview the generated ad copy and make any necessary adjustments.

[2208] "Fine-tuning" refers to the act of modifying the content or expression of generated advertising copy based on suggestions from the user or the system.

[2209] An "emotion engine" refers to technology or software that analyzes a user's visual information and voice to recognize their emotional state and reflect the results in adjusting advertising copy.

[2210] "Promotional activities" refers to marketing and promotional activities carried out to promote the sale of products and services.

[2211] "Sales performance data" refers to data such as sales figures obtained as a result of promotional activities, customer feedback, and click rates.

[2212] The present invention is a localized promotion suggestion system that detects user emotions and generates and fine-tunes advertising copy based on the emotions. The system is implemented using the following specific hardware and software:

[2213] System Components

[2214] 1. User: Launches the application, creates an account, and uses the system. The user enters information about the product or service to be sold, the target customer demographic, and the promotional budget.

[2215] 2. Terminal: Using a device such as a PC, smartphone, or tablet, the user's input information is sent to the server.

[2216] 3. Server: The server manages the entire system, including the database, generative AI model, emotion engine, etc., and processes information. Specifically, it operates with the following configuration:

[2217] Data preprocessing and storage

[2218] The server receives product information and target customer demographic data sent by users and stores it in a database (e.g., MySQL). It then preprocesses the data into a format suitable for generative AI models (e.g., GPT-4). Preprocessing includes data cleaning, missing value imputation, and numerical normalization.

[2219] Generating ad copy

[2220] The server uses the preprocessed data to create prompts, which are then fed into a generative AI model to generate localized ad copy. The generated ad copy is then stored in a database. For example, the following prompts are used:

[2221] Example prompt:

[2222] "Generate advertising copy to sell high-end sneakers to young people in their 20s and 30s in Tokyo."

[2223] Fine-tuning with the Emotion Engine

[2224] When a user previews the generated ad copy, an emotion engine (e.g., Affectiva SDK) analyzes the user's visual and audio information to recognize their emotional state. The emotion engine then suggests fine-tuning the ad copy based on the user's emotions.

[2225] Ad copy delivery

[2226] After the user has reviewed the ad copy and made any necessary adjustments, the server distributes the final ad copy to the specified distribution platform (e.g., social media, email marketing, etc.) The distribution status of the ad is monitored in real time.

[2227] Sales performance collection and feedback

[2228] Users input sales performance data (sales figures, customer feedback, click rates, etc.) as a result of their promotional activities and send it to the server. The server stores this data in a database and adds it to the generative AI model as training data. Furthermore, the emotion engine analyzes user feedback and reflects it in the sales performance data to improve the quality of advertising copy.

[2229] Specific examples

[2230] For example, if a user sells high-end sneakers in Tokyo, the following process would occur:

[2231] 1. The user sets the target customer demographic as young people in their 20s and 30s, and enters the price range and promotional budget.

[2232] 2. Based on this information, the server inputs a prompt into the generative AI model, generating advertising text such as, "The latest trend! Get your hands on the luxury sneakers that are the talk of Tokyo!"

[2233] 3. The user checks the ad copy on the preview screen, and if the emotion engine recognizes an "excited" state, it suggests more emphatic copy.

[2234] 4. Users adopt it and distribute ads on social media.

[2235] As described above, the present invention can generate and fine-tune advertising copy taking into account user emotions, and can effectively carry out regionally specific sales promotion activities.

[2236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2237] Step 1:

[2238] User: Launches the application and opens the page for creating a new account. The user enters initial registration information such as username, email address, and password, and presses the "Submit" button.

[2239] Input: Username, Email Address, Password

[2240] Output: Registration information sent

[2241] Step 2:

[2242] Terminal: Sends the registration information entered by the user to the server.

[2243] Input: Username, Email Address, Password

[2244] Output: Registration information arrives at the server

[2245] Step 3:

[2246] Server: Save the received user information in a database (e.g. MySQL) and register a new account.

[2247] Input: Registration information

[2248] Output: User information is saved in the database

[2249] What it does: Uses an SQL statement to insert user information into the database.

[2250] Step 4:

[2251] Server: Generates a notification that the user has successfully registered for an account.

[2252] Input: User information

[2253] Output: Registration completion notification

[2254] Specific behavior: Calls the notification generation routine to create a registration completion message.

[2255] Step 5:

[2256] Server: Sends notification data to the device.

[2257] Input: Registration completion notification

[2258] Output: Notification data arrives on the device

[2259] Specific operation: Uses the network sending API to send a registration completion notification to the device.

[2260] Step 6:

[2261] Terminal: Display a message to the user confirming registration.

[2262] Input: Notification data

[2263] Output: A message appears saying registration is complete.

[2264] Specific behavior: Calls a routine that displays a notification message in the user interface.

[2265] Step 7:

[2266] User: Log in to your account and open the product information entry page. Enter the product name, category, price, target customer, and promotional budget, then click the "Submit" button.

[2267] Input: Product name, category, price, target customer, promotion budget

[2268] Output: Product information is sent

[2269] Step 8:

[2270] Terminal: Sends the product information entered by the user to the server.

[2271] Input: Product name, category, price, target customer, promotion budget

[2272] Output: Product information arrives at the server

[2273] Step 9:

[2274] Server: Store the received product information in a database.

[2275] Input: Product information

[2276] Output: Product information saved in the database

[2277] Specific behavior: Uses SQL statements to insert product information into the database.

[2278] Step 10:

[2279] Server: Retrieves product information and target customer data from the database and performs preprocessing.

[2280] Input: Product information, target customer information

[2281] Output: Preprocessed data

[2282] Specific operations: Perform data cleaning, missing value imputation, numerical normalization, etc.

[2283] Step 11:

[2284] Server: Creates prompts using preprocessed data and inputs them into the generative AI model.

[2285] Input: Preprocessed data

[2286] Output: Ad copy

[2287] Specific operation: Prompt text is generated based on the preprocessed data and passed to a generative AI model to generate advertising copy.

[2288] Step 12:

[2289] Server: Stores the generated ad copy in a database.

[2290] Input: Ad copy

[2291] Output: Ad copy saved in database

[2292] What it does: Uses an SQL statement to insert ad copy into the database.

[2293] Step 13:

[2294] Users: Log in to your account and open the preview screen to view the generated ad text.

[2295] Input: User information

[2296] Output: Preview of ad copy

[2297] Step 14:

[2298] Device: The generated ad copy and preview screen are displayed to the user.

[2299] Input: Ad copy

[2300] Output: Preview screen

[2301] What it does: Calls a routine that displays ad copy and previews in the user interface.

[2302] Step 15:

[2303] Emotion engine: Analyzes the user's visual and audio information during the preview and recognizes the user's emotions.

[2304] Input: User's visual information, audio data

[2305] Output: Emotional state data

[2306] What it does: Analyzes emotions using image and audio analysis algorithms.

[2307] Step 16:

[2308] Emotion engine: Suggests ad copy tweaks based on the user's emotional state.

[2309] Input: Emotional state data, advertising text

[2310] Output: Tweak suggestions

[2311] What it does: Analyzes emotional state and generates suggested revisions to ad copy based on that.

[2312] Step 17:

[2313] User: Review the suggested ad copy and manually adjust it as needed.

[2314] Input: Tweak suggestions, ad copy

[2315] Output: Final adjusted ad copy

[2316] Step 18:

[2317] Device: Sends the adjustments to the server.

[2318] Input: Final ad copy

[2319] Output: The adjustments are sent to the server

[2320] Step 19:

[2321] Server: Saves the final adjusted ad copy in the database.

[2322] Input: Final ad copy

[2323] Output: Adjusted ad copy saved in database

[2324] What it does: Uses an SQL statement to insert the final ad copy into the database.

[2325] Step 20:

[2326] User: Select the ad copy you want to deliver and choose the delivery platform (SNS, email, etc.).

[2327] Input: Finalized ad copy, distribution platform

[2328] Output: Distribution setting data

[2329] Step 21:

[2330] Device: Sends distribution settings to the server.

[2331] Input: Distribution setting data

[2332] Output: Distribution settings are sent to the server

[2333] Step 22:

[2334] Server: Serves ads to the specified platform.

[2335] Input: Delivery setting data, finalized ad wording

[2336] Output: Ad is served

[2337] What it does: Uses network APIs to send ad copy to distribution platforms.

[2338] Step 23:

[2339] User: Enters sales performance data as a result of promotional activities and sends it to the server.

[2340] Input: Sales performance data (sales numbers, customer feedback, click rates, etc.)

[2341] Output: Sales performance data arrives at the server

[2342] Step 24:

[2343] Server: Stores the received sales data in a database.

[2344] Input: Sales performance data

[2345] Output: Sales performance data is saved in the database

[2346] Specific behavior: Uses SQL statements to insert sales performance data into the database.

[2347] Step 25:

[2348] Emotion engine: Analyzes user feedback emotionally and reflects it in sales performance data.

[2349] Input: Sales performance data, feedback data

[2350] Output: Sentiment analysis data

[2351] What it does: Analyzes emotions using image and audio analysis algorithms.

[2352] Step 26:

[2353] Server: Sales performance data along with the analysis results are added as learning data to the generated AI model to improve the quality of advertising wording.

[2354] Input: Sales performance data, sentiment analysis data

[2355] Output: An updated generative AI model

[2356] Specific operation: Use a learning algorithm to update the parameters of the generative AI model.

[2357] (Application example 2)

[2358] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2359] Conventional advertising systems lacked the ability to analyze user sentiment and fine-tune ad copy when generating region-specific ad copy. This resulted in ad copy that was less likely to resonate with target customers, resulting in insufficient effectiveness of sales promotion activities. Furthermore, when users fine-tuned the generated ad copy, the effectiveness of the suggested copy was often not optimized based on user sentiment, making fine-tuning the ad copy complicated and time-consuming. Furthermore, when sales performance data was added as training data for the AI ​​model, the results of sentiment analysis were not reflected, making it difficult to use the results in generating the next ad copy.

[2360] 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.

[2361] In this invention, the server includes: a means for a user to input information about the product or service they are selling, their target customer demographic, and their promotional budget; a means for the server to preprocess the received information into a format suitable for the generation AI model; a means for the generation AI model to generate region-specific advertising text based on the preprocessed data; a means for the server to save the generated advertising text by linking it to the user's account; a means for the user to preview and fine-tune the generated advertising text; a means for the user to fine-tune the generated advertising text using a sentiment analysis engine that analyzes user sentiment; a means for the server to distribute the user-tuned advertising text to a specified platform; a means for the user to input the results of their promotional activities and send sales performance data to the server; and a means for the server to add the sales performance data to the generation AI model as training data. This allows the results of the user's sentiment analysis to be reflected in the generation of region-specific advertising text, enabling the generation and fine-tuning of more effective advertising text that appeals to the target customer demographic. Furthermore, by reflecting the results of the sentiment analysis in the sales performance data, the accuracy of the next advertising text generation can be improved.

[2362] "User" refers to an individual or corporation that uses a system or application.

[2363] "Product or service information" refers to information including data such as the name, category, and price of the product being sold.

[2364] "Target customer demographic" refers to the demographic of customers who are likely to purchase a particular product or service.

[2365] "Promotional budget" refers to the total amount of funds allocated for carrying out promotional activities.

[2366] "Server" refers to a central location that stores, processes, and distributes data.

[2367] A "generative AI model" refers to an artificial intelligence algorithm that generates advertising copy and other information based on input data.

[2368] "Preprocessing" refers to the process of converting data into a format suitable for a generative AI model.

[2369] "Region-specific advertising language" refers to advertising content that is suited to the preferences and behavior of consumers in a specific region.

[2370] An "account" refers to authentication information used to identify a user and associate individual data and settings with the user.

[2371] "Preview" refers to a display that allows you to check the generated advertising copy before it is actually delivered.

[2372] "Tweaking" refers to making small changes to optimize ad wording or settings.

[2373] An "emotion analysis engine" refers to a device or software that has the function of analyzing emotions from data such as a user's facial expressions and voice.

[2374] "Platform" refers to a medium, such as social media or email marketing, used to deliver advertisements.

[2375] "Sales performance data" refers to data including sales and customer feedback obtained as a result of actual sales promotion activities.

[2376] "Training data" refers to the dataset that a generative AI model uses to improve its performance.

[2377] The present invention is a system for generating region-specific advertising copy and fine-tuning it by analyzing user emotions. Specific embodiments of the system are described below.

[2378] 1. Overview of the entire system

[2379] The system consists of a user's device, a server, a generative AI model, a sentiment analysis engine, and an ad distribution platform. This system can generate and fine-tune effective advertising copy based on data entered by the user, such as product information and target customer demographics.

[2380] 2. System Configuration

[2381] Terminal

[2382] It provides a means for users to input product and service information, target customer demographics, and sales promotion budgets. This information is sent to the server, which will be described later.

[2383] server

[2384] The server uses the following software and hardware:

[2385] Database: Stores user and product information.

[2386] Generative AI model: An artificial intelligence algorithm for generating localized ad copy.

[2387] Sentiment analysis engine: Analyzes user sentiment and fine-tunes ad copy.

[2388] 3. Data Flow and Processing

[2389] User Data Entry

[2390] Users input detailed information about the products or services they want to sell, their target customer demographic, and their sales budget through the terminal. For example, if a user wants to sell high-end sneakers to people in their 20s and 30s, they enter this information into the terminal and send it to the server.

[2391] Pretreatment

[2392] The server preprocesses the received information, converting it into a format that can be properly processed by the generative AI model. Data cleaning and numerical normalization are performed at this stage.

[2393] Generating ad copy ...

Claims

1. A means for a user to input information about the product or service to be sold, a target customer demographic, and a promotional budget; A means for preprocessing the received information into a format suitable for the generative AI model; A means for the generative AI model to generate localized ad copy based on preprocessed data; and A means for the server to store the generated advertising copy by linking it to the user's account; a means for a user to preview and fine-tune the generated ad copy; a means for the server to deliver user-tuned advertising copy to a designated platform; A means for a user to input the results of sales promotion activities and transmit sales performance data to a server; The server generates sales performance data and provides a means to add it to the AI ​​model as learning data. Including system.

2. 2. The system according to claim 1, further comprising means for storing the user information and product information received by the server in a database.

3. The system of claim 1 , further comprising means for the generative AI model to generate advertising copy taking into account regional consumer behavior and preference data.

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