system

Abstract

Provide a system. A means for inputting advertising text; A means for inputting attribute information of the target demographic; A means of analysis based on the attribute information of the target demographic, a means for generating representative feedback comments from a target demographic; A means for correcting the advertisement text based on the analysis result of the generated feedback comments; A means to finalize the ad text; A system including:

Description

[Technical Field]

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

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

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

[0004] When placing an advertisement, if the attributes of the advertiser and the target audience differ significantly, the target audience may feel uncomfortable or uneasy about the advertisement, resulting in a decrease in the effectiveness of the advertisement. To solve this problem, a method is needed to generate text that is highly compatible with the target audience and correct the advertisement text. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system that includes a means for inputting advertising text, a means for inputting attribute information of a target demographic, a means for analyzing the target demographic attribute information, a means for generating representative feedback comments from the target demographic, a means for correcting the advertising text based on the analysis of the generated feedback comments, and a means for finalizing the advertising text. This makes it possible to generate advertising text that suits the sensibilities and needs of the target demographic, thereby improving advertising effectiveness by providing advertisements that are less awkward to the eye.

[0006] "Advertising text" refers to the text used to present advertising information.

[0007] A "target demographic" refers to a group of people with specific attributes to whom a particular advertisement is primarily directed.

[0008] "Attribute information" refers to data about the characteristics of the target audience, such as age, gender, preferences, and interests.

[0009] "Analysis" refers to the act of understanding and identifying the characteristics and features of the target audience based on input information.

[0010] "Feedback comments" refers to the text of impressions and opinions that the target audience may have about the advertisement.

[0011] A "natural language generation algorithm" refers to a computer method for automatically generating text in natural language based on input data.

[0012] "Correction" refers to the act of making changes to the original advertising text to make it more effective based on the analysis results of the generated feedback comments.

[0013] "Confirmation" refers to the act of determining the optimal final ad text. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0036] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0037] The device then receives this input and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0038] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0039] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0040] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0041] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0042] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0043] The system of the present invention makes it possible to reduce the sense of discomfort caused by differences in attributes between advertisers and their target demographics, thereby improving advertising effectiveness.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0047] Step 2:

[0048] The device receives the input information and sends it to the server, which uses a communication protocol to transfer the ad text and demographic information to the server.

[0049] Step 3:

[0050] The server analyzes the target demographic attribute information it receives, referencing past advertising data and demographic information to identify common characteristics and interests of women in their 20s.

[0051] Step 4:

[0052] The server uses a natural language generation algorithm to generate typical feedback comments from the target demographic, such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0053] Step 5:

[0054] The server analyzes the generated feedback comments. It reviews all comments and extracts common themes and key needs, such as "protects skin" or "want to use it every day."

[0055] Step 6:

[0056] The server analyzes the feedback comments and generates instructions to correct the ad text, such as "This foundation has a natural finish and is gentle enough for everyday use!"

[0057] Step 7:

[0058] The terminal receives the correction proposal and notifies the user, who then checks the notification and reviews the advertisement text based on the correction proposal.

[0059] Step 8:

[0060] The user reviews the correction suggestions and finalizes the ad text. The user can change the ad text to "This foundation has a natural finish and is gentle enough for everyday use!"

[0061] Step 9:

[0062] The device sends the final ad text to the server, where it is stored in a database and used for subsequent ad serving.

[0063] Through this series of steps, advertising text that is highly relevant to the target audience can be generated, improving advertising effectiveness.

[0064] Example 1

[0065] 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."

[0066] If advertising text is not effectively communicated to the target audience, the effectiveness of the advertisement will decrease, resulting in problems such as product or service awareness and sales not increasing as expected. Furthermore, creating advertisements that reflect the needs and feedback of the target audience requires a great deal of effort and time. As a result, there is a challenge in efficiently and quickly creating advertising text that is optimized for the target audience.

[0067] 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.

[0068] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for notifying of suggested corrections, and means for saving the advertising text in a database, thereby enabling automatic generation and correction of advertising text optimized for the target demographic.

[0069] "Advertising text" is text that explains the features and benefits of the products or services that an advertiser offers to its target audience.

[0070] The "target demographic" is a group of people with specific attribute information, and is the customer demographic that is expected to receive the advertising text.

[0071] "Attribute information" is data that indicates the characteristics of the target demographic, such as age, gender, hobbies, interests, and purchasing history.

[0072] "Feedback comments" are sentences generated to represent the impressions and opinions of the target audience regarding the advertising text.

[0073] A "natural language generation algorithm" is a computational method for generating human language using artificial intelligence techniques.

[0074] "Analysis" is the process of extracting the characteristics and interests of the target audience based on data.

[0075] "Adjustment" refers to optimizing the ad text based on the generated feedback comments and modifying it to make it more appropriate for the target audience.

[0076] "Notification" is the act of informing the user of the generated amendment proposal.

[0077] A "database" is a system for efficiently storing, managing, and retrieving structured data.

[0078] A "terminal" is a computer or mobile device used by a user to input information and receive feedback from a server.

[0079] A "server" is a computer system for receiving, processing, and storing data sent from a terminal.

[0080] The system of the present invention is designed to optimize advertising text for a target demographic and improve advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0081] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s."

[0082] The terminal then receives this input information and transmits it over the network to the server, where the terminal acts as a bridge between the user and the server.

[0083] The server analyzes the characteristics and interests of the target audience based on the received attribute information. For this analysis, the server uses past advertising data, demographic information, and other related databases. For example, information related to fashion, cosmetics, lifestyle, etc. for women in their 20s is utilized at this stage. Specific databases used here include Google® BigQuery and Amazon RDS.

[0084] Based on the analysis results, the server uses a natural language generation algorithm (e.g., OpenAI (registered trademark) GPT-3 (registered trademark)) to generate representative feedback comments from the target demographic. The generated feedback comments include specific examples such as "I want to use products that are gentle on my skin" and "I want a foundation that I can use every day." Examples of prompt sentences used for this purpose are as follows:

[0085] Example prompt sentence:

[0086] "Generate feedback comments about the foundations that women in their 20s want."

[0087] The server reviews the generated feedback comments and extracts commonalities and key feedback points. For example, if there is a lot of feedback such as "protects skin" and "want to use it every day," these common needs are extracted as analysis results. A clustering method (e.g., K-means clustering) can be used for this analysis.

[0088] The server then generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggestion such as, "This foundation has a natural finish and is gentle on the skin even when used daily!" The suggested revisions are then notified to the user via their device.

[0089] The user reviews the proposed corrections and finalizes the ad text. For example, the final ad text might read, "This foundation has a natural finish and is gentle enough for daily use!"

[0090] Finally, the device sends the final ad text to the server, which stores this information in a database, allowing the system to easily reference the final text later.

[0091] This series of processes enables the automatic and efficient generation of advertising text that is highly relevant to the target demographic, enabling advertisers to quickly provide effective advertisements that reflect the needs of their target demographic.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] System program processing flow

[0094] Step 1: Enter your ad text and target demographics

[0095] As an advertiser, the user inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s." This sends the user's input to the system.

[0096] Input: Ad text, target demographic attributes

[0097] Output: Input data sent to the terminal

[0098] Step 2: Submitting the entered data

[0099] The terminal receives user input data in real time and sends it to the server via the network as a POST request, where the data is packaged in JSON format.

[0100] Input: Ad text received from the user and target demographic attributes

[0101] Output: JSON data sent to the server

[0102] Step 3: Analyze your target audience

[0103] Based on the target demographic information received by the server, a specific analytical algorithm is used to analyze the characteristics and interests of the target demographic, using appropriate past advertising data and demographic information from a database.

[0104] Input: Target demographic attributes, past advertising data, demographic information

[0105] Output: Analysis of the characteristics and interests of the target audience

[0106] Step 4: Generate feedback comments

[0107] Based on the analysis results, the server uses a natural language generation algorithm (e.g., a generative AI model) to generate representative feedback comments from the target demographic.

[0108] Input: Analysis results, prompt text (e.g., "Please generate feedback comments about the foundation that women in their 20s want.")

[0109] Output: Generated feedback comments

[0110] Step 5: Review feedback comments

[0111] The server reviews the generated feedback comments and extracts commonalities and key feedback points using clustering techniques (e.g., K-means clustering).

[0112] Input: Generated feedback comments

[0113] Output: Analysis results including commonalities and key points

[0114] Step 6: Generate correction instructions for the ad text

[0115] The server generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggested revision: "This foundation has a natural finish and is gentle enough for daily use!"

[0116] Input: Review results, ad text

[0117] Output: Ad text revision suggestions

[0118] Step 7: Notification of proposed amendments

[0119] The server notifies the user of the generated correction proposal via the terminal, which receives the data from the server and displays it on the user interface (UI).

[0120] Input: Ad text revision suggestions

[0121] Output: Correction proposals notified to the user

[0122] Step 8: Final review and confirmation

[0123] The user reviews the proposed corrections and confirms the final ad text on the system UI. When the user clicks the confirm button, the device resends the information to the server.

[0124] Input: Proposed amendment, final user review

[0125] Output: Finalized ad text

[0126] Step 9: Save your final ad text

[0127] The device sends the finalized ad text to the server, which stores this information in a database so the system can reference the final text later.

[0128] Input: Confirmed ad text

[0129] Output: Ad text stored on the server

[0130] The above is the specific flow of each processing step of the system.

[0131] (Application example 1)

[0132] 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."

[0133] Conventional methods for creating ad texts have had the problem of making it difficult to efficiently reflect the needs and feedback of the target audience, resulting in reduced advertising effectiveness. Furthermore, the ad creation process was cumbersome, as advertisers needed a high level of expertise and time to accurately grasp the characteristics of their target audience and generate and edit appropriate ad text.

[0134] 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.

[0135] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of a target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for inputting the advertising text and attribute information of the target demographic via a smart device, and means for saving the generated advertising text, thereby enabling advertisers to quickly and easily generate and optimize advertising text that is more effective for the target demographic.

[0136] - "Advertising text" means any text or message used to promote a product or service.

[0137] "Target demographic information" is data that describes the characteristics, such as age, gender, and interests, of a specific group of people who will receive an advertisement.

[0138] "Representative feedback comments from the target audience" are sentences that show the target audience's typical reactions and opinions to a particular advertisement.

[0139] "Analysis of feedback comments" refers to the process of analyzing the content of the generated comments and extracting commonalities and key points.

[0140] "Correcting the ad text" means modifying the content of the ad text based on feedback comments to make it more relevant to the target audience.

[0141] "Determining the final advertising text" refers to the process of finally determining the content of the corrected advertising text.

[0142] "Smart devices" are devices such as mobile phones and eyeglass-type information terminals connected to the Internet that advertisers use to input, confirm, and correct data.

[0143] A "natural language generation algorithm" is a computational method or model for generating language that humans naturally use.

[0144] A "database" is a system that stores large amounts of data in an organized manner and allows quick access to specific information.

[0145] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0146] First, a user acts as an advertiser and uses a smart device (smartphone or smart glasses) to input the ad text and target demographic information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic.

[0147] The device then receives this input information and sends it to a server. The server then analyzes the characteristics and interests of the target audience based on their attribute information. This analysis utilizes past advertising data, demographic information, and other related databases. The analysis is performed using a natural language generation algorithm (such as GPT-3).

[0148] Based on the analysis results, the server uses a generative AI model to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can use every day."

[0149] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0150] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0151] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0152] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0153] Computer systems and their operation

[0154] The system consists of several important hardware and software elements.

[0155] Hardware used:

[0156] Smartphone

[0157] Smart Glasses

[0158] Software used:

[0159] Server side: Flask or Django

[0160] Generative AI model: GPT-3

[0161] Data transmission: Requests

[0162] Examples of specific examples and prompts

[0163] As a specific example, an advertiser uses a smart device to enter the following information into the app:

[0164] Ad text: "New foundation makes your skin glow!"

[0165] Target demographic: Women in their 20s

[0166] The server receives the data, analyzes it, and uses an AI model to generate feedback comments and provide optimized ad text, such as:

[0167] Optimized ad text: "A foundation that gives a natural finish and is gentle enough for everyday use!"

[0168] An example of an input prompt for the generative AI model is as follows:

[0169] prompt:

[0170] Target demographics:

[0171] Age: 20s

[0172] Gender: Female

[0173] Ad text:

[0174] "Your new foundation will make your skin glow!"

[0175] Generate feedback comments for this target audience.

[0176] The generative AI model generates feedback comments like the following:

[0177] "I want a foundation that I can use every day."

[0178] It's important to use ingredients that are gentle on the skin.

[0179] Emphasis on a natural finish

[0180] Based on this feedback, the server generates optimized ad text and provides it to the user.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] The user uses a smart device to input the ad text and demographic information for the target demographic. The input data is "Our new foundation will make your skin glow!" (ad text) and "Women in their 20s" (target demographic). This data is saved on the device and then sent to the server.

[0184] Step 2:

[0185] The device sends the received ad text and target demographic attribute information to the server. The input data is sent in JSON format, and the server receives it and prepares it for analysis.

[0186] Step 3:

[0187] The server performs analysis based on the target demographic's attribute information. Specifically, it derives the target demographic's characteristics and interests using related databases such as past advertising data and demographic information. The input is the target demographic's attribute information, and the output is the analysis results.

[0188] Step 4:

[0189] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate representative feedback comments from the target demographic. The input is the analysis results, and the output is the generated feedback comments. This feedback might be something like, "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0190] Step 5:

[0191] The server reviews the feedback comments and extracts commonalities and key feedback points. Here, the generated feedback comments are analyzed to identify important keywords and common needs. The input is the generated feedback comments, and the output is the extracted feedback points.

[0192] Step 6:

[0193] The server generates instructions to correct the ad text based on the extracted feedback points. Specific correction suggestions include, "This foundation has a natural finish and is gentle enough for daily use!" The input is the extracted feedback points, and the output is the correction instructions.

[0194] Step 7:

[0195] The server sends the correction instructions to the terminal and notifies the user. The user reviews the correction proposal and modifies the final advertising text as necessary. The input is the correction instructions, and the output is the final advertising text reviewed by the user.

[0196] Step 8:

[0197] The user finalizes the final ad text, and the device sends the final text to the server. The final ad text might be something like, "This foundation has a natural finish and is gentle enough for everyday use!"

[0198] Step 9:

[0199] The server stores the finalized ad text in a database. The input is the finalized ad text, and the output is the stored ad text. This ensures that the most appropriate ad is delivered to the target audience.

[0200] 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.

[0201] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. In particular, the present invention combines an emotion engine that recognizes user emotions, enabling more accurate generation and correction of advertising text. Specific system embodiments are described below.

[0202] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0203] The device then receives this input information and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0204] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0205] The server generates instructions to revise the ad text based on the generated feedback comments. The emotion engine analyzes the emotional tone of the comments and reflects the results in the revision. For example, the server generates a revision suggestion such as "This foundation has a natural finish and is gentle on the skin even when used daily," and notifies the user via their device.

[0206] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text could be, for example, "This foundation has a natural finish and is gentle enough for daily use."

[0207] The final ad text, once confirmed by the device, is sent to the server and stored in a database. This series of processes makes it possible to generate ads that are highly compatible with the target demographic and do not feel out of place. The introduction of an emotion engine also makes it possible to more precisely express emotions in the ad text, resulting in more effective ad delivery.

[0208] In this way, the system of the present invention aims to reduce the sense of discomfort caused by differences in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0212] Step 2:

[0213] The device receives the entered information and sends it to the server. Specifically, it transfers the advertising text and target demographic information entered in the input form to the server.

[0214] Step 3:

[0215] The server performs an analysis based on the target demographic attribute information received. It references past advertising data and demographic information to identify the characteristics and general interests of the target demographic. This analysis provides a detailed understanding of the target demographic profile.

[0216] Step 4:

[0217] The server uses a natural language generation algorithm to generate representative feedback comments based on the target demographic, while simultaneously utilizing an emotion engine to analyze the emotional tone of the generated comments. For example, for a comment such as "I want to use skin-friendly products," the server identifies emotional tones such as "reassurance" and "trust."

[0218] Step 5:

[0219] The server reviews the feedback comments analyzed by the sentiment engine and extracts common themes and key needs. For example, if many comments contain feedback such as "protects skin" or "can be used daily," this will be recognized as a key need.

[0220] Step 6:

[0221] The server generates instructions to correct the advertising text based on the analysis of the feedback comments and their emotional tone. Specifically, it creates a suggested correction, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0222] Step 7:

[0223] The device displays the proposed corrections to the user, who then reviews them. The analysis results of the emotion engine are also displayed, allowing the user to understand the emotions of the target audience before reviewing the text.

[0224] Step 8:

[0225] The user reviews the proposed revisions and finalizes the ad text. Specifically, the user confirms and confirms the final ad text: "This foundation has a natural finish and is gentle on the skin, even when used daily!"

[0226] Step 9:

[0227] The device sends the final ad text to the server, which stores it in a database for use in subsequent ad campaigns.

[0228] Through this series of steps, the system of the present invention generates advertising text that matches the emotions and needs of the target audience, improving advertising effectiveness. By introducing an emotion engine, it is possible to capture the emotional tone of the target audience and create advertisements that are more relatable.

[0229] Example 2

[0230] 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."

[0231] While conventional ad text generation systems perform analysis based on the target demographic's attribute information, they do not perform precise adjustments based on emotions, making it difficult to generate ad text that is optimized for the target demographic and has a high affinity with the target demographic. Furthermore, after the final ad text is finalized, it is not properly saved or managed, making subsequent analysis and revision difficult.

[0232] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting advertising text, a means for inputting attribute information of the target demographic, a means for analyzing based on the attribute information of the target demographic, a means for generating representative feedback comments from the target demographic, a means for correcting the advertising text based on the analysis results of the generated feedback comments, a means for finalizing the advertising text, a means for analyzing the emotional tone of the generated feedback comments and correcting the advertising text including the analyzed emotional tone, and a means for saving the finalized advertising text. This makes it possible to generate sophisticated advertising text that takes into consideration the emotions of the target demographic and to appropriately save and manage it.

[0233] "Advertising text" is a sentence that expresses the message that the advertiser wants to convey to the target audience.

[0234] "Target demographic" refers to people with specific attributes (age, gender, interests, etc.) that are targeted by the ad text.

[0235] "Attribute information" is detailed data about the specific attributes of the target demographic (age, gender, interests, etc.).

[0236] "Feedback comments" are sentences that express opinions and impressions received from the target demographic.

[0237] "Natural language generation algorithms" refer to computational techniques and methods for generating natural language that humans can understand.

[0238] "Emotional tone" is information that expresses emotions (such as joy, sadness, or anger) detected from text.

[0239] "Analysis" is the process of examining input data in detail and extracting hidden patterns and information.

[0240] "Correction" means modifying the entered advertising text to make it optimal for the target audience.

[0241] "Final" means that the final ad text has been decided and will not be revised further.

[0242] "Storage" means keeping the confirmed advertising text in a database or the like for subsequent use or analysis.

[0243] The system of the present invention aims to generate advanced advertising text and improve its effectiveness, and by combining it with an emotion engine in particular, it is possible to generate advertising text that has a higher affinity with the target demographic.

[0244] First, the user acts as an advertiser and inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device and sent to the server.

[0245] Next, the server performs an analysis based on the target demographic attribute information received. This analysis utilizes past advertising data, demographic information, and other related databases. As a specific example, the analysis targets past advertising data related to "beauty and health" for the target demographic of "women in their 20s."

[0246] Based on the analysis results, the server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. The emotion engine is also used to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0247] The server generates instructions to amend the ad text based on the generated feedback comments. At this time, the emotional tone detected by the emotion engine is also reflected in the amendments. For example, a suggested amendment might be, "This foundation has a natural finish and is gentle on the skin even when used daily." The proposed amendments are notified to the user via their device.

[0248] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text might be, for example, "This foundation has a natural finish and is gentle on the skin even when used daily." The final ad text is sent from the device to the server and stored in a database.

[0249] This series of processes makes it possible to generate advertisements that are highly relevant to the target audience and do not feel out of place. In addition, the introduction of an emotion engine allows for more refined emotional expression in the ad text, making it possible to deliver effective advertisements.

[0250] A specific example of a prompt is as follows:

[0251] User: Enter the ad text "New foundation will make your skin glow!" and the target demographic "Women in their 20s."

[0252] Server: Now that the ad text and demographic information has been received, analysis begins.

[0253] Server: Using the emotion engine, the following feedback comment was generated:

[0254] "This foundation is amazing!" (Emotional tone: joy, satisfaction)

[0255] Server: We suggest you revise your ad text as follows:

[0256] "This foundation has a natural finish and is gentle on the skin even when used daily."

[0257] Device: Suggested fix sent to user.

[0258] User: Review the proposed changes and confirm the final ad text: "This foundation has a natural finish and is gentle enough for everyday use."

[0259] Device: The final ad text was sent to the server and stored in the database.

[0260] As described above, the system of the present invention aims to reduce the sense of incongruity caused by the difference in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] The user inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device. The input data is the ad text and the demographic information of the target demographic.

[0264] Step 2:

[0265] The device sends the received input information to the server. The data sent includes the ad text and demographic information of the target audience.

[0266] Step 3:

[0267] The server analyzes the received information. For the analysis, it uses past advertising data, demographic information, and other related databases. For example, it uses past advertising data aimed at women in their 20s to analyze the characteristics of the target demographic. The input data is the received advertising text and attribute information of the target demographic, and the output data is the analysis results of the target demographic's characteristics and interests.

[0268] Step 4:

[0269] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, it generates a feedback comment such as "This foundation is truly amazing!" and detects "joy" or "satisfaction" as its emotional tone. The input data is the analysis result, and the output data is the feedback comment and its emotional tone.

[0270] Step 5:

[0271] The server generates instructions for correcting the advertising text based on the generated feedback comments. The corrections also include the emotional tone detected by the emotion engine. For example, a correction suggestion may be generated such as "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the feedback comments and their emotional tone, and the output data are correction suggestions for the advertising text.

[0272] Step 6:

[0273] The terminal notifies the user of the proposed corrections. The notified data is the proposed corrections to the advertisement text.

[0274] Step 7:

[0275] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. For example, the final ad text may be "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the proposed revisions to the ad text, and the output data is the final ad text.

[0276] Step 8:

[0277] The device sends the final ad text to the server, which stores it in a database. The data stored is the final ad text. This process generates a refined ad text that is stored in a format that is useful for future use and analysis.

[0278] Through the above processing steps, it becomes possible to generate and manage effective advertising texts that are optimized for the target demographic.

[0279] (Application example 2)

[0280] 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."

[0281] With conventional advertising systems, it was difficult to generate effective ad text for the target demographic, making it difficult to improve advertising effectiveness.In addition, because it was not possible to adjust ad text to reflect user emotions and real-time reactions, ads that were perceived as unnatural by the target demographic were sometimes delivered.

[0282] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for determining the final advertising text, means for collecting user data, emotion analysis means for analyzing the user's facial expressions and tone of voice, means for optimizing the advertising text in real time based on the emotion analysis results, and means for displaying the corrected advertising text. This makes it possible to generate and display highly accurate advertising text that reflects the user's emotions.

[0283] The "means for inputting advertisement text" is a mechanism that provides an interface for the advertiser to input the content of the advertisement.

[0284] The "means for inputting attribute information of the target demographic" is a mechanism that provides an interface for inputting information about the specific user demographic targeted by the advertisement.

[0285] "Means for analyzing based on the attribute information of the target demographic" refers to a mechanism for analyzing the characteristics and interests of that demographic based on the target demographic's attribute information.

[0286] The "means for generating representative feedback comments from the target demographic" is a mechanism for automatically generating anticipated reactions from the target user demographic.

[0287] The "means for correcting the advertisement text based on the analysis results of the generated feedback comments" is a mechanism for correcting the advertisement content based on the analysis results of the generated feedback comments.

[0288] The "means for determining the final advertisement text" is a mechanism for finally determining the corrected advertisement text.

[0289] "Means for collecting user data" refers to a mechanism for collecting data from users (facial expressions, voice, etc.).

[0290] The "emotion analysis means for analyzing the user's facial expressions and voice tones" is a mechanism for analyzing emotions from the collected user's facial expressions and voice.

[0291] "Means for optimizing advertising text in real time based on the results of sentiment analysis" refers to a mechanism for optimizing advertising text on the spot based on the analyzed sentiment data.

[0292] The "means for displaying the corrected advertising text" is a device or interface for displaying the optimized advertising text to the user.

[0293] The present invention provides a system for displaying advertising text optimized for a specific demographic. This system optimizes text using a sentiment analysis engine and a natural language generation algorithm based on advertising text entered by an advertiser and attribute information of the target demographic.

[0294] 1. System Configuration

[0295] The system includes the following major components:

[0296] 1. How to enter ad text and attribute information:

[0297] Advertisers input their advertising text and target demographic information via their terminals, and this data is sent to the server.

[0298] 2. Analysis method:

[0299] The server then analyzes the target demographic information it receives, using past advertising data and demographic information.

[0300] 3. Feedback Comment Generation Method:

[0301] It uses natural language generation algorithms to generate representative feedback comments from your target audience, while a sentiment analysis engine analyzes the emotional tone of the comments.

[0302] 4. Ad text correction methods:

[0303] Based on the analysis of the generated feedback comments, the ad text is revised. The emotional tone of the feedback comments is reflected by the emotion engine.

[0304] 5. Final ad text confirmation method:

[0305] The advertiser reviews and confirms the revised ad text, which is then saved on the server.

[0306] 6. User Data Collection Methods:

[0307] Smart glasses and other devices are used to collect data such as a user's facial expressions and tone of voice.

[0308] 7. Emotion analysis means:

[0309] The collected user data is analyzed to understand the user's emotional tone.

[0310] 8. Text optimization methods:

[0311] Ad text is optimized in real time based on sentiment analysis results.

[0312] 9. Advertising display means:

[0313] The optimized ad text is displayed to the user through a device such as smart glasses.

[0314] 2. Program Processing Overview

[0315] The server first receives the advertisement text and target demographic attribute information entered by the advertiser. Then, it uses an analysis means to analyze the characteristics and interests of the target demographic and generates feedback comments. The emotional tone of the feedback comments is analyzed by an emotion analysis engine, and the advertisement text is corrected based on the results.

[0316] In real time, emotion analysis is performed from the user's facial expressions and voice tones collected using a user data collection means, and the advertisement text is optimized based on the emotion analysis results. The optimized advertisement text is then displayed to the user through a device such as smart glasses.

[0317] 3. Specific Examples

[0318] For example, use the following prompt:

[0319] Prompt Sentence Examples

[0320] User Data: "Expression: Smiling, Tone of Voice: Excited"

[0321] Ad text: "Try out your new smartwatch!"

[0322] Target demographic: "Men in their 30s"

[0323] In this example, if the user is smiling and has an excited voice, the ad text will be optimized to match the user's emotions, for example, "This smartwatch will make your daily life more fun!"

[0324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0325] Step 1:

[0326] The terminal receives advertising text and target demographic attribute information from the advertiser and sends the data to the server. The terminal receives advertising text and target demographic attribute information as input and processes the data before sending it to the server.

[0327] Step 2:

[0328] The server analyzes the target demographic attribute information it receives. Using the target demographic attribute information, past advertising data, and demographic information as input, it performs data calculations to extract the characteristics and interests of the target demographic.

[0329] Step 3:

[0330] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic based on the analysis results, and processes the data to generate the feedback comments using the analysis results as input.

[0331] Step 4:

[0332] The server analyzes the emotional tone of the generated feedback comments using an emotion engine, and performs a data operation using the feedback comments as input to analyze the emotional tone.

[0333] Step 5:

[0334] The server corrects the advertisement text based on the sentiment analysis result, using the emotional tone of the feedback comments as input and performing correction operations on the advertisement text.

[0335] Step 6:

[0336] The terminal notifies the advertiser of the proposed amendments, and the advertiser finalizes the ad text. The proposed amendments are used as input to perform data processing to finalize the ad text.

[0337] Step 7:

[0338] The server stores the final ad text in the database. Using the final ad text as input, it processes the data to store in the database.

[0339] Step 8:

[0340] Data on facial expressions and voice tones are collected by the user using smart glasses. Data calculations are performed using the collected user facial expressions and voice data as input.

[0341] Step 9:

[0342] The server analyzes the collected user data to identify the user's emotional tone. Using the user's facial expression and voice data as input, it performs data calculations to analyze the emotional tone.

[0343] Step 10:

[0344] The server optimizes the ad text in real time based on the sentiment analysis results. Using the sentiment analysis results as input, it processes the data to optimize the ad text.

[0345] Step 11:

[0346] The optimized advertisement text is displayed to the user through the smart glasses. Using the optimized advertisement text as input, a data calculation is performed to display the data to the user.

[0347] 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.

[0348] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0349] 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.

[0350] [Second embodiment]

[0351] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0352] 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.

[0353] 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).

[0354] 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.

[0355] 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.

[0356] 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).

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] In the smart glasses 214, 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.

[0362] 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."

[0363] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0364] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0365] The device then receives this input and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0366] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0367] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0368] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0369] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0370] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0371] The system of the present invention makes it possible to reduce the sense of discomfort caused by differences in attributes between advertisers and their target demographics, thereby improving advertising effectiveness.

[0372] The processing flow will be explained below.

[0373] Step 1:

[0374] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0375] Step 2:

[0376] The device receives the input information and sends it to the server, which uses a communication protocol to transfer the ad text and demographic information to the server.

[0377] Step 3:

[0378] The server analyzes the target demographic attribute information it receives, referencing past advertising data and demographic information to identify common characteristics and interests of women in their 20s.

[0379] Step 4:

[0380] The server uses a natural language generation algorithm to generate typical feedback comments from the target demographic, such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0381] Step 5:

[0382] The server analyzes the generated feedback comments. It reviews all comments and extracts common themes and key needs, such as "protects skin" or "want to use it every day."

[0383] Step 6:

[0384] The server analyzes the feedback comments and generates instructions to correct the ad text, such as "This foundation has a natural finish and is gentle enough for everyday use!"

[0385] Step 7:

[0386] The terminal receives the correction proposal and notifies the user, who then checks the notification and reviews the advertisement text based on the correction proposal.

[0387] Step 8:

[0388] The user reviews the correction suggestions and finalizes the ad text. The user can change the ad text to "This foundation has a natural finish and is gentle enough for everyday use!"

[0389] Step 9:

[0390] The device sends the final ad text to the server, where it is stored in a database and used for subsequent ad serving.

[0391] Through this series of steps, advertising text that is highly relevant to the target audience can be generated, improving advertising effectiveness.

[0392] Example 1

[0393] 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."

[0394] If advertising text is not effectively communicated to the target audience, the effectiveness of the advertisement will decrease, resulting in problems such as product or service awareness and sales not increasing as expected. Furthermore, creating advertisements that reflect the needs and feedback of the target audience requires a great deal of effort and time. As a result, there is a challenge in efficiently and quickly creating advertising text that is optimized for the target audience.

[0395] 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.

[0396] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for notifying of suggested corrections, and means for saving the advertising text in a database, thereby enabling automatic generation and correction of advertising text optimized for the target demographic.

[0397] "Advertising text" is text that explains the features and benefits of the products or services that an advertiser offers to its target audience.

[0398] The "target demographic" is a group of people with specific attribute information, and is the customer demographic that is expected to receive the advertising text.

[0399] "Attribute information" is data that indicates the characteristics of the target demographic, such as age, gender, hobbies, interests, and purchasing history.

[0400] "Feedback comments" are sentences generated to represent the impressions and opinions of the target audience regarding the advertising text.

[0401] A "natural language generation algorithm" is a computational method for generating human language using artificial intelligence techniques.

[0402] "Analysis" is the process of extracting the characteristics and interests of the target audience based on data.

[0403] "Adjustment" refers to optimizing the ad text based on the generated feedback comments and modifying it to make it more appropriate for the target audience.

[0404] "Notification" is the act of informing the user of the generated amendment proposal.

[0405] A "database" is a system for efficiently storing, managing, and retrieving structured data.

[0406] A "terminal" is a computer or mobile device used by a user to input information and receive feedback from a server.

[0407] A "server" is a computer system for receiving, processing, and storing data sent from a terminal.

[0408] The system of the present invention is designed to optimize advertising text for a target demographic and improve advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0409] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s."

[0410] The terminal then receives this input information and transmits it over the network to the server, where the terminal acts as a bridge between the user and the server.

[0411] The server analyzes the characteristics and interests of the target audience based on the attribute information it receives. For this analysis, the server uses past advertising data, demographic information, and other related databases. For example, information related to fashion, cosmetics, and lifestyles for women in their 20s is utilized at this stage. Specific databases used here include Google BigQuery and Amazon RDS.

[0412] Based on the analysis results, the server uses a natural language generation algorithm (e.g., OpenAI GPT-3) to generate representative feedback comments from the target demographic. The generated feedback comments include specific examples such as "I want to use products that are gentle on my skin" and "I want a foundation that I can use every day." Examples of prompt sentences used for this purpose are as follows:

[0413] Example prompt sentence:

[0414] "Generate feedback comments about the foundations that women in their 20s want."

[0415] The server reviews the generated feedback comments and extracts commonalities and key feedback points. For example, if there is a lot of feedback such as "protects skin" and "want to use it every day," these common needs are extracted as analysis results. A clustering method (e.g., K-means clustering) can be used for this analysis.

[0416] The server then generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggestion such as, "This foundation has a natural finish and is gentle on the skin even when used daily!" The suggested revisions are then notified to the user via their device.

[0417] The user reviews the proposed corrections and finalizes the ad text. For example, the final ad text might read, "This foundation has a natural finish and is gentle enough for daily use!"

[0418] Finally, the device sends the final ad text to the server, which stores this information in a database, allowing the system to easily reference the final text later.

[0419] This series of processes enables the automatic and efficient generation of advertising text that is highly relevant to the target demographic, enabling advertisers to quickly provide effective advertisements that reflect the needs of their target demographic.

[0420] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0421] System program processing flow

[0422] Step 1: Enter your ad text and target demographics

[0423] As an advertiser, the user inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s." This sends the user's input to the system.

[0424] Input: Ad text, target demographic attributes

[0425] Output: Input data sent to the terminal

[0426] Step 2: Submitting the entered data

[0427] The terminal receives user input data in real time and sends it to the server via the network as a POST request, where the data is packaged in JSON format.

[0428] Input: Ad text received from the user and target demographic attributes

[0429] Output: JSON data sent to the server

[0430] Step 3: Analyze your target audience

[0431] Based on the target demographic information received by the server, a specific analytical algorithm is used to analyze the characteristics and interests of the target demographic, using appropriate past advertising data and demographic information from a database.

[0432] Input: Target demographic attributes, past advertising data, demographic information

[0433] Output: Analysis of the characteristics and interests of the target audience

[0434] Step 4: Generate feedback comments

[0435] Based on the analysis results, the server uses a natural language generation algorithm (e.g., a generative AI model) to generate representative feedback comments from the target demographic.

[0436] Input: Analysis results, prompt text (e.g., "Please generate feedback comments about the foundation that women in their 20s want.")

[0437] Output: Generated feedback comments

[0438] Step 5: Review feedback comments

[0439] The server reviews the generated feedback comments and extracts commonalities and key feedback points using clustering techniques (e.g., K-means clustering).

[0440] Input: Generated feedback comments

[0441] Output: Analysis results including commonalities and key points

[0442] Step 6: Generate correction instructions for the ad text

[0443] The server generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggested revision: "This foundation has a natural finish and is gentle enough for daily use!"

[0444] Input: Review results, ad text

[0445] Output: Ad text revision suggestions

[0446] Step 7: Notification of proposed amendments

[0447] The server notifies the user of the generated correction proposal via the terminal, which receives the data from the server and displays it on the user interface (UI).

[0448] Input: Ad text revision suggestions

[0449] Output: Correction proposals notified to the user

[0450] Step 8: Final review and confirmation

[0451] The user reviews the proposed corrections and confirms the final ad text on the system UI. When the user clicks the confirm button, the device resends the information to the server.

[0452] Input: Proposed amendment, final user review

[0453] Output: Finalized ad text

[0454] Step 9: Save your final ad text

[0455] The device sends the finalized ad text to the server, which stores this information in a database so the system can reference the final text later.

[0456] Input: Confirmed ad text

[0457] Output: Ad text stored on the server

[0458] The above is the specific flow of each processing step of the system.

[0459] (Application example 1)

[0460] 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."

[0461] Conventional methods for creating ad texts have had the problem of making it difficult to efficiently reflect the needs and feedback of the target audience, resulting in reduced advertising effectiveness. Furthermore, the ad creation process was cumbersome, as advertisers needed a high level of expertise and time to accurately grasp the characteristics of their target audience and generate and edit appropriate ad text.

[0462] 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.

[0463] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of a target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for inputting the advertising text and attribute information of the target demographic via a smart device, and means for saving the generated advertising text, thereby enabling advertisers to quickly and easily generate and optimize advertising text that is more effective for the target demographic.

[0464] - "Advertising text" means any text or message used to promote a product or service.

[0465] "Target demographic information" is data that describes the characteristics, such as age, gender, and interests, of a specific group of people who will receive an advertisement.

[0466] "Representative feedback comments from the target audience" are sentences that show the target audience's typical reactions and opinions to a particular advertisement.

[0467] "Analysis of feedback comments" refers to the process of analyzing the content of the generated comments and extracting commonalities and key points.

[0468] "Correcting the ad text" means modifying the content of the ad text based on feedback comments to make it more relevant to the target audience.

[0469] "Determining the final advertising text" refers to the process of finally determining the content of the corrected advertising text.

[0470] "Smart devices" are devices such as mobile phones and eyeglass-type information terminals connected to the Internet that advertisers use to input, confirm, and correct data.

[0471] A "natural language generation algorithm" is a computational method or model for generating language that humans naturally use.

[0472] A "database" is a system that stores large amounts of data in an organized manner and allows quick access to specific information.

[0473] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0474] First, a user acts as an advertiser and uses a smart device (smartphone or smart glasses) to input the ad text and target demographic information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic.

[0475] The device then receives this input information and sends it to a server. The server then analyzes the characteristics and interests of the target audience based on their attribute information. This analysis utilizes past advertising data, demographic information, and other related databases. The analysis is performed using a natural language generation algorithm (such as GPT-3).

[0476] Based on the analysis results, the server uses a generative AI model to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can use every day."

[0477] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0478] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0479] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0480] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0481] Computer systems and their operation

[0482] The system consists of several important hardware and software elements.

[0483] Hardware used:

[0484] Smartphone

[0485] Smart Glasses

[0486] Software used:

[0487] Server side: Flask or Django

[0488] Generative AI model: GPT-3

[0489] Data transmission: Requests

[0490] Examples of specific examples and prompts

[0491] As a specific example, an advertiser uses a smart device to enter the following information into the app:

[0492] Ad text: "New foundation makes your skin glow!"

[0493] Target demographic: Women in their 20s

[0494] The server receives the data, analyzes it, and uses an AI model to generate feedback comments and provide optimized ad text, such as:

[0495] Optimized ad text: "A foundation that gives a natural finish and is gentle enough for everyday use!"

[0496] An example of an input prompt for the generative AI model is as follows:

[0497] prompt:

[0498] Target demographics:

[0499] Age: 20s

[0500] Gender: Female

[0501] Ad text:

[0502] "Your new foundation will make your skin glow!"

[0503] Generate feedback comments for this target audience.

[0504] The generative AI model generates feedback comments like the following:

[0505] "I want a foundation that I can use every day."

[0506] It's important to use ingredients that are gentle on the skin.

[0507] Emphasis on a natural finish

[0508] Based on this feedback, the server generates optimized ad text and provides it to the user.

[0509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0510] Step 1:

[0511] The user uses a smart device to input the ad text and demographic information for the target demographic. The input data is "Our new foundation will make your skin glow!" (ad text) and "Women in their 20s" (target demographic). This data is saved on the device and then sent to the server.

[0512] Step 2:

[0513] The device sends the received ad text and target demographic attribute information to the server. The input data is sent in JSON format, and the server receives it and prepares it for analysis.

[0514] Step 3:

[0515] The server performs analysis based on the target demographic's attribute information. Specifically, it derives the target demographic's characteristics and interests using related databases such as past advertising data and demographic information. The input is the target demographic's attribute information, and the output is the analysis results.

[0516] Step 4:

[0517] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate representative feedback comments from the target demographic. The input is the analysis results, and the output is the generated feedback comments. This feedback might be something like, "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0518] Step 5:

[0519] The server reviews the feedback comments and extracts commonalities and key feedback points. Here, the generated feedback comments are analyzed to identify important keywords and common needs. The input is the generated feedback comments, and the output is the extracted feedback points.

[0520] Step 6:

[0521] The server generates instructions to correct the ad text based on the extracted feedback points. Specific correction suggestions include, "This foundation has a natural finish and is gentle enough for daily use!" The input is the extracted feedback points, and the output is the correction instructions.

[0522] Step 7:

[0523] The server sends the correction instructions to the terminal and notifies the user. The user reviews the correction proposal and modifies the final advertising text as necessary. The input is the correction instructions, and the output is the final advertising text reviewed by the user.

[0524] Step 8:

[0525] The user finalizes the final ad text, and the device sends the final text to the server. The final ad text might be something like, "This foundation has a natural finish and is gentle enough for everyday use!"

[0526] Step 9:

[0527] The server stores the finalized ad text in a database. The input is the finalized ad text, and the output is the stored ad text. This ensures that the most appropriate ad is delivered to the target audience.

[0528] 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.

[0529] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. In particular, the present invention combines an emotion engine that recognizes user emotions, enabling more accurate generation and correction of advertising text. Specific system embodiments are described below.

[0530] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0531] The device then receives this input information and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0532] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0533] The server generates instructions to revise the ad text based on the generated feedback comments. The emotion engine analyzes the emotional tone of the comments and reflects the results in the revision. For example, the server generates a revision suggestion such as "This foundation has a natural finish and is gentle on the skin even when used daily," and notifies the user via their device.

[0534] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text could be, for example, "This foundation has a natural finish and is gentle enough for daily use."

[0535] The final ad text, once confirmed by the device, is sent to the server and stored in a database. This series of processes makes it possible to generate ads that are highly compatible with the target demographic and do not feel out of place. The introduction of an emotion engine also makes it possible to more precisely express emotions in the ad text, resulting in more effective ad delivery.

[0536] In this way, the system of the present invention aims to reduce the sense of discomfort caused by differences in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0537] The processing flow will be explained below.

[0538] Step 1:

[0539] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0540] Step 2:

[0541] The device receives the entered information and sends it to the server. Specifically, it transfers the advertising text and target demographic information entered in the input form to the server.

[0542] Step 3:

[0543] The server performs an analysis based on the target demographic attribute information received. It references past advertising data and demographic information to identify the characteristics and general interests of the target demographic. This analysis provides a detailed understanding of the target demographic profile.

[0544] Step 4:

[0545] The server uses a natural language generation algorithm to generate representative feedback comments based on the target demographic, while simultaneously utilizing an emotion engine to analyze the emotional tone of the generated comments. For example, for a comment such as "I want to use skin-friendly products," the server identifies emotional tones such as "reassurance" and "trust."

[0546] Step 5:

[0547] The server reviews the feedback comments analyzed by the sentiment engine and extracts common themes and key needs. For example, if many comments contain feedback such as "protects skin" or "can be used daily," this will be recognized as a key need.

[0548] Step 6:

[0549] The server generates instructions to correct the advertising text based on the analysis of the feedback comments and their emotional tone. Specifically, it creates a suggested correction, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0550] Step 7:

[0551] The device displays the proposed corrections to the user, who then reviews them. The analysis results of the emotion engine are also displayed, allowing the user to understand the emotions of the target audience before reviewing the text.

[0552] Step 8:

[0553] The user reviews the proposed revisions and finalizes the ad text. Specifically, the user confirms and confirms the final ad text: "This foundation has a natural finish and is gentle on the skin, even when used daily!"

[0554] Step 9:

[0555] The device sends the final ad text to the server, which stores it in a database for use in subsequent ad campaigns.

[0556] Through this series of steps, the system of the present invention generates advertising text that matches the emotions and needs of the target audience, improving advertising effectiveness. By introducing an emotion engine, it is possible to capture the emotional tone of the target audience and create advertisements that are more relatable.

[0557] Example 2

[0558] 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."

[0559] While conventional ad text generation systems perform analysis based on the target demographic's attribute information, they do not perform precise adjustments based on emotions, making it difficult to generate ad text that is optimized for the target demographic and has a high affinity with the target demographic. Furthermore, after the final ad text is finalized, it is not properly saved or managed, making subsequent analysis and revision difficult.

[0560] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting advertising text, a means for inputting attribute information of the target demographic, a means for analyzing based on the attribute information of the target demographic, a means for generating representative feedback comments from the target demographic, a means for correcting the advertising text based on the analysis results of the generated feedback comments, a means for finalizing the advertising text, a means for analyzing the emotional tone of the generated feedback comments and correcting the advertising text including the analyzed emotional tone, and a means for saving the finalized advertising text. This makes it possible to generate sophisticated advertising text that takes into consideration the emotions of the target demographic and to appropriately save and manage it.

[0561] "Advertising text" is a sentence that expresses the message that the advertiser wants to convey to the target audience.

[0562] "Target demographic" refers to people with specific attributes (age, gender, interests, etc.) that are targeted by the ad text.

[0563] "Attribute information" is detailed data about the specific attributes of the target demographic (age, gender, interests, etc.).

[0564] "Feedback comments" are sentences that express opinions and impressions received from the target demographic.

[0565] "Natural language generation algorithms" refer to computational techniques and methods for generating natural language that humans can understand.

[0566] "Emotional tone" is information that expresses emotions (such as joy, sadness, or anger) detected from text.

[0567] "Analysis" is the process of examining input data in detail and extracting hidden patterns and information.

[0568] "Correction" means modifying the entered advertising text to make it optimal for the target audience.

[0569] "Final" means that the final ad text has been decided and will not be revised further.

[0570] "Storage" means keeping the confirmed advertising text in a database or the like for subsequent use or analysis.

[0571] The system of the present invention aims to generate advanced advertising text and improve its effectiveness, and by combining it with an emotion engine in particular, it is possible to generate advertising text that has a higher affinity with the target demographic.

[0572] First, the user acts as an advertiser and inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device and sent to the server.

[0573] Next, the server performs an analysis based on the target demographic attribute information received. This analysis utilizes past advertising data, demographic information, and other related databases. As a specific example, the analysis targets past advertising data related to "beauty and health" for the target demographic of "women in their 20s."

[0574] Based on the analysis results, the server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. The emotion engine is also used to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0575] The server generates instructions to amend the ad text based on the generated feedback comments. At this time, the emotional tone detected by the emotion engine is also reflected in the amendments. For example, a suggested amendment might be, "This foundation has a natural finish and is gentle on the skin even when used daily." The proposed amendments are notified to the user via their device.

[0576] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text might be, for example, "This foundation has a natural finish and is gentle on the skin even when used daily." The final ad text is sent from the device to the server and stored in a database.

[0577] This series of processes makes it possible to generate advertisements that are highly relevant to the target audience and do not feel out of place. In addition, the introduction of an emotion engine allows for more refined emotional expression in the ad text, making it possible to deliver effective advertisements.

[0578] A specific example of a prompt is as follows:

[0579] User: Enter the ad text "New foundation will make your skin glow!" and the target demographic "Women in their 20s."

[0580] Server: Now that the ad text and demographic information has been received, analysis begins.

[0581] Server: Using the emotion engine, the following feedback comment was generated:

[0582] "This foundation is amazing!" (Emotional tone: joy, satisfaction)

[0583] Server: We suggest you revise your ad text as follows:

[0584] "This foundation has a natural finish and is gentle on the skin even when used daily."

[0585] Device: Suggested fix sent to user.

[0586] User: Review the proposed changes and confirm the final ad text: "This foundation has a natural finish and is gentle enough for everyday use."

[0587] Device: The final ad text was sent to the server and stored in the database.

[0588] As described above, the system of the present invention aims to reduce the sense of incongruity caused by the difference in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0590] Step 1:

[0591] The user inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device. The input data is the ad text and the demographic information of the target demographic.

[0592] Step 2:

[0593] The device sends the received input information to the server. The data sent includes the ad text and demographic information of the target audience.

[0594] Step 3:

[0595] The server analyzes the received information. For the analysis, it uses past advertising data, demographic information, and other related databases. For example, it uses past advertising data aimed at women in their 20s to analyze the characteristics of the target demographic. The input data is the received advertising text and attribute information of the target demographic, and the output data is the analysis results of the target demographic's characteristics and interests.

[0596] Step 4:

[0597] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, it generates a feedback comment such as "This foundation is truly amazing!" and detects "joy" or "satisfaction" as its emotional tone. The input data is the analysis result, and the output data is the feedback comment and its emotional tone.

[0598] Step 5:

[0599] The server generates instructions for correcting the advertising text based on the generated feedback comments. The corrections also include the emotional tone detected by the emotion engine. For example, a correction suggestion may be generated such as "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the feedback comments and their emotional tone, and the output data are correction suggestions for the advertising text.

[0600] Step 6:

[0601] The terminal notifies the user of the proposed corrections. The notified data is the proposed corrections to the advertisement text.

[0602] Step 7:

[0603] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. For example, the final ad text may be "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the proposed revisions to the ad text, and the output data is the final ad text.

[0604] Step 8:

[0605] The device sends the final ad text to the server, which stores it in a database. The data stored is the final ad text. This process generates a refined ad text that is stored in a format that is useful for future use and analysis.

[0606] Through the above processing steps, it becomes possible to generate and manage effective advertising texts that are optimized for the target demographic.

[0607] (Application example 2)

[0608] 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."

[0609] With conventional advertising systems, it was difficult to generate effective ad text for the target demographic, making it difficult to improve advertising effectiveness.In addition, because it was not possible to adjust ad text to reflect user emotions and real-time reactions, ads that were perceived as unnatural by the target demographic were sometimes delivered.

[0610] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for determining the final advertising text, means for collecting user data, emotion analysis means for analyzing the user's facial expressions and tone of voice, means for optimizing the advertising text in real time based on the emotion analysis results, and means for displaying the corrected advertising text. This makes it possible to generate and display highly accurate advertising text that reflects the user's emotions.

[0611] The "means for inputting advertisement text" is a mechanism that provides an interface for the advertiser to input the content of the advertisement.

[0612] The "means for inputting attribute information of the target demographic" is a mechanism that provides an interface for inputting information about the specific user demographic targeted by the advertisement.

[0613] "Means for analyzing based on the attribute information of the target demographic" refers to a mechanism for analyzing the characteristics and interests of that demographic based on the target demographic's attribute information.

[0614] The "means for generating representative feedback comments from the target demographic" is a mechanism for automatically generating anticipated reactions from the target user demographic.

[0615] The "means for correcting the advertisement text based on the analysis results of the generated feedback comments" is a mechanism for correcting the advertisement content based on the analysis results of the generated feedback comments.

[0616] The "means for determining the final advertisement text" is a mechanism for finally determining the corrected advertisement text.

[0617] "Means for collecting user data" refers to a mechanism for collecting data from users (facial expressions, voice, etc.).

[0618] The "emotion analysis means for analyzing the user's facial expressions and voice tones" is a mechanism for analyzing emotions from the collected user's facial expressions and voice.

[0619] "Means for optimizing advertising text in real time based on the results of sentiment analysis" refers to a mechanism for optimizing advertising text on the spot based on the analyzed sentiment data.

[0620] The "means for displaying the corrected advertising text" is a device or interface for displaying the optimized advertising text to the user.

[0621] The present invention provides a system for displaying advertising text optimized for a specific demographic. This system optimizes text using a sentiment analysis engine and a natural language generation algorithm based on advertising text entered by an advertiser and attribute information of the target demographic.

[0622] 1. System Configuration

[0623] The system includes the following major components:

[0624] 1. How to enter ad text and attribute information:

[0625] Advertisers input their advertising text and target demographic information via their terminals, and this data is sent to the server.

[0626] 2. Analysis method:

[0627] The server then analyzes the target demographic information it receives, using past advertising data and demographic information.

[0628] 3. Feedback Comment Generation Method:

[0629] It uses natural language generation algorithms to generate representative feedback comments from your target audience, while a sentiment analysis engine analyzes the emotional tone of the comments.

[0630] 4. Ad text correction methods:

[0631] Based on the analysis of the generated feedback comments, the ad text is revised. The emotional tone of the feedback comments is reflected by the emotion engine.

[0632] 5. Final ad text confirmation method:

[0633] The advertiser reviews and confirms the revised ad text, which is then saved on the server.

[0634] 6. User Data Collection Methods:

[0635] Smart glasses and other devices are used to collect data such as a user's facial expressions and tone of voice.

[0636] 7. Emotion analysis means:

[0637] The collected user data is analyzed to understand the user's emotional tone.

[0638] 8. Text optimization methods:

[0639] Ad text is optimized in real time based on sentiment analysis results.

[0640] 9. Advertising display means:

[0641] The optimized ad text is displayed to the user through a device such as smart glasses.

[0642] 2. Program Processing Overview

[0643] The server first receives the advertisement text and target demographic attribute information entered by the advertiser. Then, it uses an analysis means to analyze the characteristics and interests of the target demographic and generates feedback comments. The emotional tone of the feedback comments is analyzed by an emotion analysis engine, and the advertisement text is corrected based on the results.

[0644] In real time, emotion analysis is performed from the user's facial expressions and voice tones collected using a user data collection means, and the advertisement text is optimized based on the emotion analysis results. The optimized advertisement text is then displayed to the user through a device such as smart glasses.

[0645] 3. Specific Examples

[0646] For example, use the following prompt:

[0647] Prompt Sentence Examples

[0648] User Data: "Expression: Smiling, Tone of Voice: Excited"

[0649] Ad text: "Try out your new smartwatch!"

[0650] Target demographic: "Men in their 30s"

[0651] In this example, if the user is smiling and has an excited voice, the ad text will be optimized to match the user's emotions, for example, "This smartwatch will make your daily life more fun!"

[0652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0653] Step 1:

[0654] The terminal receives advertising text and target demographic attribute information from the advertiser and sends the data to the server. The terminal receives advertising text and target demographic attribute information as input and processes the data before sending it to the server.

[0655] Step 2:

[0656] The server analyzes the target demographic attribute information it receives. Using the target demographic attribute information, past advertising data, and demographic information as input, it performs data calculations to extract the characteristics and interests of the target demographic.

[0657] Step 3:

[0658] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic based on the analysis results, and processes the data to generate the feedback comments using the analysis results as input.

[0659] Step 4:

[0660] The server analyzes the emotional tone of the generated feedback comments using an emotion engine, and performs a data operation using the feedback comments as input to analyze the emotional tone.

[0661] Step 5:

[0662] The server corrects the advertisement text based on the sentiment analysis result, using the emotional tone of the feedback comments as input and performing correction operations on the advertisement text.

[0663] Step 6:

[0664] The terminal notifies the advertiser of the proposed amendments, and the advertiser finalizes the ad text. The proposed amendments are used as input to perform data processing to finalize the ad text.

[0665] Step 7:

[0666] The server stores the final ad text in the database. Using the final ad text as input, it processes the data to store in the database.

[0667] Step 8:

[0668] Data on facial expressions and voice tones are collected by the user using smart glasses. Data calculations are performed using the collected user facial expressions and voice data as input.

[0669] Step 9:

[0670] The server analyzes the collected user data to identify the user's emotional tone. Using the user's facial expression and voice data as input, it performs data calculations to analyze the emotional tone.

[0671] Step 10:

[0672] The server optimizes the ad text in real time based on the sentiment analysis results. Using the sentiment analysis results as input, it processes the data to optimize the ad text.

[0673] Step 11:

[0674] The optimized advertisement text is displayed to the user through the smart glasses. Using the optimized advertisement text as input, a data calculation is performed to display the data to the user.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Third embodiment]

[0679] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0680] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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."

[0691] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0692] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0693] The device then receives this input and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0694] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0695] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0696] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0697] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0698] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0699] The system of the present invention makes it possible to reduce the sense of discomfort caused by differences in attributes between advertisers and their target demographics, thereby improving advertising effectiveness.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0703] Step 2:

[0704] The device receives the input information and sends it to the server, which uses a communication protocol to transfer the ad text and demographic information to the server.

[0705] Step 3:

[0706] The server analyzes the target demographic attribute information it receives, referencing past advertising data and demographic information to identify common characteristics and interests of women in their 20s.

[0707] Step 4:

[0708] The server uses a natural language generation algorithm to generate typical feedback comments from the target demographic, such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0709] Step 5:

[0710] The server analyzes the generated feedback comments. It reviews all comments and extracts common themes and key needs, such as "protects skin" or "want to use it every day."

[0711] Step 6:

[0712] The server analyzes the feedback comments and generates instructions to correct the ad text, such as "This foundation has a natural finish and is gentle enough for everyday use!"

[0713] Step 7:

[0714] The terminal receives the correction proposal and notifies the user, who then checks the notification and reviews the advertisement text based on the correction proposal.

[0715] Step 8:

[0716] The user reviews the correction suggestions and finalizes the ad text. The user can change the ad text to "This foundation has a natural finish and is gentle enough for everyday use!"

[0717] Step 9:

[0718] The device sends the final ad text to the server, where it is stored in a database and used for subsequent ad serving.

[0719] Through this series of steps, advertising text that is highly relevant to the target audience can be generated, improving advertising effectiveness.

[0720] Example 1

[0721] 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."

[0722] If advertising text is not effectively communicated to the target audience, the effectiveness of the advertisement will decrease, resulting in problems such as product or service awareness and sales not increasing as expected. Furthermore, creating advertisements that reflect the needs and feedback of the target audience requires a great deal of effort and time. As a result, there is a challenge in efficiently and quickly creating advertising text that is optimized for the target audience.

[0723] 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.

[0724] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for notifying of suggested corrections, and means for saving the advertising text in a database, thereby enabling automatic generation and correction of advertising text optimized for the target demographic.

[0725] "Advertising text" is text that explains the features and benefits of the products or services that an advertiser offers to its target audience.

[0726] The "target demographic" is a group of people with specific attribute information, and is the customer demographic that is expected to receive the advertising text.

[0727] "Attribute information" is data that indicates the characteristics of the target demographic, such as age, gender, hobbies, interests, and purchasing history.

[0728] "Feedback comments" are sentences generated to represent the impressions and opinions of the target audience regarding the advertising text.

[0729] A "natural language generation algorithm" is a computational method for generating human language using artificial intelligence techniques.

[0730] "Analysis" is the process of extracting the characteristics and interests of the target audience based on data.

[0731] "Adjustment" refers to optimizing the ad text based on the generated feedback comments and modifying it to make it more appropriate for the target audience.

[0732] "Notification" is the act of informing the user of the generated amendment proposal.

[0733] A "database" is a system for efficiently storing, managing, and retrieving structured data.

[0734] A "terminal" is a computer or mobile device used by a user to input information and receive feedback from a server.

[0735] A "server" is a computer system for receiving, processing, and storing data sent from a terminal.

[0736] The system of the present invention is designed to optimize advertising text for a target demographic and improve advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0737] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s."

[0738] The terminal then receives this input information and transmits it over the network to the server, where the terminal acts as a bridge between the user and the server.

[0739] The server analyzes the characteristics and interests of the target audience based on the attribute information it receives. For this analysis, the server uses past advertising data, demographic information, and other related databases. For example, information related to fashion, cosmetics, and lifestyles for women in their 20s is utilized at this stage. Specific databases used here include Google BigQuery and Amazon RDS.

[0740] Based on the analysis results, the server uses a natural language generation algorithm (e.g., OpenAI GPT-3) to generate representative feedback comments from the target demographic. The generated feedback comments include specific examples such as "I want to use products that are gentle on my skin" and "I want a foundation that I can use every day." Examples of prompt sentences used for this purpose are as follows:

[0741] Example prompt sentence:

[0742] "Generate feedback comments about the foundations that women in their 20s want."

[0743] The server reviews the generated feedback comments and extracts commonalities and key feedback points. For example, if there is a lot of feedback such as "protects skin" and "want to use it every day," these common needs are extracted as analysis results. A clustering method (e.g., K-means clustering) can be used for this analysis.

[0744] The server then generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggestion such as, "This foundation has a natural finish and is gentle on the skin even when used daily!" The suggested revisions are then notified to the user via their device.

[0745] The user reviews the proposed corrections and finalizes the ad text. For example, the final ad text might read, "This foundation has a natural finish and is gentle enough for daily use!"

[0746] Finally, the device sends the final ad text to the server, which stores this information in a database, allowing the system to easily reference the final text later.

[0747] This series of processes enables the automatic and efficient generation of advertising text that is highly relevant to the target demographic, enabling advertisers to quickly provide effective advertisements that reflect the needs of their target demographic.

[0748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0749] System program processing flow

[0750] Step 1: Enter your ad text and target demographics

[0751] As an advertiser, the user inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s." This sends the user's input to the system.

[0752] Input: Ad text, target demographic attributes

[0753] Output: Input data sent to the terminal

[0754] Step 2: Submitting the entered data

[0755] The terminal receives user input data in real time and sends it to the server via the network as a POST request, where the data is packaged in JSON format.

[0756] Input: Ad text received from the user and target demographic attributes

[0757] Output: JSON data sent to the server

[0758] Step 3: Analyze your target audience

[0759] Based on the target demographic information received by the server, a specific analytical algorithm is used to analyze the characteristics and interests of the target demographic, using appropriate past advertising data and demographic information from a database.

[0760] Input: Target demographic attributes, past advertising data, demographic information

[0761] Output: Analysis of the characteristics and interests of the target audience

[0762] Step 4: Generate feedback comments

[0763] Based on the analysis results, the server uses a natural language generation algorithm (e.g., a generative AI model) to generate representative feedback comments from the target demographic.

[0764] Input: Analysis results, prompt text (e.g., "Please generate feedback comments about the foundation that women in their 20s want.")

[0765] Output: Generated feedback comments

[0766] Step 5: Review feedback comments

[0767] The server reviews the generated feedback comments and extracts commonalities and key feedback points using clustering techniques (e.g., K-means clustering).

[0768] Input: Generated feedback comments

[0769] Output: Analysis results including commonalities and key points

[0770] Step 6: Generate correction instructions for the ad text

[0771] The server generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggested revision: "This foundation has a natural finish and is gentle enough for daily use!"

[0772] Input: Review results, ad text

[0773] Output: Ad text revision suggestions

[0774] Step 7: Notification of proposed amendments

[0775] The server notifies the user of the generated correction proposal via the terminal, which receives the data from the server and displays it on the user interface (UI).

[0776] Input: Ad text revision suggestions

[0777] Output: Correction proposals notified to the user

[0778] Step 8: Final review and confirmation

[0779] The user reviews the proposed corrections and confirms the final ad text on the system UI. When the user clicks the confirm button, the device resends the information to the server.

[0780] Input: Proposed amendment, final user review

[0781] Output: Finalized ad text

[0782] Step 9: Save your final ad text

[0783] The device sends the finalized ad text to the server, which stores this information in a database so the system can reference the final text later.

[0784] Input: Confirmed ad text

[0785] Output: Ad text stored on the server

[0786] The above is the specific flow of each processing step of the system.

[0787] (Application example 1)

[0788] 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."

[0789] Conventional methods for creating ad texts have had the problem of making it difficult to efficiently reflect the needs and feedback of the target audience, resulting in reduced advertising effectiveness. Furthermore, the ad creation process was cumbersome, as advertisers needed a high level of expertise and time to accurately grasp the characteristics of their target audience and generate and edit appropriate ad text.

[0790] 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.

[0791] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of a target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for inputting the advertising text and attribute information of the target demographic via a smart device, and means for saving the generated advertising text, thereby enabling advertisers to quickly and easily generate and optimize advertising text that is more effective for the target demographic.

[0792] - "Advertising text" means any text or message used to promote a product or service.

[0793] "Target demographic information" is data that describes the characteristics, such as age, gender, and interests, of a specific group of people who will receive an advertisement.

[0794] "Representative feedback comments from the target audience" are sentences that show the target audience's typical reactions and opinions to a particular advertisement.

[0795] "Analysis of feedback comments" refers to the process of analyzing the content of the generated comments and extracting commonalities and key points.

[0796] "Correcting the ad text" means modifying the content of the ad text based on feedback comments to make it more relevant to the target audience.

[0797] "Determining the final advertising text" refers to the process of finally determining the content of the corrected advertising text.

[0798] "Smart devices" are devices such as mobile phones and eyeglass-type information terminals connected to the Internet that advertisers use to input, confirm, and correct data.

[0799] A "natural language generation algorithm" is a computational method or model for generating language that humans naturally use.

[0800] A "database" is a system that stores large amounts of data in an organized manner and allows quick access to specific information.

[0801] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[0802] First, a user acts as an advertiser and uses a smart device (smartphone or smart glasses) to input the ad text and target demographic information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic.

[0803] The device then receives this input information and sends it to a server. The server then analyzes the characteristics and interests of the target audience based on their attribute information. This analysis utilizes past advertising data, demographic information, and other related databases. The analysis is performed using a natural language generation algorithm (such as GPT-3).

[0804] Based on the analysis results, the server uses a generative AI model to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can use every day."

[0805] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[0806] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0807] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[0808] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[0809] Computer systems and their operation

[0810] The system consists of several important hardware and software elements.

[0811] Hardware used:

[0812] Smartphone

[0813] Smart Glasses

[0814] Software used:

[0815] Server side: Flask or Django

[0816] Generative AI model: GPT-3

[0817] Data transmission: Requests

[0818] Examples of specific examples and prompts

[0819] As a specific example, an advertiser uses a smart device to enter the following information into the app:

[0820] Ad text: "New foundation makes your skin glow!"

[0821] Target demographic: Women in their 20s

[0822] The server receives the data, analyzes it, and uses an AI model to generate feedback comments and provide optimized ad text, such as:

[0823] Optimized ad text: "A foundation that gives a natural finish and is gentle enough for everyday use!"

[0824] An example of an input prompt for the generative AI model is as follows:

[0825] prompt:

[0826] Target demographics:

[0827] Age: 20s

[0828] Gender: Female

[0829] Ad text:

[0830] "Your new foundation will make your skin glow!"

[0831] Generate feedback comments for this target audience.

[0832] The generative AI model generates feedback comments like the following:

[0833] "I want a foundation that I can use every day."

[0834] It's important to use ingredients that are gentle on the skin.

[0835] Emphasis on a natural finish

[0836] Based on this feedback, the server generates optimized ad text and provides it to the user.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] The user uses a smart device to input the ad text and demographic information for the target demographic. The input data is "Our new foundation will make your skin glow!" (ad text) and "Women in their 20s" (target demographic). This data is saved on the device and then sent to the server.

[0840] Step 2:

[0841] The device sends the received ad text and target demographic attribute information to the server. The input data is sent in JSON format, and the server receives it and prepares it for analysis.

[0842] Step 3:

[0843] The server performs analysis based on the target demographic's attribute information. Specifically, it derives the target demographic's characteristics and interests using related databases such as past advertising data and demographic information. The input is the target demographic's attribute information, and the output is the analysis results.

[0844] Step 4:

[0845] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate representative feedback comments from the target demographic. The input is the analysis results, and the output is the generated feedback comments. This feedback might be something like, "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[0846] Step 5:

[0847] The server reviews the feedback comments and extracts commonalities and key feedback points. Here, the generated feedback comments are analyzed to identify important keywords and common needs. The input is the generated feedback comments, and the output is the extracted feedback points.

[0848] Step 6:

[0849] The server generates instructions to correct the ad text based on the extracted feedback points. Specific correction suggestions include, "This foundation has a natural finish and is gentle enough for daily use!" The input is the extracted feedback points, and the output is the correction instructions.

[0850] Step 7:

[0851] The server sends the correction instructions to the terminal and notifies the user. The user reviews the correction proposal and modifies the final advertising text as necessary. The input is the correction instructions, and the output is the final advertising text reviewed by the user.

[0852] Step 8:

[0853] The user finalizes the final ad text, and the device sends the final text to the server. The final ad text might be something like, "This foundation has a natural finish and is gentle enough for everyday use!"

[0854] Step 9:

[0855] The server stores the finalized ad text in a database. The input is the finalized ad text, and the output is the stored ad text. This ensures that the most appropriate ad is delivered to the target audience.

[0856] 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.

[0857] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. In particular, the present invention combines an emotion engine that recognizes user emotions, enabling more accurate generation and correction of advertising text. Specific system embodiments are described below.

[0858] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[0859] The device then receives this input information and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[0860] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0861] The server generates instructions to revise the ad text based on the generated feedback comments. The emotion engine analyzes the emotional tone of the comments and reflects the results in the revision. For example, the server generates a revision suggestion such as "This foundation has a natural finish and is gentle on the skin even when used daily," and notifies the user via their device.

[0862] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text could be, for example, "This foundation has a natural finish and is gentle enough for daily use."

[0863] The final ad text, once confirmed by the device, is sent to the server and stored in a database. This series of processes makes it possible to generate ads that are highly compatible with the target demographic and do not feel out of place. The introduction of an emotion engine also makes it possible to more precisely express emotions in the ad text, resulting in more effective ad delivery.

[0864] In this way, the system of the present invention aims to reduce the sense of incongruity caused by differences in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0865] The processing flow will be explained below.

[0866] Step 1:

[0867] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[0868] Step 2:

[0869] The device receives the entered information and sends it to the server. Specifically, it transfers the advertising text and target demographic information entered in the input form to the server.

[0870] Step 3:

[0871] The server performs an analysis based on the target demographic attribute information received. It references past advertising data and demographic information to identify the characteristics and general interests of the target demographic. This analysis provides a detailed understanding of the target demographic profile.

[0872] Step 4:

[0873] The server uses a natural language generation algorithm to generate representative feedback comments based on the target demographic, while simultaneously utilizing an emotion engine to analyze the emotional tone of the generated comments. For example, for a comment such as "I want to use skin-friendly products," the server identifies emotional tones such as "reassurance" and "trust."

[0874] Step 5:

[0875] The server reviews the feedback comments analyzed by the sentiment engine and extracts common themes and key needs. For example, if many comments contain feedback such as "protects skin" or "can be used daily," this will be recognized as a key need.

[0876] Step 6:

[0877] The server generates instructions to correct the advertising text based on the analysis of the feedback comments and their emotional tone. Specifically, it creates a suggested correction, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[0878] Step 7:

[0879] The device displays the proposed corrections to the user, who then reviews them. The analysis results of the emotion engine are also displayed, allowing the user to understand the emotions of the target audience before reviewing the text.

[0880] Step 8:

[0881] The user reviews the proposed revisions and finalizes the ad text. Specifically, the user confirms and confirms the final ad text: "This foundation has a natural finish and is gentle on the skin, even when used daily!"

[0882] Step 9:

[0883] The device sends the final ad text to the server, which stores it in a database for use in subsequent ad campaigns.

[0884] Through this series of steps, the system of the present invention generates advertising text that matches the emotions and needs of the target audience, improving advertising effectiveness. By introducing an emotion engine, it is possible to capture the emotional tone of the target audience and create advertisements that are more relatable.

[0885] Example 2

[0886] 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."

[0887] While conventional ad text generation systems perform analysis based on the target demographic's attribute information, they do not perform precise adjustments based on emotions, making it difficult to generate ad text that is optimized for the target demographic and has a high affinity with the target demographic. Furthermore, after the final ad text is finalized, it is not properly saved or managed, making subsequent analysis and revision difficult.

[0888] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting advertising text, a means for inputting attribute information of the target demographic, a means for analyzing based on the attribute information of the target demographic, a means for generating representative feedback comments from the target demographic, a means for correcting the advertising text based on the analysis results of the generated feedback comments, a means for finalizing the advertising text, a means for analyzing the emotional tone of the generated feedback comments and correcting the advertising text including the analyzed emotional tone, and a means for saving the finalized advertising text. This makes it possible to generate sophisticated advertising text that takes into consideration the emotions of the target demographic and to appropriately save and manage it.

[0889] "Advertising text" is a sentence that expresses the message that the advertiser wants to convey to the target audience.

[0890] "Target demographic" refers to people with specific attributes (age, gender, interests, etc.) that are targeted by the ad text.

[0891] "Attribute information" is detailed data about the specific attributes of the target demographic (age, gender, interests, etc.).

[0892] "Feedback comments" are sentences that express opinions and impressions received from the target demographic.

[0893] "Natural language generation algorithms" refer to computational techniques and methods for generating natural language that humans can understand.

[0894] "Emotional tone" is information that expresses emotions (such as joy, sadness, or anger) detected from text.

[0895] "Analysis" is the process of examining input data in detail and extracting hidden patterns and information.

[0896] "Correction" means modifying the entered advertising text to make it optimal for the target audience.

[0897] "Final" means that the final ad text has been decided and will not be revised further.

[0898] "Storage" means keeping the confirmed advertising text in a database or the like for subsequent use or analysis.

[0899] The system of the present invention aims to generate advanced advertising text and improve its effectiveness, and by combining it with an emotion engine in particular, it is possible to generate advertising text that has a higher affinity with the target demographic.

[0900] First, the user acts as an advertiser and inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device and sent to the server.

[0901] Next, the server performs an analysis based on the target demographic attribute information received. This analysis utilizes past advertising data, demographic information, and other related databases. As a specific example, the analysis targets past advertising data related to "beauty and health" for the target demographic of "women in their 20s."

[0902] Based on the analysis results, the server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. The emotion engine is also used to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[0903] The server generates instructions to amend the ad text based on the generated feedback comments. At this time, the emotional tone detected by the emotion engine is also reflected in the amendments. For example, a suggested amendment might be, "This foundation has a natural finish and is gentle on the skin even when used daily." The proposed amendments are notified to the user via their device.

[0904] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text might be, for example, "This foundation has a natural finish and is gentle on the skin even when used daily." The final ad text is sent from the device to the server and stored in a database.

[0905] This series of processes makes it possible to generate advertisements that are highly relevant to the target audience and do not feel out of place. In addition, the introduction of an emotion engine allows for more refined emotional expression in the ad text, making it possible to deliver effective advertisements.

[0906] A specific example of a prompt is as follows:

[0907] User: Enter the ad text "New foundation will make your skin glow!" and the target demographic "Women in their 20s."

[0908] Server: Now that the ad text and demographic information has been received, analysis begins.

[0909] Server: Using the emotion engine, the following feedback comment was generated:

[0910] "This foundation is amazing!" (Emotional tone: joy, satisfaction)

[0911] Server: We suggest you revise your ad text as follows:

[0912] "This foundation has a natural finish and is gentle on the skin even when used daily."

[0913] Device: Suggested fix sent to user.

[0914] User: Review the proposed changes and confirm the final ad text: "This foundation has a natural finish and is gentle enough for everyday use."

[0915] Device: The final ad text was sent to the server and stored in the database.

[0916] As described above, the system of the present invention aims to reduce the sense of incongruity caused by the difference in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[0917] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0918] Step 1:

[0919] The user inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device. The input data is the ad text and the demographic information of the target demographic.

[0920] Step 2:

[0921] The device sends the received input information to the server. The data sent includes the ad text and demographic information of the target audience.

[0922] Step 3:

[0923] The server analyzes the received information. For the analysis, it uses past advertising data, demographic information, and other related databases. For example, it uses past advertising data aimed at women in their 20s to analyze the characteristics of the target demographic. The input data is the received advertising text and attribute information of the target demographic, and the output data is the analysis results of the target demographic's characteristics and interests.

[0924] Step 4:

[0925] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, it generates a feedback comment such as "This foundation is truly amazing!" and detects "joy" or "satisfaction" as its emotional tone. The input data is the analysis result, and the output data is the feedback comment and its emotional tone.

[0926] Step 5:

[0927] The server generates instructions for correcting the advertising text based on the generated feedback comments. The corrections also include the emotional tone detected by the emotion engine. For example, a correction suggestion may be generated such as "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the feedback comments and their emotional tone, and the output data are correction suggestions for the advertising text.

[0928] Step 6:

[0929] The terminal notifies the user of the proposed corrections. The notified data is the proposed corrections to the advertisement text.

[0930] Step 7:

[0931] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. For example, the final ad text may be "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the proposed revisions to the ad text, and the output data is the final ad text.

[0932] Step 8:

[0933] The device sends the final ad text to the server, which stores it in a database. The data stored is the final ad text. This process generates a refined ad text that is stored in a format that is useful for future use and analysis.

[0934] Through the above processing steps, it becomes possible to generate and manage effective advertising texts that are optimized for the target demographic.

[0935] (Application example 2)

[0936] 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."

[0937] With conventional advertising systems, it was difficult to generate effective ad text for the target demographic, making it difficult to improve advertising effectiveness.In addition, because it was not possible to adjust ad text to reflect user emotions and real-time reactions, ads that were perceived as unnatural by the target demographic were sometimes delivered.

[0938] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for determining the final advertising text, means for collecting user data, emotion analysis means for analyzing the user's facial expressions and tone of voice, means for optimizing the advertising text in real time based on the emotion analysis results, and means for displaying the corrected advertising text. This makes it possible to generate and display highly accurate advertising text that reflects the user's emotions.

[0939] The "means for inputting advertisement text" is a mechanism that provides an interface for the advertiser to input the content of the advertisement.

[0940] The "means for inputting attribute information of the target demographic" is a mechanism that provides an interface for inputting information about the specific user demographic targeted by the advertisement.

[0941] "Means for analyzing based on the attribute information of the target demographic" refers to a mechanism for analyzing the characteristics and interests of that demographic based on the target demographic's attribute information.

[0942] The "means for generating representative feedback comments from the target demographic" is a mechanism for automatically generating anticipated reactions from the target user demographic.

[0943] The "means for correcting the advertisement text based on the analysis results of the generated feedback comments" is a mechanism for correcting the advertisement content based on the analysis results of the generated feedback comments.

[0944] The "means for determining the final advertisement text" is a mechanism for finally determining the corrected advertisement text.

[0945] "Means for collecting user data" refers to a mechanism for collecting data from users (facial expressions, voice, etc.).

[0946] The "emotion analysis means for analyzing the user's facial expressions and voice tones" is a mechanism for analyzing emotions from the collected user's facial expressions and voice.

[0947] "Means for optimizing advertising text in real time based on the results of sentiment analysis" refers to a mechanism for optimizing advertising text on the spot based on the analyzed sentiment data.

[0948] The "means for displaying the corrected advertising text" is a device or interface for displaying the optimized advertising text to the user.

[0949] The present invention provides a system for displaying advertising text optimized for a specific demographic. This system optimizes text using a sentiment analysis engine and a natural language generation algorithm based on advertising text entered by an advertiser and attribute information of the target demographic.

[0950] 1. System Configuration

[0951] The system includes the following major components:

[0952] 1. How to enter ad text and attribute information:

[0953] Advertisers input their advertising text and target demographic information via their terminals, and this data is sent to the server.

[0954] 2. Analysis method:

[0955] The server then analyzes the target demographic information it receives, using past advertising data and demographic information.

[0956] 3. Feedback Comment Generation Method:

[0957] It uses natural language generation algorithms to generate representative feedback comments from your target audience, while a sentiment analysis engine analyzes the emotional tone of the comments.

[0958] 4. Ad text correction methods:

[0959] Based on the analysis of the generated feedback comments, the ad text is revised. The emotional tone of the feedback comments is reflected by the emotion engine.

[0960] 5. Final ad text confirmation method:

[0961] The advertiser reviews and confirms the revised ad text, which is then saved on the server.

[0962] 6. User Data Collection Methods:

[0963] Smart glasses and other devices are used to collect data such as a user's facial expressions and tone of voice.

[0964] 7. Emotion analysis means:

[0965] The collected user data is analyzed to understand the user's emotional tone.

[0966] 8. Text optimization methods:

[0967] Ad text is optimized in real time based on sentiment analysis results.

[0968] 9. Advertising display means:

[0969] The optimized ad text is displayed to the user through a device such as smart glasses.

[0970] 2. Program Processing Overview

[0971] The server first receives the advertisement text and target demographic attribute information entered by the advertiser. Then, it uses an analysis means to analyze the characteristics and interests of the target demographic and generates feedback comments. The emotional tone of the feedback comments is analyzed by an emotion analysis engine, and the advertisement text is corrected based on the results.

[0972] In real time, emotion analysis is performed from the user's facial expressions and voice tones collected using a user data collection means, and the advertisement text is optimized based on the emotion analysis results. The optimized advertisement text is then displayed to the user through a device such as smart glasses.

[0973] 3. Specific Examples

[0974] For example, use the following prompt:

[0975] Prompt Sentence Examples

[0976] User Data: "Expression: Smiling, Tone of Voice: Excited"

[0977] Ad text: "Try out your new smartwatch!"

[0978] Target demographic: "Men in their 30s"

[0979] In this example, if the user is smiling and has an excited voice, the ad text will be optimized to match the user's emotions, for example, "This smartwatch will make your daily life more fun!"

[0980] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0981] Step 1:

[0982] The terminal receives advertising text and target demographic attribute information from the advertiser and sends the data to the server. The terminal receives advertising text and target demographic attribute information as input and processes the data before sending it to the server.

[0983] Step 2:

[0984] The server analyzes the target demographic attribute information it receives. Using the target demographic attribute information, past advertising data, and demographic information as input, it performs data calculations to extract the characteristics and interests of the target demographic.

[0985] Step 3:

[0986] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic based on the analysis results, and processes the data to generate the feedback comments using the analysis results as input.

[0987] Step 4:

[0988] The server analyzes the emotional tone of the generated feedback comments using an emotion engine, and performs a data operation using the feedback comments as input to analyze the emotional tone.

[0989] Step 5:

[0990] The server corrects the advertisement text based on the sentiment analysis result, using the emotional tone of the feedback comments as input and performing correction operations on the advertisement text.

[0991] Step 6:

[0992] The terminal notifies the advertiser of the proposed amendments, and the advertiser finalizes the ad text. The proposed amendments are used as input to perform data processing to finalize the ad text.

[0993] Step 7:

[0994] The server stores the final ad text in the database. Using the final ad text as input, it processes the data to store in the database.

[0995] Step 8:

[0996] Data on facial expressions and voice tones are collected by the user using smart glasses. Data calculations are performed using the collected user facial expressions and voice data as input.

[0997] Step 9:

[0998] The server analyzes the collected user data to identify the user's emotional tone. Using the user's facial expression and voice data as input, it performs data calculations to analyze the emotional tone.

[0999] Step 10:

[1000] The server optimizes the ad text in real time based on the sentiment analysis results. Using the sentiment analysis results as input, it processes the data to optimize the ad text.

[1001] Step 11:

[1002] The optimized advertisement text is displayed to the user through the smart glasses. Using the optimized advertisement text as input, a data calculation is performed to display the data to the user.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] [Fourth embodiment]

[1007] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1008] 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.

[1009] 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).

[1010] 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.

[1011] 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.

[1012] 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).

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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.

[1018] 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.

[1019] 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."

[1020] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[1021] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[1022] The device then receives this input and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[1023] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[1024] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[1025] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[1026] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[1027] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[1028] The system of the present invention makes it possible to reduce the sense of discomfort caused by differences in attributes between advertisers and their target demographics, thereby improving advertising effectiveness.

[1029] The processing flow will be explained below.

[1030] Step 1:

[1031] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[1032] Step 2:

[1033] The device receives the input information and sends it to the server, which uses a communication protocol to transfer the ad text and demographic information to the server.

[1034] Step 3:

[1035] The server analyzes the target demographic attribute information it receives, referencing past advertising data and demographic information to identify common characteristics and interests of women in their 20s.

[1036] Step 4:

[1037] The server uses a natural language generation algorithm to generate typical feedback comments from the target demographic, such as "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[1038] Step 5:

[1039] The server analyzes the generated feedback comments. It reviews all comments and extracts common themes and key needs, such as "protects skin" or "want to use it every day."

[1040] Step 6:

[1041] The server analyzes the feedback comments and generates instructions to correct the ad text, such as "This foundation has a natural finish and is gentle enough for everyday use!"

[1042] Step 7:

[1043] The terminal receives the correction proposal and notifies the user, who then checks the notification and reviews the advertisement text based on the correction proposal.

[1044] Step 8:

[1045] The user reviews the correction suggestions and finalizes the ad text. The user can change the ad text to "This foundation has a natural finish and is gentle enough for everyday use!"

[1046] Step 9:

[1047] The device sends the final ad text to the server, where it is stored in a database and used for subsequent ad serving.

[1048] Through this series of steps, advertising text that is highly relevant to the target audience can be generated, improving advertising effectiveness.

[1049] Example 1

[1050] 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."

[1051] If advertising text is not effectively communicated to the target audience, the effectiveness of the advertisement will decrease, resulting in problems such as product or service awareness and sales not increasing as expected. Furthermore, creating advertisements that reflect the needs and feedback of the target audience requires a great deal of effort and time. As a result, there is a challenge in efficiently and quickly creating advertising text that is optimized for the target audience.

[1052] 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.

[1053] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for notifying of suggested corrections, and means for saving the advertising text in a database, thereby enabling automatic generation and correction of advertising text optimized for the target demographic.

[1054] "Advertising text" is text that explains the features and benefits of the products or services that an advertiser offers to its target audience.

[1055] The "target demographic" is a group of people with specific attribute information, and is the customer demographic that is expected to receive the advertising text.

[1056] "Attribute information" is data that indicates the characteristics of the target demographic, such as age, gender, hobbies, interests, and purchasing history.

[1057] "Feedback comments" are sentences generated to represent the impressions and opinions of the target audience regarding the advertising text.

[1058] A "natural language generation algorithm" is a computational method for generating human language using artificial intelligence techniques.

[1059] "Analysis" is the process of extracting the characteristics and interests of the target audience based on data.

[1060] "Adjustment" refers to optimizing the ad text based on the generated feedback comments and modifying it to make it more appropriate for the target audience.

[1061] "Notification" is the act of informing the user of the generated amendment proposal.

[1062] A "database" is a system for efficiently storing, managing, and retrieving structured data.

[1063] A "terminal" is a computer or mobile device used by a user to input information and receive feedback from a server.

[1064] A "server" is a computer system for receiving, processing, and storing data sent from a terminal.

[1065] The system of the present invention is designed to optimize advertising text for a target demographic and improve advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[1066] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s."

[1067] The terminal then receives this input information and transmits it over the network to the server, where the terminal acts as a bridge between the user and the server.

[1068] The server analyzes the characteristics and interests of the target audience based on the attribute information it receives. For this analysis, the server uses past advertising data, demographic information, and other related databases. For example, information related to fashion, cosmetics, and lifestyles for women in their 20s is utilized at this stage. Specific databases used here include Google BigQuery and Amazon RDS.

[1069] Based on the analysis results, the server uses a natural language generation algorithm (e.g., OpenAI GPT-3) to generate representative feedback comments from the target demographic. The generated feedback comments include specific examples such as "I want to use products that are gentle on my skin" and "I want a foundation that I can use every day." Examples of prompt sentences used for this purpose are as follows:

[1070] Example prompt sentence:

[1071] "Generate feedback comments about the foundations that women in their 20s want."

[1072] The server reviews the generated feedback comments and extracts commonalities and key feedback points. For example, if there is a lot of feedback such as "protects skin" and "want to use it every day," these common needs are extracted as analysis results. A clustering method (e.g., K-means clustering) can be used for this analysis.

[1073] The server then generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggestion such as, "This foundation has a natural finish and is gentle on the skin even when used daily!" The suggested revisions are then notified to the user via their device.

[1074] The user reviews the proposed corrections and finalizes the ad text. For example, the final ad text might read, "This foundation has a natural finish and is gentle enough for daily use!"

[1075] Finally, the device sends the final ad text to the server, which stores this information in a database, allowing the system to easily reference the final text later.

[1076] This series of processes enables the automatic and efficient generation of advertising text that is highly relevant to the target demographic, enabling advertisers to quickly provide effective advertisements that reflect the needs of their target demographic.

[1077] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1078] System program processing flow

[1079] Step 1: Enter your ad text and target demographics

[1080] As an advertiser, the user inputs the ad text and target demographic attribute information into the system. For example, the ad text might be "This new foundation will make your skin glow!" and the target demographic attribute might be "women in their 20s." This sends the user's input to the system.

[1081] Input: Ad text, target demographic attributes

[1082] Output: Input data sent to the terminal

[1083] Step 2: Submitting the entered data

[1084] The terminal receives user input data in real time and sends it to the server via the network as a POST request, where the data is packaged in JSON format.

[1085] Input: Ad text received from the user and target demographic attributes

[1086] Output: JSON data sent to the server

[1087] Step 3: Analyze your target audience

[1088] Based on the target demographic information received by the server, a specific analytical algorithm is used to analyze the characteristics and interests of the target demographic, using appropriate past advertising data and demographic information from a database.

[1089] Input: Target demographic attributes, past advertising data, demographic information

[1090] Output: Analysis of the characteristics and interests of the target audience

[1091] Step 4: Generate feedback comments

[1092] Based on the analysis results, the server uses a natural language generation algorithm (e.g., a generative AI model) to generate representative feedback comments from the target demographic.

[1093] Input: Analysis results, prompt text (e.g., "Please generate feedback comments about the foundation that women in their 20s want.")

[1094] Output: Generated feedback comments

[1095] Step 5: Review feedback comments

[1096] The server reviews the generated feedback comments and extracts commonalities and key feedback points using clustering techniques (e.g., K-means clustering).

[1097] Input: Generated feedback comments

[1098] Output: Analysis results including commonalities and key points

[1099] Step 6: Generate correction instructions for the ad text

[1100] The server generates suggested revisions to the ad text based on the review results. Specifically, it creates a suggested revision: "This foundation has a natural finish and is gentle enough for daily use!"

[1101] Input: Review results, ad text

[1102] Output: Ad text revision suggestions

[1103] Step 7: Notification of proposed amendments

[1104] The server notifies the user of the generated correction proposal via the terminal, which receives the data from the server and displays it on the user interface (UI).

[1105] Input: Ad text revision suggestions

[1106] Output: Correction proposals notified to the user

[1107] Step 8: Final review and confirmation

[1108] The user reviews the proposed corrections and confirms the final ad text on the system UI. When the user clicks the confirm button, the device resends the information to the server.

[1109] Input: Proposed amendment, final user review

[1110] Output: Finalized ad text

[1111] Step 9: Save your final ad text

[1112] The device sends the finalized ad text to the server, which stores this information in a database so the system can reference the final text later.

[1113] Input: Confirmed ad text

[1114] Output: Ad text stored on the server

[1115] The above is the specific flow of each processing step of the system.

[1116] (Application example 1)

[1117] 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."

[1118] Conventional methods for creating ad texts have had the problem of making it difficult to efficiently reflect the needs and feedback of the target audience, resulting in reduced advertising effectiveness. Furthermore, the ad creation process was cumbersome, as advertisers needed a high level of expertise and time to accurately grasp the characteristics of their target audience and generate and edit appropriate ad text.

[1119] 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.

[1120] In this invention, the server includes means for inputting advertising text, means for inputting attribute information of a target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for finalizing the advertising text, means for inputting the advertising text and attribute information of the target demographic via a smart device, and means for saving the generated advertising text, thereby enabling advertisers to quickly and easily generate and optimize advertising text that is more effective for the target demographic.

[1121] - "Advertising text" means any text or message used to promote a product or service.

[1122] "Target demographic information" is data that describes the characteristics, such as age, gender, and interests, of a specific group of people who will receive an advertisement.

[1123] "Representative feedback comments from the target audience" are sentences that show the target audience's typical reactions and opinions to a particular advertisement.

[1124] "Analysis of feedback comments" refers to the process of analyzing the content of the generated comments and extracting commonalities and key points.

[1125] "Correcting the ad text" means modifying the content of the ad text based on feedback comments to make it more relevant to the target audience.

[1126] "Determining the final advertising text" refers to the process of finally determining the content of the corrected advertising text.

[1127] "Smart devices" are devices such as mobile phones and eyeglass-type information terminals connected to the Internet that advertisers use to input, confirm, and correct data.

[1128] A "natural language generation algorithm" is a computational method or model for generating language that humans naturally use.

[1129] A "database" is a system that stores large amounts of data in an organized manner and allows quick access to specific information.

[1130] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. This system inputs advertising text and attribute information of the target demographic, generates and analyzes feedback comments from the target demographic based on that information, and corrects the advertising text. Specific embodiments of the system are described below.

[1131] First, a user acts as an advertiser and uses a smart device (smartphone or smart glasses) to input the ad text and target demographic information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic.

[1132] The device then receives this input information and sends it to a server. The server then analyzes the characteristics and interests of the target audience based on their attribute information. This analysis utilizes past advertising data, demographic information, and other related databases. The analysis is performed using a natural language generation algorithm (such as GPT-3).

[1133] Based on the analysis results, the server uses a generative AI model to generate representative feedback comments from the target demographic, including specific feedback such as "I want to use products that are gentle on my skin" or "I want a foundation that I can use every day."

[1134] The server reviews the feedback comments and extracts commonalities and key feedback points. For example, if there are many comments about "protecting the skin" and "wanting to use it every day," it will determine that these are common needs.

[1135] Based on the review results, the server generates instructions to correct the advertisement text, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[1136] The user reviews the correction suggestions and finalizes the ad text, which might read, "This foundation has a natural finish and is gentle enough for everyday use!"

[1137] Finally, the final ad text is sent to the server and stored in a database. This process allows for the creation of highly relevant ads that do not feel out of place in the target audience.

[1138] Computer systems and their operation

[1139] The system consists of several important hardware and software elements.

[1140] Hardware used:

[1141] Smartphone

[1142] Smart Glasses

[1143] Software used:

[1144] Server side: Flask or Django

[1145] Generative AI model: GPT-3

[1146] Data transmission: Requests

[1147] Examples of specific examples and prompts

[1148] As a specific example, an advertiser uses a smart device to enter the following information into the app:

[1149] Ad text: "New foundation makes your skin glow!"

[1150] Target demographic: Women in their 20s

[1151] The server receives the data, analyzes it, and uses an AI model to generate feedback comments and provide optimized ad text, such as:

[1152] Optimized ad text: "A foundation that gives a natural finish and is gentle enough for everyday use!"

[1153] An example of an input prompt for the generative AI model is as follows:

[1154] prompt:

[1155] Target demographics:

[1156] Age: 20s

[1157] Gender: Female

[1158] Ad text:

[1159] "Your new foundation will make your skin glow!"

[1160] Generate feedback comments for this target audience.

[1161] The generative AI model generates feedback comments like the following:

[1162] "I want a foundation that I can use every day."

[1163] It's important to use ingredients that are gentle on the skin.

[1164] Emphasis on a natural finish

[1165] Based on this feedback, the server generates optimized ad text and provides it to the user.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The user uses a smart device to input the ad text and demographic information for the target demographic. The input data is "Our new foundation will make your skin glow!" (ad text) and "Women in their 20s" (target demographic). This data is saved on the device and then sent to the server.

[1169] Step 2:

[1170] The device sends the received ad text and target demographic attribute information to the server. The input data is sent in JSON format, and the server receives it and prepares it for analysis.

[1171] Step 3:

[1172] The server performs analysis based on the target demographic's attribute information. Specifically, it derives the target demographic's characteristics and interests using related databases such as past advertising data and demographic information. The input is the target demographic's attribute information, and the output is the analysis results.

[1173] Step 4:

[1174] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate representative feedback comments from the target demographic. The input is the analysis results, and the output is the generated feedback comments. This feedback might be something like, "I want to use products that are gentle on my skin" or "I want a foundation that I can wear every day."

[1175] Step 5:

[1176] The server reviews the feedback comments and extracts commonalities and key feedback points. Here, the generated feedback comments are analyzed to identify important keywords and common needs. The input is the generated feedback comments, and the output is the extracted feedback points.

[1177] Step 6:

[1178] The server generates instructions to correct the ad text based on the extracted feedback points. Specific correction suggestions include, "This foundation has a natural finish and is gentle enough for daily use!" The input is the extracted feedback points, and the output is the correction instructions.

[1179] Step 7:

[1180] The server sends the correction instructions to the terminal and notifies the user. The user reviews the correction proposal and modifies the final advertising text as necessary. The input is the correction instructions, and the output is the final advertising text reviewed by the user.

[1181] Step 8:

[1182] The user finalizes the final ad text, and the device sends the final text to the server. The final ad text might be something like, "This foundation has a natural finish and is gentle enough for everyday use!"

[1183] Step 9:

[1184] The server stores the finalized ad text in a database. The input is the finalized ad text, and the output is the stored ad text. This ensures that the most appropriate ad is delivered to the target audience.

[1185] 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.

[1186] The system of the present invention optimizes advertising text for a target demographic and improves advertising effectiveness. In particular, the present invention combines an emotion engine that recognizes user emotions, enabling more accurate generation and correction of advertising text. Specific system embodiments are described below.

[1187] First, the user acts as an advertiser and inputs the ad text and target demographic attribute information. For example, the user might input the text "This new foundation will make your skin glow!" and "women in their 20s" as the target demographic attribute.

[1188] The device then receives this input information and sends it to a server, which analyzes the characteristics and interests of the target audience based on their demographic information, using historical advertising data, demographic information, and other relevant databases.

[1189] The server then uses a natural language generation algorithm based on the analysis results to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[1190] The server generates instructions to revise the ad text based on the generated feedback comments. The emotion engine analyzes the emotional tone of the comments and reflects the results in the revision. For example, the server generates a revision suggestion such as "This foundation has a natural finish and is gentle on the skin even when used daily," and notifies the user via their device.

[1191] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text could be, for example, "This foundation has a natural finish and is gentle enough for daily use."

[1192] The final ad text, once confirmed by the device, is sent to the server and stored in a database. This series of processes makes it possible to generate ads that are highly compatible with the target demographic and do not feel out of place. The introduction of an emotion engine also makes it possible to more precisely express emotions in the ad text, resulting in more effective ad delivery.

[1193] In this way, the system of the present invention aims to reduce the sense of discomfort caused by differences in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[1194] The processing flow will be explained below.

[1195] Step 1:

[1196] The user inputs the ad text and demographic information for the target audience. For example, the ad text is "New foundation will make your skin glow!" and the target demographic is "Women in their 20s."

[1197] Step 2:

[1198] The device receives the entered information and sends it to the server. Specifically, it transfers the advertising text and target demographic information entered in the input form to the server.

[1199] Step 3:

[1200] The server performs an analysis based on the target demographic attribute information received. It references past advertising data and demographic information to identify the characteristics and general interests of the target demographic. This analysis provides a detailed understanding of the target demographic profile.

[1201] Step 4:

[1202] The server uses a natural language generation algorithm to generate representative feedback comments based on the target demographic, while simultaneously utilizing an emotion engine to analyze the emotional tone of the generated comments. For example, for a comment such as "I want to use skin-friendly products," the server identifies emotional tones such as "reassurance" and "trust."

[1203] Step 5:

[1204] The server reviews the feedback comments analyzed by the sentiment engine and extracts common themes and key needs. For example, if many comments contain feedback such as "protects skin" or "can be used daily," this will be recognized as a key need.

[1205] Step 6:

[1206] The server generates instructions to correct the advertising text based on the analysis of the feedback comments and their emotional tone. Specifically, it creates a suggested correction, such as "This foundation has a natural finish and is gentle on the skin even when used daily!", and notifies the user via their device.

[1207] Step 7:

[1208] The device displays the proposed corrections to the user, who then reviews them. The analysis results of the emotion engine are also displayed, allowing the user to understand the emotions of the target audience before reviewing the text.

[1209] Step 8:

[1210] The user reviews the proposed revisions and finalizes the ad text. Specifically, the user confirms and confirms the final ad text: "This foundation has a natural finish and is gentle on the skin, even when used daily!"

[1211] Step 9:

[1212] The device sends the final ad text to the server, which stores it in a database for use in subsequent ad campaigns.

[1213] Through this series of steps, the system of the present invention generates advertising text that matches the emotions and needs of the target audience, improving advertising effectiveness. By introducing an emotion engine, it is possible to capture the emotional tone of the target audience and create advertisements that are more relatable.

[1214] Example 2

[1215] 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."

[1216] While conventional ad text generation systems perform analysis based on the target demographic's attribute information, they do not perform precise adjustments based on emotions, making it difficult to generate ad text that is optimized for the target demographic and has a high affinity with the target demographic. Furthermore, after the final ad text is finalized, it is not properly saved or managed, making subsequent analysis and revision difficult.

[1217] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting advertising text, a means for inputting attribute information of the target demographic, a means for analyzing based on the attribute information of the target demographic, a means for generating representative feedback comments from the target demographic, a means for correcting the advertising text based on the analysis results of the generated feedback comments, a means for finalizing the advertising text, a means for analyzing the emotional tone of the generated feedback comments and correcting the advertising text including the analyzed emotional tone, and a means for saving the finalized advertising text. This makes it possible to generate sophisticated advertising text that takes into consideration the emotions of the target demographic and to appropriately save and manage it.

[1218] "Advertising text" is a sentence that expresses the message that the advertiser wants to convey to the target audience.

[1219] "Target demographic" refers to people with specific attributes (age, gender, interests, etc.) that are targeted by the ad text.

[1220] "Attribute information" is detailed data about the specific attributes of the target demographic (age, gender, interests, etc.).

[1221] "Feedback comments" are sentences that express opinions and impressions received from the target demographic.

[1222] "Natural language generation algorithms" refer to computational techniques and methods for generating natural language that humans can understand.

[1223] "Emotional tone" is information that expresses emotions (such as joy, sadness, or anger) detected from text.

[1224] "Analysis" is the process of examining input data in detail and extracting hidden patterns and information.

[1225] "Correction" means modifying the entered advertising text to make it optimal for the target audience.

[1226] "Final" means that the final ad text has been decided and will not be revised further.

[1227] "Storage" means keeping the confirmed advertising text in a database or the like for subsequent use or analysis.

[1228] The system of the present invention aims to generate advanced advertising text and improve its effectiveness, and by combining it with an emotion engine in particular, it is possible to generate advertising text that has a higher affinity with the target demographic.

[1229] First, the user acts as an advertiser and inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device and sent to the server.

[1230] Next, the server performs an analysis based on the target demographic attribute information received. This analysis utilizes past advertising data, demographic information, and other related databases. As a specific example, the analysis targets past advertising data related to "beauty and health" for the target demographic of "women in their 20s."

[1231] Based on the analysis results, the server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. The emotion engine is also used to analyze the emotional tone of the generated comments. For example, the emotion engine can detect emotional tones such as "joy" and "satisfaction" in the comment, "This foundation is so great!"

[1232] The server generates instructions to amend the ad text based on the generated feedback comments. At this time, the emotional tone detected by the emotion engine is also reflected in the amendments. For example, a suggested amendment might be, "This foundation has a natural finish and is gentle on the skin even when used daily." The proposed amendments are notified to the user via their device.

[1233] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. The final ad text might be, for example, "This foundation has a natural finish and is gentle on the skin even when used daily." The final ad text is sent from the device to the server and stored in a database.

[1234] This series of processes makes it possible to generate advertisements that are highly relevant to the target audience and do not feel out of place. In addition, the introduction of an emotion engine allows for more refined emotional expression in the ad text, making it possible to deliver effective advertisements.

[1235] A specific example of a prompt is as follows:

[1236] User: Enter the ad text "New foundation will make your skin glow!" and the target demographic "Women in their 20s."

[1237] Server: Now that the ad text and demographic information has been received, analysis begins.

[1238] Server: Using the emotion engine, the following feedback comment was generated:

[1239] "This foundation is amazing!" (Emotional tone: joy, satisfaction)

[1240] Server: We suggest you revise your ad text as follows:

[1241] "This foundation has a natural finish and is gentle on the skin even when used daily."

[1242] Device: Suggested fix sent to user.

[1243] User: Review the proposed changes and confirm the final ad text: "This foundation has a natural finish and is gentle enough for everyday use."

[1244] Device: The final ad text was sent to the server and stored in the database.

[1245] As described above, the system of the present invention aims to reduce the sense of incongruity caused by the difference in attributes between the advertiser and the target demographic, and to improve advertising effectiveness.

[1246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1247] Step 1:

[1248] The user inputs the ad text and demographic information of the target demographic. For example, the user inputs the ad text "Our new foundation will make your skin glow!" and the demographic information of the target demographic "Women in their 20s." This input information is received by the user's device. The input data is the ad text and the demographic information of the target demographic.

[1249] Step 2:

[1250] The device sends the received input information to the server. The data sent includes the ad text and demographic information of the target audience.

[1251] Step 3:

[1252] The server analyzes the received information. For the analysis, it uses past advertising data, demographic information, and other related databases. For example, it uses past advertising data aimed at women in their 20s to analyze the characteristics of the target demographic. The input data is the received advertising text and attribute information of the target demographic, and the output data is the analysis results of the target demographic's characteristics and interests.

[1253] Step 4:

[1254] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic. It also utilizes an emotion engine to analyze the emotional tone of the generated comments. For example, it generates a feedback comment such as "This foundation is truly amazing!" and detects "joy" or "satisfaction" as its emotional tone. The input data is the analysis result, and the output data is the feedback comment and its emotional tone.

[1255] Step 5:

[1256] The server generates instructions for correcting the advertising text based on the generated feedback comments. The corrections also include the emotional tone detected by the emotion engine. For example, a correction suggestion may be generated such as "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the feedback comments and their emotional tone, and the output data are correction suggestions for the advertising text.

[1257] Step 6:

[1258] The terminal notifies the user of the proposed corrections. The notified data is the proposed corrections to the advertisement text.

[1259] Step 7:

[1260] The user reviews the proposed revisions and finalizes the ad text based on the final feedback from the emotion engine. For example, the final ad text may be "This foundation has a natural finish and is gentle on the skin even for daily use." The input data are the proposed revisions to the ad text, and the output data is the final ad text.

[1261] Step 8:

[1262] The device sends the final ad text to the server, which stores it in a database. The data stored is the final ad text. This process generates a refined ad text that is stored in a format that is useful for future use and analysis.

[1263] Through the above processing steps, it becomes possible to generate and manage effective advertising texts that are optimized for the target demographic.

[1264] (Application example 2)

[1265] 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."

[1266] With conventional advertising systems, it was difficult to generate effective ad text for the target demographic, making it difficult to improve advertising effectiveness.In addition, because it was not possible to adjust ad text to reflect user emotions and real-time reactions, ads that were perceived as unnatural by the target demographic were sometimes delivered.

[1267] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting advertising text, means for inputting attribute information of the target demographic, means for analyzing based on the attribute information of the target demographic, means for generating representative feedback comments from the target demographic, means for correcting the advertising text based on the analysis results of the generated feedback comments, means for determining the final advertising text, means for collecting user data, emotion analysis means for analyzing the user's facial expressions and tone of voice, means for optimizing the advertising text in real time based on the emotion analysis results, and means for displaying the corrected advertising text. This makes it possible to generate and display highly accurate advertising text that reflects the user's emotions.

[1268] The "means for inputting advertisement text" is a mechanism that provides an interface for the advertiser to input the content of the advertisement.

[1269] The "means for inputting attribute information of the target demographic" is a mechanism that provides an interface for inputting information about the specific user demographic targeted by the advertisement.

[1270] "Means for analyzing based on the attribute information of the target demographic" refers to a mechanism for analyzing the characteristics and interests of that demographic based on the target demographic's attribute information.

[1271] The "means for generating representative feedback comments from the target demographic" is a mechanism for automatically generating anticipated reactions from the target user demographic.

[1272] The "means for correcting the advertisement text based on the analysis results of the generated feedback comments" is a mechanism for correcting the advertisement content based on the analysis results of the generated feedback comments.

[1273] The "means for determining the final advertisement text" is a mechanism for finally determining the corrected advertisement text.

[1274] "Means for collecting user data" refers to a mechanism for collecting data from users (facial expressions, voice, etc.).

[1275] The "emotion analysis means for analyzing the user's facial expressions and voice tones" is a mechanism for analyzing emotions from the collected user's facial expressions and voice.

[1276] "Means for optimizing advertising text in real time based on the results of sentiment analysis" refers to a mechanism for optimizing advertising text on the spot based on the analyzed sentiment data.

[1277] The "means for displaying the corrected advertising text" is a device or interface for displaying the optimized advertising text to the user.

[1278] The present invention provides a system for displaying advertising text optimized for a specific demographic. This system optimizes text using a sentiment analysis engine and a natural language generation algorithm based on advertising text entered by an advertiser and attribute information of the target demographic.

[1279] 1. System Configuration

[1280] The system includes the following major components:

[1281] 1. How to enter ad text and attribute information:

[1282] Advertisers input their advertising text and target demographic information via their terminals, and this data is sent to the server.

[1283] 2. Analysis method:

[1284] The server then analyzes the target demographic information it receives, using past advertising data and demographic information.

[1285] 3. Feedback Comment Generation Method:

[1286] It uses natural language generation algorithms to generate representative feedback comments from your target audience, while a sentiment analysis engine analyzes the emotional tone of the comments.

[1287] 4. Ad text correction methods:

[1288] Based on the analysis of the generated feedback comments, the ad text is revised. The emotional tone of the feedback comments is reflected by the emotion engine.

[1289] 5. Final ad text confirmation method:

[1290] The advertiser reviews and confirms the revised ad text, which is then saved on the server.

[1291] 6. User Data Collection Methods:

[1292] Smart glasses and other devices are used to collect data such as a user's facial expressions and tone of voice.

[1293] 7. Emotion analysis means:

[1294] The collected user data is analyzed to understand the user's emotional tone.

[1295] 8. Text optimization methods:

[1296] Ad text is optimized in real time based on sentiment analysis results.

[1297] 9. Advertising display means:

[1298] The optimized ad text is displayed to the user through a device such as smart glasses.

[1299] 2. Program Processing Overview

[1300] The server first receives the advertisement text and target demographic attribute information entered by the advertiser. Then, it uses an analysis means to analyze the characteristics and interests of the target demographic and generates feedback comments. The emotional tone of the feedback comments is analyzed by an emotion analysis engine, and the advertisement text is corrected based on the results.

[1301] In real time, emotion analysis is performed from the user's facial expressions and voice tones collected using a user data collection means, and the advertisement text is optimized based on the emotion analysis results. The optimized advertisement text is then displayed to the user through a device such as smart glasses.

[1302] 3. Specific Examples

[1303] For example, use the following prompt:

[1304] Prompt Sentence Examples

[1305] User Data: "Expression: Smiling, Tone of Voice: Excited"

[1306] Ad text: "Try out your new smartwatch!"

[1307] Target demographic: "Men in their 30s"

[1308] In this example, if the user is smiling and has an excited voice, the ad text will be optimized to match the user's emotions, for example, "This smartwatch will make your daily life more fun!"

[1309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1310] Step 1:

[1311] The terminal receives advertising text and target demographic attribute information from the advertiser and sends the data to the server. The terminal receives advertising text and target demographic attribute information as input and processes the data before sending it to the server.

[1312] Step 2:

[1313] The server analyzes the target demographic attribute information it receives. Using the target demographic attribute information, past advertising data, and demographic information as input, it performs data calculations to extract the characteristics and interests of the target demographic.

[1314] Step 3:

[1315] The server uses a natural language generation algorithm to generate representative feedback comments from the target demographic based on the analysis results, and processes the data to generate the feedback comments using the analysis results as input.

[1316] Step 4:

[1317] The server analyzes the emotional tone of the generated feedback comments using an emotion engine, and performs a data operation using the feedback comments as input to analyze the emotional tone.

[1318] Step 5:

[1319] The server corrects the advertisement text based on the sentiment analysis result, using the emotional tone of the feedback comments as input and performing correction operations on the advertisement text.

[1320] Step 6:

[1321] The terminal notifies the advertiser of the proposed amendments, and the advertiser finalizes the ad text. The proposed amendments are used as input to perform data processing to finalize the ad text.

[1322] Step 7:

[1323] The server stores the final ad text in the database. Using the final ad text as input, it processes the data to store in the database.

[1324] Step 8:

[1325] Data on facial expressions and voice tones are collected by the user using smart glasses. Data calculations are performed using the collected user facial expressions and voice data as input.

[1326] Step 9:

[1327] The server analyzes the collected user data to identify the user's emotional tone. Using the user's facial expression and voice data as input, it performs data calculations to analyze the emotional tone.

[1328] Step 10:

[1329] The server optimizes the ad text in real time based on the sentiment analysis results. Using the sentiment analysis results as input, it processes the data to optimize the ad text.

[1330] Step 11:

[1331] The optimized advertisement text is displayed to the user through the smart glasses. Using the optimized advertisement text as input, a data calculation is performed to display the data to the user.

[1332] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1333] 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.

[1334] 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 robot 414.

[1335] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1336] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1337] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1338] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1339] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1340] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1341] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1342] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1343] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1344] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1345] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1346] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1347] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1348] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1349] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1350] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1351] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1352] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1353] The following is further disclosed regarding the above embodiment.

[1354] (Claim 1)

[1355] a means for inputting advertising text;

[1356] A means for inputting attribute information of the target demographic;

[1357] A means of analysis based on the attribute information of the target demographic,

[1358] a means for generating representative feedback comments from a target demographic;

[1359] A means for correcting the advertisement text based on the analysis result of the generated feedback comments;

[1360] A means to finalize the ad text;

[1361] A system including:

[1362] (Claim 2)

[1363] 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

[1364] (Claim 3)

[1365] 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm.

[1366] "Example 1"

[1367] (Claim 1)

[1368] a means for inputting advertising text;

[1369] A means for inputting attribute information of the target demographic;

[1370] A means of analysis based on the attribute information of the target demographic,

[1371] a means for generating representative feedback comments from a target demographic;

[1372] A means for correcting the advertisement text based on the analysis result of the generated feedback comments;

[1373] A means to finalize the ad text;

[1374] a means of providing notice of proposed amendments;

[1375] a means for storing the advertisement text in a database;

[1376] A system including:

[1377] (Claim 2)

[1378] 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

[1379] (Claim 3)

[1380] 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm.

[1381] "Application Example 1"

[1382] (Claim 1)

[1383] a means for inputting advertising text;

[1384] A means for inputting attribute information of the target demographic;

[1385] A means of analysis based on the attribute information of the target demographic,

[1386] a means for generating representative feedback comments from a target demographic;

[1387] A means for correcting the advertisement text based on the analysis result of the generated feedback comments;

[1388] A means to finalize the ad text;

[1389] A means for inputting advertising text and target demographic attribute information via a smart device;

[1390] a means for storing the generated advertising text;

[1391] A system including:

[1392] (Claim 2)

[1393] 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

[1394] (Claim 3)

[1395] 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm.

[1396] "Example 2: Combining Emotion Engines"

[1397] (Claim 1)

[1398] a means for inputting advertising text;

[1399] A means for inputting attribute information of the target demographic;

[1400] A means of analysis based on the attribute information of the target demographic,

[1401] a means for generating representative feedback comments from a target demographic;

[1402] A means for correcting the advertisement text based on the analysis result of the generated feedback comments;

[1403] A means to finalize the ad text;

[1404] means for analyzing the emotional tone of the generated feedback comments and amending the advertising text accordingly;

[1405] a means for storing the finalized ad text;

[1406] A system including:

[1407] (Claim 2)

[1408] 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

[1409] (Claim 3)

[1410] 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm.

[1411] "Application example 2 when combining emotion engines"

[1412] (Claim 1)

[1413] a means for inputting advertising text;

[1414] A means for inputting attribute information of the target demographic;

[1415] A means of analysis based on the attribute information of the target demographic,

[1416] a means for generating representative feedback comments from a target demographic;

[1417] A means for correcting the advertisement text based on the analysis result of the generated feedback comments;

[1418] A means to finalize the ad text;

[1419] a means for collecting user data;

[1420] emotion analysis means for analyzing a user's facial expression and tone of voice;

[1421] A means to optimize ad text in real time based on sentiment analysis results,

[1422] means for displaying the corrected advertising text;

[1423] A system including:

[1424] (Claim 2)

[1425] 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

[1426] (Claim 3)

[1427] 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm. [Explanation of symbols]

[1428] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting advertising text; A means for inputting attribute information of the target demographic; A means of analysis based on the attribute information of the target demographic, a means for generating representative feedback comments from a target demographic; A means for correcting the advertisement text based on the analysis result of the generated feedback comments; A means to finalize the ad text; A system including:

2. 2. The system according to claim 1, wherein the advertising text input means receives advertising text input by an advertiser.

3. 2. The system of claim 1, wherein the means for generating feedback comments representative of the target demographic generates the comments using a natural language generation algorithm.

Images