Support devices, support programs, support methods

Abstract

This invention provides a support device, support program, and support method for creating effective advertisements. [Solution] In an evaluation system in which a server device connects to a user terminal via a network, the server device 1, which is a support device for generating advertising information, comprises: a material information acquisition unit that acquires material information including at least material images and property information of the offerings that are the target of the advertising information; an advertising information generation unit that inputs the result information of analyzing competitive information, which is information on similar offerings that are similar to the offerings, obtained based on the material information, and an advertising information generation prompt generated based on the material information into a generation model, and generates advertising information based on the output information output from the generation model.

Description

Technical Field

[0001] The present invention relates to a support device, a support program, and a support method.

Background Art

[0002] Techniques for creating effective advertisements have been disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Patent Document 1 discloses generating content with a predetermined part other than the sales target changed in order to attract the user's interest according to the user's actions in a web advertisement. However, with this technique, it is not possible to generate a sufficiently effective advertisement.

[0005] The present invention has been made in view of such a background, and an object thereof is to support the creation of an effective advertisement.

Means for Solving the Problems

[0006] To solve the above problems, a support device for supporting the generation of advertisement information in the present disclosure includes a material information acquisition unit that acquires material information including at least a material image and property information of an offering targeted by the advertisement information, result information obtained by analyzing competition information, which is information on similar offerings similar to the offering, based on the material information, and an advertisement information generation unit that inputs an advertisement information generation prompt generated based on the material information into a generation model and generates the advertisement information based on output information output from the generation model.

[0007] Further issues and solutions disclosed in this application will be made clear in the section on embodiments of the invention and in the drawings. [Effects of the Invention]

[0008] According to the present invention, it is possible to support the creation of effective advertisements. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the overall configuration of an evaluation system according to one embodiment of the present invention. [Figure 2] This figure shows an example of the hardware configuration of the server device 1 according to the same embodiment. [Figure 3] This figure shows an example of the functional configuration of the server device 1 according to the same embodiment. [Figure 4] This figure shows an example of material information stored in the material information storage unit 131 according to the same embodiment. [Figure 5] This figure shows an example of advertising information stored in the advertising rule information storage unit 134 according to the same embodiment. [Figure 6] This figure shows an example of processing by the server device 1 according to the same embodiment. [Modes for carrying out the invention]

[0010] <Summary of the Invention> The embodiments of the present invention will be described by listing them. The present invention has, for example, the following configuration. [Item 1] A support device for generating advertising information, A material information acquisition unit that acquires material information including at least material images and property information of the offerings that are the subject of the advertising information, An advertising information generation unit inputs the results of analyzing competitive information, which is information on similar offerings to the aforementioned offering, obtained based on the aforementioned material information, and an advertising information generation prompt generated based on the aforementioned material information into a generation model, and generates the advertising information based on the output information output from the generation model. An assistance device comprising [Item 2] Input a prompt for generating user reviews, which is generated based on at least the first advertisement information, into the generation model, and based on the output information output from the generation model, generate a user review for the offering when using the advertisement information and an evaluation related to the user review. A user review information generation unit Further comprising The advertisement information generation unit regenerates the second advertisement information based on the second prompt for generating advertisement information, which is generated by modifying the first prompt for generating advertisement information used when generating the first advertisement information until the evaluation related to the user review becomes a predetermined evaluation. The assistance device according to Item 1. [Item 3] The result information includes a plurality of information on concepts related to advertisements. The advertisement information generation unit regenerates the advertisement information by modifying the concept information included in the prompt for generating advertisement information until the evaluation related to the user review becomes a predetermined evaluation. The assistance device according to Item 2. [Item 4] The advertisement information includes information on the type of medium. The prompt for generating advertisement information includes the information on the type of medium. The advertisement information generation unit generates advertisement information suitable for the type of medium. The assistance device according to Item 1 or 2. [Item 5] An advertisement rule evaluation unit that evaluates whether the advertisement information conforms to advertisement rules. Further comprising When the advertisement information does not conform to the advertisement rules, the advertisement information generation unit regenerates the advertisement information so that it conforms to the advertisement rules. The assistance device according to Item 1 or 2. [Item 6] An assistance program for assisting in the generation of advertisement information, On a computer, A material information acquisition step of acquiring material information including at least a material image and property information of an offering targeted by the advertisement information, Based on the material information, input the result information obtained by analyzing competitive information, which is information on similar offerings similar to the offering, and a prompt for generating advertisement information generated based on the material information into a generation model, and based on the output information output from the generation model, An advertisement information generation step of generating the advertisement information, An assistance program for causing the above to be executed. [Item 7] An assistance method for assisting in generating advertisement information, where a computer A material information acquisition step of acquiring material information including at least a material image and property information of an offering targeted by the advertisement information, Based on the material information, input the result information obtained by analyzing competitive information, which is information on similar offerings similar to the offering, and a prompt for generating advertisement information generated based on the material information into a generation model, and based on the output information output from the generation model, An advertisement information generation step of generating the advertisement information, An assistance method for executing the above.

[0011] <Details of the Embodiment> Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the present specification and drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant description is omitted.

[0012] ==Overview== FIG. 1 is a diagram showing the overall configuration of an evaluation system. As shown in FIG. 1, the evaluation system includes a server device 1 and a user terminal 3. The server device 1 is connected to the user terminal 3 via a network 2. Although only one server device 1 and one user terminal 3 are shown, it is needless to say that more may exist.

[0013] ==Server Device 1== Server device 1 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0014] ==User Terminal 3== User terminal 3 is a computer used by the user who creates the advertisement. User terminal 3 can be, for example, a smartphone, tablet computer, or personal computer. The user can access server device 1, for example, through an application or web browser running on user terminal 3.

[0015] Figure 2 shows an example of the hardware configuration of server device 1. Note that the illustrated configuration is just one example, and other configurations are also possible. Server device 1 includes a CPU 101, memory 102, storage device 103, communication interface 104, input device 105, and output device 106. The storage device 103 stores various data and programs, such as a hard disk drive, solid-state drive, or flash memory. The communication interface 104 is an interface for connecting to the communication network 2, such as an adapter for connecting to Ethernet®, a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or RS232C connector for serial communication. The input device 105 is for inputting data, such as a keyboard, mouse, touch panel, button, or microphone. The output device 106 is for outputting data, such as a display, printer, or speaker. Furthermore, each functional unit of the server device 1, as described later, is realized by the CPU 101 reading a program stored in the storage device 103 into the memory 102 and executing it, and each storage unit of the server device 1 is realized as part of the storage area provided by the memory 102 and the storage device 103.

[0016] Embodiments of the present invention will be described in detail below with reference to the drawings.

[0017] Figure 3 shows an example of the functional configuration of the server device 1 according to this embodiment. The server device 1 includes processing units: a material information acquisition unit 111, an advertising information generation unit 112, a user review information generation unit 113, an advertising rule evaluation unit 114, a presentation unit 115, and a translation unit 116; and storage units: a material information storage unit 131, an advertising information storage unit 132, a user review information storage unit 133, a persona information storage unit 134, and an advertising rule storage unit 135.

[0018] The following describes each memory unit.

[0019] The material information storage unit 131 stores the material information of the advertisement, as shown in Figure 4, which has been acquired by the material information acquisition unit 111. As shown in Figure 4, the material information includes at least material images and property information relating to the offering. The material images are, for example, photographs or illustrations of the offering. The property information includes, for example, the product name, main functions, features, advantages, product category, target market, catchphrase, tagline, description, and image text information obtained by reading the characters contained in the image information used in the advertisement, as well as other text information used in the advertisement.

[0020] The advertising information storage unit 132 stores the advertising information generated by the advertising information generation unit 112.

[0021] The user review information storage unit 133 stores the user review information generated by the user review information generation unit 113.

[0022] The persona information storage unit 134 stores persona information used by the user review information generation unit 113 when generating user reviews. Persona information includes the content and results of surveys actually conducted on target individuals belonging to a certain category. Specifically, the persona information stored in the persona information storage unit 134 stores statistical data such as evaluation trends, purchasing behavior patterns, and values ​​for each attribute such as age group, gender, and occupation. For example, it may include the distribution of evaluations for a specific product category among men in their 20s, such as 20% being "very satisfied," 30% being "satisfied," and 50% being "average," as well as patterns of specific evaluation comments.

[0023] The advertising rule memory unit 135 stores rules related to advertising. As shown in Figure 5 as an example, the advertising rule information is linked to the target field of the advertisement or the target product of the advertisement and includes information such as relevant laws and regulations, regulations of industry associations, internal rules and guidelines of users, how readers feel (sentimental evaluation / marketing elements), and information such as prohibited expressions and expressions that should be avoided.

[0024] The following describes each processing unit.

[0025] The material information acquisition unit 111 has the function of acquiring material information of the offerings that are the subject of the advertisement. The material information acquisition unit 111 acquires material information, for example, by receiving material information from the user terminal 3 via the network 2. The material information acquisition unit 111 stores the acquired material information in the material information storage unit 131.

[0026] The advertising information generation unit 112 has the function of generating advertising information based on material information. Based on the material information, the advertising information generation unit 112 generates competitive information, which is information on similar offerings to the offering, and result information from analyzing the competitive information. The result information includes, for example, information on the advertising concept (style, taste, copy, etc.), conversions, and the contribution of the advertising to sales.

[0027] The advertising information generation unit 112 generates an advertising information generation prompt based on the material information. The advertising information generation unit 112 generates the advertising information generation prompt in the format, for example, "Please create advertising information for [product name]. The main features are [features], the target market is [target market], and the concept is [concept]." The contents within the brackets [] may be changed as appropriate, and other content may also be included.

[0028] The advertising information generation unit 112 inputs result information and advertising information generation prompts to the generation model and generates advertising information based on the output information output from the generation model. The generation model is, for example, a large-scale language model (LLM) or an image generation AI. The advertising information includes information such as images (including video), text (copy, keywords, taglines, hashtags, etc.), and effective media. The advertising information generation unit 112 stores the generated advertising information in the advertising information storage unit 132.

[0029] The advertising information generation unit 112 can be implemented using multiple methods. For example, one method uses a rule-based approach to generate advertising information based on predefined rules, while another uses a machine learning model to learn from data of past successful advertising campaigns and generate new advertising information.

[0030] The user review information generation unit 113 has the function of generating user review information based on the advertising information generated by the advertising information generation unit 112. The user review information generation unit 113 generates a prompt for generating user review information based on at least the first advertising information. The user review information generation unit 113 generates the prompt for generating user review information in a format such as, for example, "Please write your impressions of the advertisement for [product name] from the perspective of a typical consumer in the [target market]," and the contents in [] may be changed as appropriate, and other content may also be included.

[0031] The user review information generation unit 113 inputs a user review information generation prompt to the generation model and generates user review information for the offerings when advertising information is used, along with an evaluation related to that user review information, based on the output information output from the generation model. The user review information is modeled after user reviews of offerings written by users on e-commerce sites, etc., and is generated in the form of text and evaluation values. The evaluation related to the user review information may be, for example, a user's evaluation points or an evaluation of the degree of positive or negative based on text analysis of the user review information. The user review information generation unit 113 stores the generated user review information and its evaluation in the user review information storage unit 133.

[0032] The user review information generation unit 113 can be implemented in multiple ways. For example, one method uses a template-based approach to insert elements of advertising information into a pre-prepared user review template, or another uses natural language processing technology to extract features of advertising information and generate natural-sounding text based on them. In addition, to ensure diversity in the generated user reviews, the user review information generation unit 113 may have a function to prepare virtual user profiles with different personalities and backgrounds and generate user reviews based on each profile.

[0033] The user review information generation unit 113 may generate user reviews based on multiple virtual personas and generate user reviews of the persona group for advertising information. In this case, the user review information generation unit 113 may generate a persona group that includes multiple personas statistically adjusted based on actual consumer survey data, etc., to generate more realistic user reviews.

[0034] Specifically, the user review information generation unit 113 refers to the persona information storage unit 134 and acquires data from actual consumer surveys, etc. Based on this statistical data, the user review information generation unit 113 optimizes the input prompts to the generation model. In optimizing the prompts, the user review information generation unit 113 adjusts the number of personas to match the response distribution in actual consumer surveys and reflects evaluation points and expression methods characteristic of specific attribute groups in the prompts. In addition, the user review information generation unit 113 may also incorporate actual consumer behavior patterns, such as the context of use of products and services, purchase motivations, and comparison and evaluation processes, into the prompts.

[0035] Furthermore, the user review information generation unit 113 performs statistical adjustments to the generation model. It controls the generation process so that the generated user review group matches the distribution in actual consumer surveys. In addition, by learning typical evaluation patterns and expression methods for each attribute using actual consumer review data, it achieves the generation of more natural and persuasive review texts.

[0036] For example, when the user review information generation unit 113 generates user reviews for cosmetics aimed at men in their 20s, it performs the following processing: First, the user review information generation unit 113 obtains the evaluation distribution of cosmetics among men in their 20s from actual survey data. In this process, the user review information generation unit 113 refers to distribution data such as 40% of the evaluation being "good to use," 30% being "affordable," 20% being "effective," and 10% being "good packaging." Next, based on this distribution, the user review information generation unit 113 generates a number of personas corresponding to each evaluation axis. Subsequently, the user review information generation unit 113 sets the characteristics of each persona, such as occupation, lifestyle, and values, based on the actual survey data. Finally, for each set persona, the user review information generation unit 113 generates a natural review text that is appropriate to its characteristics.

[0037] The user reviews generated in this way are used for evaluation by the advertising rule evaluation unit 114 and for improving advertising information by the advertising information generation unit 112. Furthermore, by collaborating with the translation unit 116, they can also be used to predict consumer responses in different language regions. Through these processes, the user review information generation unit 113 achieves the generation of more realistic and reliable user reviews.

[0038] The advertising information generation unit 112 also has a function to regenerate advertising information until the evaluation related to user reviews reaches a predetermined evaluation. Specifically, the advertising information generation unit 112 generates a second advertising information generation prompt by modifying, adding to, deleting, or correcting the first advertising information generation prompt used when generating the first advertising information, and then regenerates the second advertising information based on this. The user review information generation unit 113 generates user review information for the second advertising information in the manner described above. This process is repeated until the evaluation related to user reviews reaches a predetermined evaluation (for example, 4 or higher on a 5-point scale).

[0039] Furthermore, the advertising information generation unit 112 can also regenerate advertising information by changing the concept information related to the advertisement included in the results information. For example, if the initial concept was "youthfulness and vitality" and the user review rating was low, it can be changed to a different concept such as "reliability and peace of mind" and regenerated. This approach makes it possible to find more effective advertising information.

[0040] Furthermore, the advertising information generation unit 112 also has the function of generating advertising information tailored to the medium according to the information on the type of medium included in the advertising information. For example, if the medium type is social media, it generates a short, catchy copy and a visually impactful image, and if it is a banner ad on a website, it generates a design that increases the click-through rate and a concise description, thereby generating optimal advertising information tailored to the characteristics of each medium.

[0041] The advertising rule evaluation unit 114 has the function of evaluating whether the generated advertising information complies with advertising rules. Advertising rules include, for example, laws, industry association guidelines, and internal company regulations, and are stored in advance in the advertising rule storage unit 135. The advertising rule evaluation unit 114 compares the generated advertising information with these advertising rules and determines whether or not it complies.

[0042] Based on the evaluation results of the advertising rule evaluation unit 114, the advertising information generation unit 112 regenerates the advertising information to comply with the advertising rules if the advertising information does not comply with the advertising rules. For example, if a particular expression is restricted, it generates new advertising information that avoids that expression.

[0043] The advertising rule evaluation unit 114 may have a function to analyze advertising text using natural language processing technology and detect prohibited or potentially misleading expressions. It may also have a function to analyze advertising images using image recognition technology and detect inappropriate content or elements that may infringe copyright. Furthermore, it may have a function to periodically update the advertising rule database to respond to updates to advertising rules.

[0044] The presentation unit 115 presents to the user terminal 3 at least one of the following: advertising information generated by the advertising information generation unit 112, user review information and its evaluation generated by the user review information generation unit 113, information evaluated by the advertising rule evaluation unit 114, and information translated or translated by the translation unit 116, which provides further details.

[0045] The translation unit 116 has the function of translating text information into different languages, for example. The translation unit 116 accepts the user's specification of the target language. The translation unit 116 performs multilingual processing of material information and advertising information, for example. The translation unit 116 can process the text information acquired by the material information acquisition unit 111 and the advertising information generated by the advertising information generation unit 112 in their pre-translation or post-translation states. As a result, the advertising rule evaluation unit 114 automatically applies advertising rules to the country or region where the target language is used, or the country or region where the advertising information is used, in order to apply advertising rules appropriate to regional characteristics. For example, when the translation unit 116 translates a Japanese advertisement into Korean, the advertising rule evaluation unit 114 should evaluate the advertisement content based on Korean advertising laws and guidelines. Furthermore, the translation unit 116 may optimize the translated expression according to the offerings and market characteristics of the products, services, or goods that are the subject of the advertising information. For example, in the case of an advertisement for a product aimed at young people, an expression suitable for young people in the target language area should be selected.

[0046] Each processing unit may perform the following processes in cooperation with the translation unit 116. The material information acquisition unit 111 passes the acquired material information to the translation unit 116 and performs translation processing as necessary. The advertising information generation unit 112 generates advertising information in the target language based on the translated material information. At this time, generation may be performed taking into account the market characteristics and cultural background of the target market. The user review information generation unit 113 generates user reviews for the translated advertising information in the target language. With this, the user review information generation unit 113 can predict the reaction in the local market based on the translated user reviews. The advertising rule evaluation unit 114 evaluates whether the translated advertising information complies with the advertising rules in the target region.

[0047] The translation unit 116 translates the information output by each processing unit into the language specified by the user. The translated information may be provided to the user terminal 3 by the presentation unit 115 along with the original information. This allows the user to efficiently consider advertising campaigns in multiple languages.

[0048] Using Figure 6, a typical processing flow of the server device 1 of this embodiment will be explained. The material information acquisition unit 111 acquires material information (1001). The advertisement information generation unit 112 generates advertisement information (1002). The user review information generation unit 113 generates user review information for the advertisement information and evaluates the user review information (1003). If the evaluation of the user review does not reach a predetermined level, the process returns to step 1002; if it reaches a predetermined level, the process proceeds to the next step (1004). The advertisement rule evaluation unit 114 evaluates whether the advertisement information complies with the advertisement rules (1005). If the advertisement information is evaluated as not complying with the advertisement rules, the process returns to step 1002; if it complies with the advertisement rules, the process proceeds to the next step. The translation unit 116 translates the information processed by each processing unit or the information that has been processed (1007). The presentation unit 115 presents the advertisement information (which may include the translated information) to the user terminal 3 (1008).

[0049] Other examples are shown below.

[0050] The user reviews generated by the user review information generation unit 113 can be used for various purposes other than optimizing advertising information. Typical examples of their use are described below.

[0051] During the product development phase, the user review information generation unit 113 can provide a virtual market evaluation of the product prototype. In this case, the user review information generation unit 113 acquires specification information of the prototype as raw material information and generates evaluations from each anticipated user group. The server device 1 may provide feedback on the generated evaluations to the product development team, or use them to identify areas for product improvement or to detect potential problems early.

[0052] The generated user reviews also provide useful insights in formulating pricing strategies. The user review information generation unit 113 generates anticipated user reviews for different price ranges based on actual market research data. In this process, the user review information generation unit 113 refers to statistical data on price sensitivity stored in the persona information storage unit 134 and generates user reviews that include acceptability and comparative evaluations with competing products in each price range. This enables the server device 1 to consider the optimal price range and predict the impact of price increases.

[0053] The generated user reviews can also be used to optimize sales channels. The user review information generation unit 113 generates user reviews about purchasing experiences across different sales channels, such as physical stores, e-commerce sites, specialty stores, and mass markets. These user reviews reflect customer behavior patterns and expectations in each channel, and the server device 1 may use them to formulate sales strategies for each channel.

[0054] The generated user reviews also play an important role in improving customer support quality. The user review information generation unit 113 generates user reviews, including evaluations of customer support responses, based on various anticipated customer interaction scenarios. This enables the server device 1 to pre-evaluate support quality and create reference cases for staff training.

[0055] Furthermore, generating user reviews is also effective when considering entry into new markets. The user review information generation unit 113 works in conjunction with the translation unit 116 to generate user reviews that take into account the values ​​and cultural background of local consumers in the market in which the company plans to enter. This enables the server device 1 to grasp important points in the localization of products and services and to formulate marketing strategies.

[0056] In these applications, the user review information generation unit 113 performs the following processes. First, the user review information generation unit 113 sets evaluation axes according to the purpose and obtains relevant consumer data from the persona information storage unit 134. Next, the user review information generation unit 113 analyzes the evaluation distribution based on the obtained data and constructs an appropriate group of personas. Subsequently, the user review information generation unit 113 generates user reviews according to the characteristics of each persona, taking into account time-series changes and regional differences as necessary. The generated reviews are scrutinized by the advertising rule evaluation unit 114, and corrections and adjustments are made as necessary.

[0057] The user reviews generated in this way can be used by server device 1 to support various decision-making processes throughout the entire product and service lifecycle. They are particularly useful in the pre-market launch evaluation phase, as they provide cost-effective insights.

[0058] While preferred embodiments of the present disclosure have been described in detail above with reference to the attached drawings, the technical scope of the present disclosure is not limited to such examples. It is clear to any person with ordinary skill in the art of the present disclosure that various modifications or alterations may be conceived within the scope of the technical idea set forth in the claims, and these will naturally also fall within the technical scope of the present disclosure.

[0059] The devices described herein may be implemented as a single device, or they may be implemented as a group of devices (e.g., cloud servers) that are partially or entirely connected by a network. For example, the CPU and storage device of server device 10 may be implemented by different servers that are connected to each other by a network.

[0060] The series of processes performed by the apparatus described herein may be implemented using software, hardware, or a combination of software and hardware. Computer programs for implementing each function of the server device 10 according to this embodiment can be created and implemented on a PC or the like. Furthermore, a computer-readable recording medium containing such a computer program can also be provided. Examples of recording media include magnetic disks, optical disks, magneto-optical disks, and flash memory. Alternatively, the computer program may be distributed without using a recording medium, for example, via a network.

[0061] Furthermore, the processes described herein do not necessarily have to be performed in the order described. Some processing steps may be performed in parallel. Additional processing steps may be employed, and some processing steps may be omitted.

[0062] Furthermore, the effects described herein are merely descriptive or illustrative and not limiting. In other words, the technology relating to this disclosure may produce other effects that will be apparent to those skilled in the art from the description herein, in addition to or in lieu of the effects described herein. [Explanation of Symbols]

[0063] 1 Server device 2 Network 3. User terminals 101 CPU 102 memory 103 Storage device 104 Communication Interface 105 Input device 106 Output device 111 Material Information Acquisition Unit 112 Advertising Information Generation Department 113 User Review Information Generation Unit 114 Advertising Rules Evaluation Department 115 Presentation section 131 Material Information Storage Unit 132 Advertising Information Storage Unit 133 User Review Information Storage Unit 134 Persona Information Memory Unit 135 Advertising Rule Memory Unit

Claims

1. A support device for generating advertising information, A material information acquisition unit that acquires material information including at least material images and property information of the offerings that are the subject of the advertising information, An advertising information generation unit inputs the results of analyzing competitive information, which is information on similar offerings to the aforementioned offering, obtained based on the aforementioned material information, and an advertising information generation prompt generated based on the aforementioned material information into a generation model, and generates the advertising information based on the output information output from the generation model. A support device equipped with the following features.

2. A user review generation prompt generated based on at least the first advertising information is input to the generation model, and a user review information generation unit generates a user review of the offering when the advertising information is used, and an evaluation related to the user review, based on the output information output from the generation model. Furthermore, The advertising information generation unit regenerates the second advertising information based on a second advertising information generation prompt generated by modifying the first advertising information generation prompt used when generating the first advertising information, until the evaluation related to the user review reaches a predetermined evaluation. The support device according to claim 1.

3. The aforementioned results information includes multiple pieces of information related to advertising concepts, The advertising information generation unit modifies the concept information included in the advertising information generation prompt and regenerates the advertising information until the evaluation related to the user review reaches a predetermined evaluation. The support device according to claim 2.

4. The aforementioned advertising information includes information on the type of media, The advertising information generation prompt includes information on the media type, The advertising information generation unit generates advertising information that matches the type of media. The support device according to claim 1 or 2.

5. An advertising rule evaluation unit that evaluates whether the aforementioned advertising information complies with advertising rules, Furthermore, The advertising information generation unit regenerates the advertising information to comply with the advertising rules if the advertising information does not comply with the advertising rules. The support device according to claim 1 or 2.

6. A support program that assists in the generation of advertising information, On the computer, A material information acquisition step, which acquires material information including at least material images and property information of the offerings that are the subject of the advertising information, An advertising information generation step involves inputting the results of analyzing competitive information, which is information on similar offerings to the aforementioned offering, obtained based on the aforementioned material information, and an advertising information generation prompt generated based on the aforementioned material information, into a generation model, and generating the advertising information based on the output information output from the generation model, thereby generating the advertising information. A support program to enable execution.

7. A support method for generating advertising information, Computers A material information acquisition step, which acquires material information including at least material images and property information of the offerings that are the subject of the advertising information, An advertising information generation step involves inputting the results of analyzing competitive information, which is information on similar offerings to the aforementioned offering, obtained based on the aforementioned material information, and an advertising information generation prompt generated based on the aforementioned material information, into a generation model, and generating the advertising information based on the output information output from the generation model, thereby generating the advertising information. A method to support the execution of this task.

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