Method and system for dynamically generating instance for content of information provider

Abstract

To provide a method and a system for dynamically generating an instance for a content of an information provider.SOLUTION: The method for dynamically generating a content according to an embodiment includes the steps of: confirming an LLM result generated on the basis of an LLM (Large Language Models) for a prompt of a user; generating an instance for a content of the information provider by using the LLM result and an asset registered in advance of the information provider; and providing the generated instance so that the instance will be displayed in relation to the LLM result.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The following description relates to a method and system for dynamically generating instances for information provider content. [Background technology]

[0002] A large language model (LLM) is a type of artificial intelligence trained on large amounts of text data to respond in human language to natural language input. It is a language model consisting of an artificial neural network with many parameters (usually billions or more). Such LLMs are trained on large amounts of unlabeled text using self-supervised or semi-self-supervised learning. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Korean Patent No. 10-2551531 Summary of the Invention [Problem to be solved by the invention]

[0004] A method and system for dynamically generating instances for information provider content is provided. [Means for solving the problem]

[0005] A method for dynamic content generation in a computer device including at least one processor is provided, the method including: verifying, by the at least one processor, a generated Large Language Model (LLM) result based on an LLM in response to a user prompt; generating, by the at least one processor, an instance for the information provider's content using the LLM result and pre-registered assets of the information provider; and providing, by the at least one processor, the generated instance so that the generated instance is displayed in association with the LLM result.

[0006] A computer program is provided for causing a computer device to carry out the method.

[0007] A computer-readable recording medium is provided on which a program for causing a computer device to execute the method is recorded.

[0008] A computer device is provided, comprising at least one processor implemented to execute computer-readable instructions, the at least one processor: ascertaining generated Large Language Models (LLM) results in response to user prompts, utilizing the LLM results and pre-registered assets of an information provider to generate instances for the information provider's content, and providing the generated instances such that the generated instances are displayed in association with the LLM results. Effect of the Invention

[0009] A method and system can be provided for dynamically generating instances for information provider content. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an example of a network environment in accordance with an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a computing device in accordance with an embodiment of the present invention. [Diagram 3] 1 is a schematic diagram illustrating a dynamic content generation system according to an embodiment of the present invention; [Figure 4] 1 is a flow chart illustrating an example of a method for dynamically generating content in accordance with an embodiment of the present invention. [Diagram 5] FIG. 2 illustrates an example of a dynamically created advertisement instance in accordance with one embodiment of the present invention. [Figure 6] 1 is an exemplary diagram illustrating a process of providing an answer to a user prompt in accordance with an embodiment of the present invention. [Figure 7] FIG. 10 illustrates an example of providing search results in accordance with an embodiment of the present invention. [Figure 8] FIG. 13 is a diagram illustrating an example of a chat mode in one embodiment of the present invention, in which an answer is provided as an LLM result through a conversation between an LLM-based artificial intelligence and a user. [Figure 9] FIG. 13 is a diagram illustrating an example of a chat mode in one embodiment of the present invention, in which an answer is provided as an LLM result through a conversation between an LLM-based artificial intelligence and a user. [Figure 10] FIG. 13 is a diagram illustrating an example of a chat mode in one embodiment of the present invention, in which an answer is provided as an LLM result through a conversation between an LLM-based artificial intelligence and a user. [Figure 11] FIG. 13 is a diagram illustrating an example of a chat mode in one embodiment of the present invention, in which an answer is provided as an LLM result through a conversation between an LLM-based artificial intelligence and a user. [Figure 12] FIG. 13 is a diagram illustrating an example of a chat mode in one embodiment of the present invention, in which an answer is provided as an LLM result through a conversation between an LLM-based artificial intelligence and a user. [Figure 13] FIG. 11 is an exemplary diagram showing a function for switching to a second mode in an embodiment of the present invention. [Figure 14] FIG. 11 is a diagram showing an example of a function for switching to a second mode in one embodiment of the present invention. [Figure 15] FIG. 13 is a diagram showing another example of a function for switching to the second mode in the embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] <Summary of the Invention> A method for dynamic content generation in a computer device including at least one processor is provided, the method including: verifying, by the at least one processor, a generated Large Language Model (LLM) result based on an LLM in response to a user prompt; generating, by the at least one processor, an instance for the information provider's content using the LLM result and pre-registered assets of the information provider; and providing, by the at least one processor, the generated instance so that the generated instance is displayed in association with the LLM result.

[0012] According to one aspect, the step of generating an instance for the content may be characterized by combining pre-registered assets according to keywords and materials extracted from the LLM results, previous conversations between the LLM-based artificial intelligence and the user, and prompts from the information provider, and dynamically generating an instance for the content by modifying at least one of expression, format, message tone, and manner in the combined assets.

[0013] According to another aspect, the step of generating an instance for the content may be characterized by extracting a plurality of prompts from the results of the LLM and the assets, and inputting the extracted plurality of prompts into the LLM to dynamically generate an instance for the information provider's content.

[0014] According to another aspect, the assets may include at least one of a Uniform Resource Locator (URL) associated with the content that the information provider wishes to provide, a title of the content, an identification name of the content, a category of the content, multimedia associated with the content, content of the content, and content of an article associated with the content.

[0015] According to another aspect, the step of generating an instance for the content may be characterized by generating an instance for the content by further utilizing a pre-registered prompt of the information provider.

[0016] According to another aspect, the pre-registered prompt may include at least one of a phrase, keyword, and tone or format of an information message provided through an instance for the content entered by the information provider to be highlighted in relation to the content the information provider intends to provide.

[0017] According to yet another aspect, generating an instance for the content may further include using a prompt from the user to generate an instance for the content.

[0018] According to yet another aspect, the step of generating an instance for the content may be characterized in that the step of generating an instance for the content further utilizes information about the user to generate the instance for the content, the information about the user including at least one of demos, interests, and purchasing information of the user.

[0019] According to another aspect, the step of generating an instance for the content may further utilize characteristics and weighting values ​​of a target included in a pre-registered prompt of the information provider to generate an instance for the content, the weighting values ​​including at least one of a weighting value of each characteristic of the target and a weighting value of each content of the characteristic of the target.

[0020] According to another aspect, the information provider may be selected from among a plurality of information providers through a dynamic auction among information providers associated with at least one of the user prompts, the results of the LLM, and recommendation queries generated by the large-scale language model.

[0021] According to another aspect, a mode switching function to a mode in which an answer is provided as an LLM result through a dialogue between the LLM-based artificial intelligence and the user may be provided through a page provided for the search service.

[0022] According to another aspect, when content of a specific brand is disclosed through a page provided for the search service, the mode switching function may be provided in conjunction with the content of the specific brand.

[0023] According to another aspect, the mode switching function may be in the form of a link for executing the mode switching function and may be included in at least one recommendation prompt generated in association with the particular brand, and the at least one recommendation prompt may be provided through the page in conjunction with content for the particular brand.

[0024] According to yet another aspect, the page may include a search results page provided in response to the user's search terms, the mode switching function being in the form of a link for executing the mode switching function and included in at least one recommendation prompt generated in association with at least one search result among the search results included in the search results page, and the at least one recommendation prompt may be provided via the search results page in association with the at least one search result.

[0025] A computer program is provided for causing a computer device to carry out the method.

[0026] A computer-readable recording medium is provided on which a program for causing a computer device to execute the method is recorded.

[0027] A computer device is provided, comprising at least one processor implemented to execute computer-readable instructions, the at least one processor: ascertaining generated Large Language Models (LLM) results in response to user prompts, utilizing the LLM results and pre-registered assets of an information provider to generate instances for the information provider's content, and providing the generated instances such that the generated instances are displayed in association with the LLM results.

[0028] <Details of the Invention> Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings.

[0029] The dynamic content generation system according to the embodiment of the present invention may be realized by at least one computer device. In this case, a computer program according to an embodiment of the present invention may be installed and executed in the computer device realizing the dynamic content generation system, and the computer device may execute the dynamic content generation method according to the embodiment according to the control of the executed computer program. The above-mentioned computer program may be recorded in a computer-readable recording medium in combination with the computer device to cause the computer to execute the dynamic content generation method.

[0030] Fig. 1 is a diagram showing an example of a network environment in one embodiment of the present invention. The network environment in Fig. 1 shows an example including a plurality of electronic devices 110, 120, 130, 140, a plurality of servers 150, 160, and a network 170. Fig. 1 is merely an example for explaining the invention, and the number of electronic devices and the number of servers are not limited to those shown in Fig. 1.

[0031] The electronic devices 110, 120, 130, and 140 may be fixed terminals or mobile terminals realized by computer systems. Examples of the electronic devices 110, 120, 130, and 140 include smartphones, mobile phones, navigation systems, personal computers (PCs), notebook PCs, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), tablets, game consoles, wearable devices, internet of things (IoT) devices, virtual reality (VR) devices, and augmented reality (AR) devices. As an example, FIG. 1 shows a smartphone as an example of the electronic device 110, but in the embodiment of the present invention, the electronic device 110 may mean one of various physical computer systems that can communicate with other electronic devices 120, 130, and 140 and / or servers 150 and 160 via a network 170 using a substantially wireless or wired communication method.

[0032] The communication method is not limited, and may include not only a communication method utilizing a communication network (for example, a mobile communication network, a wired Internet, a wireless Internet, a broadcast network, a phase network, etc.) that the network 170 can include, but also a short-range wireless communication between devices. For example, the network 170 may include any one or more of a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a broadband network (BBN), the Internet, etc. Furthermore, the network 170 may include any one or more of a network topology including, but not limited to, a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or a hierarchical network, etc.

[0033] Each of the servers 150 and 160 may be realized by one or more computer devices that communicate with the multiple electronic devices 110, 120, 130, and 140 via the network 170 to provide instructions, codes, files, contents, services, and the like. For example, the server 150 may be a system that provides a first service to the multiple electronic devices 110, 120, 130, and 140 connected via the network 170, and the server 160 may be a system that provides a second service to the multiple electronic devices 110, 120, 130, and 140 connected via the network 170. As a more specific example, the server 150 may provide a service (such as a search service, for example) targeted by an application, which is a computer program installed and executed in the multiple electronic devices 110, 120, 130, and 140, as a first service to the multiple electronic devices 110, 120, 130, and 140. As another example, the server 160 may provide, as a second service, a service of distributing files for installing and executing the above-mentioned applications to the multiple electronic devices 110, 120, 130, and 140.

[0034] 2 is a block diagram showing an example of a computer device according to an embodiment of the present invention. Each of the electronic devices 110, 120, 130, and 140 and the servers 150 and 160 described above may be realized by a computer device 200 shown in FIG.

[0035] Such a computer device 200 may include a memory 210, a processor 220, a communication interface 230, and an input / output interface 240, as shown in FIG. 2. The memory 210 is a computer-readable recording medium and may include a random access memory (RAM), a read only memory (ROM), and a permanent mass storage device such as a disk drive. Here, a permanent mass storage device such as a ROM or a disk drive may be included in the computer device 200 as a separate permanent storage device separate from the memory 210. The memory 210 may also store an operating system and at least one program code. Such software components may be loaded into the memory 210 from a computer-readable recording medium separate from the memory 210. Such separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. In another embodiment, the software components may be loaded into the memory 210 through a communication interface 230 that is not a computer-readable recording medium. For example, the software components may be loaded into the memory 210 of the computing device 200 based on a computer program installed by a file received over the network 170 .

[0036] The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to the processor 220 by the memory 210 or the communication interface 230. For example, the processor 220 may be configured to execute instructions received according to program code stored in a storage device such as the memory 210.

[0037] The communication interface 230 may provide a function for the computer device 200 to communicate with other devices (for example, the above-mentioned recording device) via the network 170. For example, requests, instructions, data, files, etc. generated by the processor 220 of the computer device 200 according to program codes recorded in a recording device such as the memory 210 may be transmitted to other devices via the network 170 under the control of the communication interface 230. Conversely, signals, instructions, data, files, etc. from other devices may be received by the computer device 200 via the communication interface 230 of the computer device 200 via the network 170. The signals, instructions, data, etc. received via the communication interface 230 may be transmitted to the processor 220 or the memory 210, and the files, etc. may be recorded on a recording medium (the above-mentioned permanent recording device) that the computer device 200 may further include.

[0038] The input / output interface 240 may be a means for interfacing with the input / output device 250. For example, the input device may include a device such as a microphone, a keyboard, or a mouse, and the output device may include a device such as a display and a speaker. As another example, the input / output interface 240 may be a means for interfacing with a device in which functions for input and output are integrated into one, such as a touch screen. The input / output device 250 may be configured as a single device together with the computer device 200.

[0039] Also, in other embodiments, computing device 200 may include fewer or more components than those of Figure 2. However, most prior art components need not be explicitly illustrated. For example, computing device 200 may be implemented to include at least some of the input / output devices 250 described above, and may further include other components such as a transceiver, a database, etc.

[0040] 3 is a schematic diagram of a dynamic content generation system according to an embodiment of the present invention, showing a dynamic content generation system 310, a search system 320, a number of users 330, and a number of information providers 340.

[0041] The search system 320 may correspond to a server (for example, the server 150) that provides a search service to a plurality of users 330, and may be realized by at least one computer device 200. Here, each of the plurality of users 330 may be a physical device of the user that connects to the search system 320 via the network 170 to receive the search service, and such a physical device may be realized by the computer device 200 described above.

[0042] The content dynamic generation system 310 according to the present embodiment may be included in the search system 320, or may be realized in a form that links with the search system 320 via the network 170. In the embodiment of FIG. 3, an example is shown in which the content dynamic generation system 310 is realized in a form that is included in the search system 320. In this case, the content dynamic generation system 310 may be realized on at least one physical device for realizing the search system 320. Depending on the embodiment, the content dynamic generation system 310 may be realized in a physical device other than the physical device that realizes the search system 320, and may be realized in a form that communicates with the search system 320 via the network 170.

[0043] The search service provided by the search system 320 to the multiple users 330 may include search results corresponding to the user's input. The search results may be generated based on information searchable on the web. The search system 320 may also provide a search service by including information to be provided by the multiple information providers 340 (instances for the information provider's content) in the search results. Here, the information provided by the multiple information providers 340 may be advertising information, but is not limited to this. The search service itself that provides such search results is well known, and therefore a detailed description will be omitted.

[0044] Meanwhile, the search system 320 according to the present embodiment may provide a search service by including an answer based on artificial intelligence such as a large language model (LLM) in the search results. For example, assume that the search system 320 receives a natural language-based prompt from a specific user among the multiple users 330. In this case, the search system 320 may input the received prompt into the LLM, generate a first answer suitable for the prompt as an LLM result, and provide the user with a search result including the first answer. At this time, the search result may include at least a part of various existing search results in addition to the first answer. In addition, the search system 320 may provide a search service by a conversation between an LLM-based artificial intelligence and a user. A search service may be provided to a user by switching between a first mode in which a first answer is provided as an LLM result based on a general search service and a second mode in which a first answer is provided as an LLM result based on a conversation between an LLM-based artificial intelligence and a user. At this time, a user interface for switching to the second mode may be provided in the first mode, and a user interface for switching to the first mode may be provided in the second mode. Additionally, in each of the first and second modes, at least a portion of the first response may further include an instance for the information provider's content being provided as a second response.

[0045] According to an embodiment, the search system 320 may verify whether the natural language based prompt received from the user is a prompt that may provide the information of the information provider. For example, if the information provider is an advertiser who wishes to publish his / her advertisement, the advertiser may not want his / her advertisement to be published in response to a prompt requesting preset illegal information or preset non-advertising information. Therefore, the search system 320 preliminarily verifies whether the user's prompt is a safe prompt for providing the information provider's content. In this case, if the user's prompt is not a prompt requesting preset illegal information or preset non-advertising information, the search system 320 may request the content dynamic generation system 310 to generate and provide a second answer.

[0046] The content dynamic generation system 310 may dynamically generate an instance for content of an information provider selected from a plurality of information providers 340. Such an instance may be generated as an AI-based second answer onto which a message of the information provider is projected, and may be transmitted to the search system 320. In this case, the search system 320 may provide a user with a search result including not only the first answer generated using the LLM, but also the AI-based second answer generated by the content dynamic generation system 310.

[0047] In this case, the search system 320 may dynamically generate the AI-based second answer using the user's prompt, the first answer generated using the LLM, assets registered by the information provider, and / or prompts registered by the information provider, instead of providing the information provided by the information provider as is. Here, the assets may include, for example, a uniform resource locator (URL) related to the content to be provided by the information provider, a title or identification name of the content, a content category, multimedia related to the content, the content, and the content of an article related to the content. Here, the multimedia related to the content may include an image or a video related to the content. For example, if the information provider is an advertiser who wants to advertise a specific product or service, the assets may include a URL related to the product or service, a product name or a service name, a product or service category, product information or service information, and the content of an article related to the product or service. In addition, the prompt registered by the information provider may include information such as a phrase or keyword to be emphasized in relation to the content to be provided by the information provider, and a tone or format of an information message to be provided as the second answer. In this way, the search system 320 may dynamically generate a second answer in response to a user's natural language-based prompt, taking into consideration not only a first answer generated using the LLM, but also the registered assets and prompts of an information provider who wishes to provide their own information, and even the first answer.

[0048] In addition, depending on the embodiment, the content dynamic generation system 310 may generate the second answer by further using information about the user. Here, the information about the user may include the user's demo, interests, purchase information, etc., and may be used to customize the second answer for the user.

[0049] In addition, since there may be a plurality of information providers who wish to disclose their information, the content dynamic generation system 310 may dynamically determine which of the plurality of information providers to use for generating the second answer by using their assets and prompts. As an example, the content dynamic generation system 310 may primarily select an information provider associated with the first answer, i.e., the LLM result, from among the plurality of information providers based on the first answer. According to an embodiment, the content dynamic generation system 310 may use at least one of the user's prompt, the LLM result, and a recommendation query generated by the LLM when primarily selecting an information provider. Thereafter, the content dynamic generation system 310 may dynamically perform an auction among the primarily selected information providers and select the first information provider as an information provider for generating the second answer. As the auction method, one of well-known methods may be used. For example, a GSP (Generalized Second Price) auction method may be used.

[0050] In this way, when the search system 320 provides an answer to a natural language-based prompt from a user, the content dynamic generation system 310 can dynamically generate an answer that is dynamically generated based on the assets and prompts of the information provider, i.e., an artificial intelligence-based second answer in which the message of the information provider is projected. Thus, the search system 320 can provide the user with an answer that is dynamically generated in association with the natural language-based prompt received from the user, such that the message of the information provider is projected.

[0051] In addition, depending on the embodiment, the content of the user prompt may be insufficient for matching with information of a particular information provider. In this case, the dynamic content generation system 310 may provide the search system 320 with a question to guide the user prompt to include sufficient information for the matching. Such a question may also be generated by the LLM, and information obtained as the user's answer to the question may also be included in the user prompt.

[0052] 4 is a flowchart showing an example of a method for dynamic content generation according to an embodiment of the present invention. The method for dynamic content generation according to this embodiment may be executed by a computer device 200 that realizes the dynamic content generation system 310 described above. In this case, the processor 220 of the computer device 200 may be realized to execute control instructions according to the operating system code and at least one computer program code included in the memory 210. Here, the processor 220 may control the computer device 200 so that the computer device 200 executes steps 410 to 430 included in the method of FIG. 4 according to the control instructions provided by the code recorded in the computer device 200.

[0053] In step 410, the computer device 200 may check LLM results generated based on Large Language Models (LLMs) in response to a user prompt. Here, the user prompt may include, but is not limited to, a user query input through a search service provided by the search system 320. As an example, the search system 320 may provide a conversation function between an artificial intelligence and a user, and information input by the user may be used as a user prompt through such a conversation function. In this case, the LLM results generated by the search system 320 using the LLM in response to the information input by the user may be checked by the computer device 200 in step 410. Such a conversation function may be realized in the form of a sub-service included in the search service, or may be realized in the form of a service separate from the search service and linked to the search service.

[0054] At step 420, the computing device 200 may generate an instance for the information provider's content using the LLM results and the information provider's pre-registered assets.

[0055] First, the information provider may be an information provider selected from a plurality of information providers (for example, a plurality of information providers 340). As an example, the computer device 200 may primarily select an information provider associated with at least one of the user's prompt, the LLM result, and a recommendation query generated by the large-scale language model from among the plurality of information providers. As described above, the primarily selected information provider may include an information provider who intends to provide information associated with at least one of the user's prompt, the LLM result, and the recommendation query. As an example, the computer device 200 may compare at least one of the user's prompt, the LLM result, and the recommendation query with each of the information registered in association with the plurality of information providers, and primarily select an information provider who has registered information associated with at least one of the user's prompt, the LLM result, and the recommendation query. Thereafter, the computer device 200 may dynamically perform an auction among the primarily selected information providers to select a final information provider. As an example, the computer device 200 may select a final information provider by conducting a dynamic auction among the initially selected information providers using a known auction method such as a GSP auction method.

[0056] In addition, the assets may include, as described above, a uniform resource locator (URL) related to the content that the information provider intends to provide, a title or identification name of the content, a content category, multimedia related to the content, the content, and the content of an article related to the content. If the information provider is an advertiser, the advertiser may register various advertisement materials as assets. As the advertisement material assets, various kinds and types of data such as text, images, and promotional videos related to the advertiser's products or services, as well as news articles, short-form contents, audio, and URLs of web documents may be registered. In this case, the computer device 200 may derive various information related to the advertiser's advertisement, such as the data format, people appearing in the data, products, services, style, and tone and manner, using the LLM for the registered assets, and use the information to create an instance for the advertiser's advertisement.

[0057] Also, according to an embodiment, the information provider's prompt may be further utilized in generating the instance. As an example, the prompt registered by the information provider may include at least one of a phrase or keyword entered to be emphasized in relation to the content to be provided by the information provider, and a tone or format of an information message to be provided as an answer. As an example, the information provider's prompt may be registered in advance by a method in which the information provider inputs the prompt to the search system 320. For example, the search system 320 may provide the information provider with a registration function for registering assets and prompts from the information provider. If the information provider is an advertiser, it is assumed that the advertiser uses the registration function provided by the search system 320 to input and / or select various prompts such as the purpose of the advertising campaign, a message to be emphasized, an advertising target, and values ​​such as a desired tone and manner when responding to an advertisement. In this case, the computer device 200 may generate an instance for the information provider's content by combining the information provider's assets with the information provider's prompts to meet the user's needs and conversation context, taking into account keywords and materials in the LLM results, previous conversations between the LLM-based artificial intelligence and the user, and modifying at least one of the expression, format, message tone, and manner of the combined assets. As another example, the computer device 200 may extract various prompts from the LLM results, the information provider's assets, and prompts, and then input the extracted prompts to the LLM to generate an instance for the information provider's content. In this case, the LLM may be trained to generate an instance for the information provider's content according to the input prompts.

[0058] Meanwhile, the registration function for allowing an asset and a prompt to be registered from an information provider may be based on a conversation between the LLM-based artificial intelligence and the information provider. For example, the registration function may provide a function for a conversation between the LLM-based artificial intelligence and the information provider, and may allow an asset and / or a prompt to be registered from the information provider based on the content of the conversation.

[0059] Also, according to an embodiment, the computer device 200 may generate an instance customized for a user by generating an instance by further using information of a Data Management Platform (DMP) that stores user information such as the user's gender, age group, and interests. As an example, if it is determined that additional user information is necessary, a process of asking for additional information through an LLM conversation with the user by the search system 320 may be added. For example, in relation to a user's prompt for shoes, the computer device 200 may use the search system 320 to have a conversation with the user using an LLM-based artificial intelligence and provide the user with questions such as "What is your foot size?" and "Do you have wide feet?". In addition, the computer device 200 may add the user's answers to such questions as user information and use them in generating an instance. For this reason, the pre-registered prompt of the information provider may further include target characteristics and weights. As an example, the target characteristics may include demographics (gender, age (or age group)), interests, and / or purchase information. The weighting value may include, for example, a weighting value for each characteristic of the target and / or a weighting value for each content of the characteristic. For example, the weighting value for each characteristic may indicate how much weighting is given to which characteristic among the gender, age, and interests of the target. For example, if the gender is female, a weighting value of 5 may be given if the age is in the 20s, and a weighting value of 3 may be given if the interest is sports. In addition, the weighting value for each content may indicate how much weighting is given to which content among the contents of the same characteristic. For example, if the interests set by the information provider are sports, fashion, and games, the information provider may give a weighting value of 8 to sports, 6 to fashion, and 2 to games. In this case, the computer device 200 may further use information on the characteristics and weighting values ​​of the target to generate an instance for the information provider's content. Such information on the characteristics and weighting values ​​of the target may be optionally used when information on the characteristics and weighting values ​​of the target can be used in the LLM.

[0060] In step 430, the computer device 200 may provide the generated instance so that the generated instance is displayed in association with the LLM result. As an example, in response to a user prompt input as a query such as a user's search term in a search service, the search system 320 may provide search results including the LLM result. In this case, the generated instance may be included in the search result in association with the LLM result, so that the instance for the information provider's content may be displayed to the user in association with the LLM result. As another example, in a conversation between an LLM-based artificial intelligence and a user, when an LLM result is provided as an answer to a user's prompt, an instance for the information provider's content may be provided in association with the provided answer.

[0061] Thereafter, the computer device 200 may use the user feedback data to improve or modify the content of the information provider. For example, if the information provider is an advertiser, the user feedback data may be used to analyze the performance of the advertisement, to suggest to the advertiser to register additional assets or change the advertiser's prompt, or to change the method of generating an advertisement instance.

[0062] FIG. 5 is a diagram showing an example of an advertisement instance dynamically created in an embodiment of the present invention. FIG. 5 shows an example of an advertisement instance 540 generated by considering all of an asset 510 for an advertisement, an LLM result 520 generated by the LLM in response to a user's prompt, and an advertiser's prompt 530. In FIG. 5, "AAA" means the advertiser's product name, and "BBB" means the advertiser's brand name. In addition, in the prompt 530, "ad" means an advertisement, "SEO" means search engine optimization, and "organic" means the LLM result 520. In this way, the search system 320 can generate an advertisement instance 540 based on the LLM by considering both the LLM result 520 generated by the LLM in response to a user's prompt and the asset 510 and the prompt 530 registered by the advertiser as an information provider. As a more specific example, the dynamic content generation system 310 may extract multiple prompts for the LLM from the LLM results 520, assets 510, and prompts 530, respectively, and input the extracted prompts into the LLM to generate the advertisement instance 540.

[0063] In the embodiment of FIG. 5, an example is described in which the assets 510 contain only text and a text-based advertisement instance 540 is generated, but when a collection of assets including various multimedia such as images and videos is used, the dynamic content generation system 310 can provide advertisement instances in more diverse formats including images, videos, etc.

[0064] 6 is an exemplary diagram illustrating a process of providing an answer to a user's prompt in an embodiment of the present invention. In the embodiment of FIG. 6, an example is described in which an information provider wishes to publish an advertisement for an advertiser's product or service as information to be provided.

[0065] The search system 320 may receive a user prompt 601 from a user terminal connected via the network 170. As an example, the prompt may correspond to a natural language-based search term input by the user. The user may input the search term into a user interface of a search service provided through the user's terminal, and the search system 320 may receive the search term input into the user interface as the user prompt 601.

[0066] At this time, the search system 320 may select the prompt to be actually used by analyzing the user prompt 601 and extracting and summarizing the user intent in the process of extracting and summarizing the user intent (User Intent Extracting & Summarizing 602).

[0067] Meanwhile, the search system 320 and / or the content dynamic generation system 310 may guide the user to provide sufficient information to provide an answer that reflects the marketing message of the advertiser. As an example, the content of the user prompt 601 may be insufficient to match the marketing message of a particular advertiser. In this case, the content dynamic generation system 310 may generate a question to induce additional information for selecting a particular advertiser, and may provide the generated question to the user through the search system 320. After this, when an answer to the question is received from the user, the content of the answer may be used to complete the prompt. The question specification prompt 603 may include a prompt obtained from the user's answer.

[0068] At this time, the search system 320 and / or the dynamic content generation system 310 may select a specified user prompt 604 as a prompt for providing the marketing message. That is, the specified user prompt 604 may be specified based on a prompt obtained from the extraction and summary of user intent 602 for the user prompt 601 and a question specification prompt 603.

[0069] Prompt Ads Safe Check 605 may be an example of a process that verifies whether a specified user prompt 604 is a prompt that is OK for exposing an advertiser's marketing message to a user. As an example, the search system 320 may generate and provide an answer if the specified user prompt 604 is not a prompt requesting pre-defined illegal information or pre-defined non-advertising information.

[0070] The search system 320 may also input specified user prompts 604 into the LLM to generate LLM results. Figure 5 shows an example of an Organic LLM Result Memory 606 that stores such LLM results.

[0071] The content dynamic generation system 310 may primarily select an advertiser associated with the LLM result based on the LLM result recorded in the organic LLM result memory 606. In this case, the advertiser associated with the LLM result may be an advertiser who has registered a marketing message that may be published together with the LLM result. The marketing message that may be published together with the LLM result may be selected based on the relevance between the information registered by the advertiser and the LLM result. In addition, according to an embodiment, the content dynamic generation system 310 may use at least one of the user prompt 604, the LLM result, and a recommendation query generated by a large-scale language model when primarily selecting an advertiser. In this case, the content dynamic generation system 310 may primarily select an advertiser based on the relevance between at least one of the user prompt 604, the LLM result, and the recommendation query and the information registered by the advertiser. In this case, the content dynamic generation system 310 may select a specific advertiser from among the advertisers primarily selected by the Ad Prompt Auction 607.

[0072] When the advertiser is selected, the content dynamic generation system 310 may obtain the advertisement assets 608 registered by the selected advertiser and the advertiser prompts 609 registered by the selected advertiser. In this case, the content dynamic generation system 310 may generate an answer prompt 610 reflecting the marketing message of the advertiser by using at least one of the user prompt 601 and the LLM results recorded in the organic LLM result memory 606, and at least one of the advertisement assets 608 and the advertiser prompts 609. In some embodiments, the advertiser may want to provide an answer in a specific format according to the characteristics of the user. For this reason, the content dynamic generation system 310 may generate the answer prompt 610 by further reflecting information about the user. As an example, the information about the user may include at least one of the demo, interests, and purchase information of the user. For example, the content dynamic generation system 310 may analyze the advertiser prompts 609 and find out that the advertiser wants a relatively more detailed answer to be provided to female users than to male users. In this case, the dynamic content generation system 310 may determine the gender of the user from the user's demo, and then generate the answer prompt 610 taking into consideration the determined gender of the user.

[0073] After the answer prompt 610 is generated, the dynamic content generation system 310 may check (611) whether the generated answer prompt 610 is generated to suit the tone and / or format confirmed from the advertiser prompt 609. If the generated answer prompt 610 does not match the tone and / or format desired by the advertiser, it is also possible to process the answer prompt 610 to match the tone and / or format desired by the advertiser. In addition, depending on the embodiment, the search system 320 may additionally check whether the generated answer prompt 610 is safe to be made public, etc.

[0074] Thereafter, the dynamic content generation system 310 may provide the finally generated answer 612 to the user through the search system 320. As an example, the search system 320 may add the answer 612 to the search results and provide it to the user through the search service. In addition, depending on the embodiment, user information (such as gender, age, and interests) recorded in the DMP 613 may be further used to generate the answer prompt 610. By utilizing such user information, the search system 320 can generate an answer 612 optimized for the user.

[0075] Meanwhile, in order to efficiently utilize the DMP 613, the advertiser prompt 609 may further include information regarding the characteristics and weightings of the target. As an example, the characteristics of the target may include demographics (gender, age (or age group)), interests, and / or purchase information. As an example, the weightings may include the weightings of each characteristic of the target and / or the weightings of each content of the characteristics. As an example, the weightings of each characteristic may indicate how much weighting is given to which characteristic among the gender, age, and interests of the target. For example, if the gender is female, a weighting of 5 may be given, if the age is in the 20s, a weighting of 3 may be given, and if the interest is exercise, a weighting of 8 may be given. In addition, the weightings of each content may indicate how much weighting is given to which content among the contents of the same characteristic. As an example, if the interests set by the advertiser are exercise, fashion, and games, the advertiser may give a weighting of 8 to exercise, 6 to fashion, and 2 to games through the advertiser prompt 609. In this case, the dynamic content generation system 310 may further utilize information regarding advertiser desired target characteristics and weightings contained in the advertiser prompt 609 to generate the answer prompt 610. Such information regarding target characteristics and weightings may be optionally utilized if information regarding target characteristics and weightings is available in the LLM.

[0076] FIG. 7 is a diagram showing an example of providing search results in an embodiment of the present invention. FIG. 7 shows an example of a screen of a search page 700 provided to a user through a search service. The search page 700 may include a user interface 710 for allowing a user to input a prompt. The search page 700 may also include a search result area 720 for displaying search results. In this case, the search result area 720 may include an LLM result area 730 for displaying LLM results generated based on a large-scale language model in response to the user's prompt.

[0077] The search result area 720 also shows an example of an answer area 740 that displays an answer generated by the dynamic content generation system 310 in response to a user's prompt. In this embodiment, an example is shown in which multiple answers are displayed in the answer area 740. In this way, multiple answers can be generated and displayed in response to one prompt. It is also possible to generate and display answers for each of two or more information providers. For this reason, the search system 320 may select two or more information providers.

[0078] On the other hand, the embodiment of FIG. 7 illustrates an example in which answers of information providers selected based on the user's prompt input to the user interface 710 and / or the LLM results displayed in the LLM result area 730 are displayed in the expansion area 750. For example, the expansion area 750 may further display answers of information providers selected based on at least one of the user's prompt, the LLM result, and the recommendation query generated by the large-scale language model. If the information provider is an advertiser, the expansion area 750 may further display advertisements of the advertisers selected based on at least one of the user's prompt, the LLM result, and the recommendation query generated by the large-scale language model.

[0079] Also, the dotted frame 760 displays a question for requesting an additional prompt from the user in relation to the answer displayed in the expansion area 750. When the user selects a specific question, this question may be recognized as the user's additional prompt. When providing a conversational search service, the user's additional prompt may be recognized as the user's next conversation. In this case, the search system 320 and / or the content dynamic generation system 310 may generate LLM results and / or answers by taking into account the entire conversation with the user.

[0080] In addition, in the embodiment of FIG. 7, an example in which a user's prompt is input to the user interface 710 of the search service to dynamically generate an answer has been described. However, depending on the embodiment, the search results may also include an interface for generating and providing a dynamic answer. For example, a function for dynamically generating and providing an answer by inputting a user's prompt through each and / or some of various vertical services provided in the existing search ecosystem may be provided to the user. Here, the vertical service may refer to a service for each of various collections that classify search results, such as shopping search, knowledge search, local search, UGC (User Generated Contents) search, language search, image search, video search, and news search. For example, when a shopping search service is provided separately as a vertical service of the integrated search service, a function for dynamically generating and providing an answer may be provided to the user by inputting a user's prompt in the shopping search service. When a plurality of different advertising services are provided as vertical services of the integrated search service, a function for dynamically generating and providing an answer may be provided to the user by inputting a user's prompt in each of the plurality of advertising services.

[0081] 8 to 12 show an example of a chat mode in which an answer that is an LLM result is provided through a conversation between an LLM-based artificial intelligence and a user in one embodiment of the present invention. Such a chat mode may correspond to the second mode described above.

[0082] In the embodiment of FIG. 8, an input interface 810 is shown for inputting a user's prompt. The input interface 810 may be associated with a virtual keyboard function for the user to input text and / or a function for transmitting the text input to the input interface 810 to the search system 320. In addition, a session initialization interface 811 for initializing a current conversation session may be provided. The session initialization interface 811 may be associated with a function for requesting the search system 320 to initialize the current conversation session and start a new conversation session. In the embodiment of FIG. 8, an example is shown in which the session initialization interface 811 having a specific icon shape is disposed on the left side of the input interface 810, but the shape, type (icon, button, link, etc.), position, etc. of the session initialization interface 811 may be variously set according to the embodiment. In addition, a multimedia input interface 812 for inputting multimedia such as images and videos in addition to text as a user's prompt may be provided. The multimedia input interface 812 may be associated with a function for selecting and transmitting multimedia data stored in the user's terminal or transmitting multimedia data generated through a camera provided in the user's terminal.

[0083] Meanwhile, in the embodiment of FIG. 8, a first area 820 is shown in which a user's prompt transmitted to the search system 320 through the input interface 810 is displayed in the form of a message for conversation. Also, a second area 830 is shown in association with the first area 820 for displaying a process of generating an answer to the user's prompt. The process of generating an answer may include, for example, a "search" process, a "search result analysis" process, a "consider whether further search is necessary" process, and an "answer generation completion" process, but is not limited thereto. In the embodiment of FIG. 8, the second area 830 shows the "answer generation completion" process. Also, the embodiment of FIG. 8 shows a third area 830 in which an answer generated in response to the user's prompt is displayed. At this time, an icon 831 for indicating an entity that provided the answer may be displayed in association with the third area 830. For example, the icon 831 may include information that can be used to identify the search system 820.

[0084] The search system 320 may further provide the user with a first recommendation prompt 840. In this case, the user can continue the next conversation with the artificial intelligence in the current conversation session by simply selecting the first recommendation prompt 840, with the first recommendation prompt 840 being used as the user's prompt. The search system 320 may further provide the user with a second recommendation prompt 850 for a conversation with a specific information provider. In this case, an icon 851 for indicating the information provider may be displayed in association with the second recommendation prompt 850. For example, the icon 851 may include information such as an image or text related to the information provider.

[0085] The embodiment of FIG. 9 illustrates an example in which a conversation with a user is conducted through an artificial intelligence specialized for a specific information provider by the user selecting the second recommendation prompt 850 in FIG. 8. In this case, the artificial intelligence specialized for a specific information provider may also be an artificial intelligence provided by the search system 320 based on the LLM. In some embodiments, the artificial intelligence specialized for a specific information provider may be registered in the search system 320 by a specific information provider, or may be provided by a specific information provider from the beginning. In this case, the answers 910 and 920 provided by the artificial intelligence specialized for a specific information provider may display information 930 for informing that the answers 910 and 920 are provided by a specific information provider, and an icon 851 for the information provider may further be displayed. Such answers 910 and 920 may be dynamically generated by reflecting information (for example, assets, prompts, etc.) associated with the information provider and registered, as described above. Each of the ad cards (ad card 1 (921), ad card 2 (922), and ad card 3 (923)) included in the answer 920 may be produced in a form including a product image and a product description (such as a product identifier and price).

[0086] The embodiment of Figure 10 illustrates an example where a dynamically generated answer is provided for a specific information provider's app advertisement in response to a user prompt for an app recommendation, where a specific brand content answer is dynamically generated and provided in a search generative experience (SGE) style.

[0087] The embodiment of Figure 11 illustrates an example where information provider responses are dynamically generated and provided in other SGE style formats in response to the same user prompts as those shown in Figure 10. In this manner, information provider responses can be dynamically generated and provided in a variety of formats and content in response to prompts.

[0088] The embodiment of Fig. 12 shows an example in which an information provider's response is provided during a conversation with an artificial intelligence as if it were another user in a group chat room. That is, the information provider's response 1220 is provided in a conversational format in addition to the artificial intelligence's response 1210, allowing the user to experience an experience as if they were having a conversation with two or more other users in a group chat room.

[0089] In the above, a search service is provided to a user by switching between a first mode in which a first answer is provided as an LLM result by using a general search service and a second mode in which a first answer is provided as an LLM result by a conversation between an LLM-based AI and a user. Meanwhile, according to an embodiment, a function for switching to the second mode by using a search service that does not provide an LLM result may be provided.

[0090] Figures 13 and 14 are diagrams illustrating an example of a function for switching to the second mode in an embodiment of the present invention. The embodiments of Figures 13 and 14 illustrate an example in which when search results in various categories are provided in association with a user's search term, a function for switching to the second mode is provided in each of the various categories.

[0091] In the embodiment of FIG. 13, a part of a search page 1300 provided to a user through a search service is shown. The search page 1300 may include a user interface 1310 for allowing a user to input a search term. The search page 1300 may also include a search result area 1320 for displaying search results. At this time, the search result area 1320 may display search results for various categories. In the embodiment of FIG. 13, a user inputs a brand name "AAA" of a specific brand as a search term, and a search result 1330 specialized for the brand is provided as one of the search results of various categories. At this time, a function for switching to a second mode may be provided in association with the search result 1330. In the embodiment of FIG. 13, an example is shown in which a recommendation prompt related to the brand is presented, as shown in a dotted frame 1340. At this time, a link including a mode switching function may be set for each recommendation prompt. When a user selects one of the presented recommendation prompts, a second mode in which a first answer is provided as an LLM result by a conversation between an LLM-based artificial intelligence and the user through a link set in the selected recommendation prompt may be provided to the user. The selected recommendation prompt may then be used as the user's prompt in the second mode.

[0092] In the embodiment of FIG. 14, an example is shown in which search results 1410 of other categories in the search result area 1320 are displayed as another part of the search page 1300. At this time, a function for switching to the second mode may be provided in association with the individual search results 1420 related to the input search term. In the embodiment of FIG. 14, an example is shown in which a recommendation prompt related to the individual search results 1420 is presented, as shown in a dotted frame 1430. When the user selects one of the presented recommendation prompts, a second mode may be provided to the user in which a first answer is provided as an LLM result by a conversation between the LLM-based artificial intelligence and the user using the selected recommendation prompt as a user prompt.

[0093] 13 and 14, the functionality for switching to the second mode is provided via a recommendation prompt, but depending on the embodiment, the functionality for switching to the second mode may be provided by an icon, a link, etc. Depending on the embodiment, the functionality for switching to the second mode may be provided in association with the entire search page 1300, rather than a specific search result. For example, the functionality for switching to the second mode may be provided by an icon overlaid on a specific area of ​​the screen, regardless of scrolling of the search page 1300.

[0094] In addition, according to an embodiment, the function for switching to the second mode may be provided in association with content in various services other than the service for providing search results. For example, the function for switching to the second mode may be provided by content related to a particular brand (e.g., a display advertisement (DA) related to the brand or related to products or services of the brand) that is disclosed through the various vertical services as described above.

[0095] Fig. 15 is a diagram showing another example of a function for switching to the second mode in an embodiment of the present invention. Fig. 15 shows an example of a mobile screen 1510 on which a page providing news content is displayed, and shows a state in which brand "AAA" related content (1520) is provided along with the news content through the page. In this case, a recommendation prompt including a function for switching to the second mode may be provided in association with the brand "AAA" related content 1520, as shown in a dotted frame 1530.

[0096] As described above, according to an embodiment of the present invention, a method and system for dynamically generating an AI-based answer to which an information provider's message is projected can be provided.

[0097] The above-described systems or devices may be realized by hardware components or a combination of hardware and software components. For example, the devices and components described in the embodiments may be realized using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or various devices capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications that run on the OS. The processing device may also respond to the execution of the software to access, record, manipulate, process, and generate data. For convenience of understanding, a single processing device may be described as being used, but those skilled in the art will understand that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, the processing device may include multiple processors or one processor and one controller. Other processing configurations, such as parallel processors, are also possible.

[0098] The software may include computer programs, codes, instructions, or a combination of one or more of these, and may configure a processing device to operate as desired or instruct the processing device, either independently or collectively. The software and / or data may be embodied in any type of machine, component, physical device, virtual device, computer storage medium, or device to be interpreted based on the processing device or to provide instructions or data to the processing device. The software may be distributed and stored or executed in a distributed manner on computer systems connected by a network. The software and data may be stored on one or more computer-readable storage media.

[0099] The method according to the embodiment may be realized in the form of program instructions executable by various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The medium may be a medium that continuously records a program executable by a computer, or a medium that temporarily records the program for execution or download. The medium may be a variety of recording or storage means in the form of a single or multiple hardware devices combined, and may be a medium directly connected to a certain computer system, or may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and media configured to record program instructions such as ROMs, RAMs, flash memories, and the like. Other examples of the medium include recording media and storage media managed by app stores that distribute applications, sites that supply or distribute various other software, and servers. Examples of the program instructions include not only machine language codes such as those generated by compilers, but also high-level language codes executed by a computer using an interpreter, etc.

[0100] Although the embodiments have been described based on the limited embodiments and drawings as above, those skilled in the art will be able to make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from that described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or combined in a manner different from that described, or may be counterbalanced or replaced by other components or equivalents, and still achieve suitable results.

[0101] Therefore, different embodiments are within the scope of the appended claims provided that they are equivalent to the claims. [Explanation of symbols]

[0102] 310: Dynamic Ad Generation System 320: Search system 330: User 340:Informant

Claims

1. 1. A method for dynamic content generation in a computing device including at least one processor, comprising: identifying, by the at least one processor, Large Language Models (LLM) results generated based on the LLM in response to user prompts; generating, by the at least one processor, an instance for the information provider's content using the LLM results and the information provider's pre-registered assets; and providing, by the at least one processor, the generated instance such that the generated instance is displayed in association with the LLM results. A method for dynamically generating content.

2. The step of generating an instance for the content includes: Combining the pre-registered assets according to keywords and materials extracted from the LLM results, previous conversations between the LLM-based artificial intelligence and the user, and prompts from the information provider, and dynamically generating an instance for the content by modifying at least one of expression, format, tone of message, and manner in the combined assets. The method for dynamic content generation according to claim 1 .

3. The step of generating an instance for the content includes: extracting prompts from the LLM results and the assets, and inputting the extracted prompts into the LLM to dynamically generate instances for the information provider's content; The method for dynamic content generation according to claim 1 .

4. The assets include at least one of a URL (Uniform Resource Locator) related to the content that the information provider intends to provide, a title of the content, an identification name of the content, a category of the content, multimedia related to the content, content of the content, and content of an article related to the content. The method for dynamic content generation according to any one of claims 1 to 3, characterized in that

5. The step of generating an instance for the content includes: generating an instance for said content further utilizing pre-registered prompts of said information provider; The method for dynamic content generation according to claim 1 .

6. The pre-registered prompts include at least one of a phrase or keyword entered by the information provider to be highlighted in association with the content the information provider intends to provide, and a tone or format of an information message to be provided through an instance for the content. The method for dynamic content generation according to claim 5 .

7. The step of generating an instance for the content includes: Further utilizing the user's prompts to generate instances for the content. The method for dynamic content generation according to claim 1 .

8. The step of generating an instance for the content includes: further utilizing information about the user to generate an instance for the content; The information about the user includes at least one of demos, interests, and purchasing information of the user.

2. The method for dynamic content generation according to claim 1 .

9. The step of generating an instance for the content includes: generating an instance for the content further utilizing target characteristics and weights included in the information provider's pre-registered prompts; The weighting value includes at least one of a weighting value for each characteristic of the target and a weighting value for each content of the characteristic of the target. The method for dynamic content generation according to claim 8 .

10. The information provider is selected from among a plurality of information providers through a dynamic auction among information providers associated with at least one of the user's prompt, the LLM results, and a recommendation query generated by the large-scale language model. The method for dynamically generating content according to any one of claims 1 to 3 and claims 5 to 9.

11. A mode switching function to a mode that provides answers as the LLM results through a dialogue between the LLM-based artificial intelligence and the user is provided through a page provided for a search service. The method for dynamically generating content according to any one of claims 1 to 3 and claims 5 to 9.

12. When content of a particular brand is disclosed through a page provided for the search service, the mode switching function is provided in association with the content of the particular brand. The method for dynamic content generation according to claim 11 .

13. the mode switching function is included in at least one recommendation prompt generated in association with the particular brand in the form of a link for executing the mode switching function; the at least one recommendation prompt is provided through the page in conjunction with content for the particular brand. The method for dynamic content generation according to claim 12 .

14. the pages include a search result page provided in response to the user's search terms; the mode switching function is included in at least one recommendation prompt generated in association with at least one search result among the search results included in the search result page as a form of a link for executing the mode switching function; the at least one recommendation prompt is provided via the search result page in association with the at least one search result. The method for dynamic content generation according to claim 11 .

15. A computer program for causing a computer device to execute the method according to any one of claims 1 to 3 and claims 5 to 9.

16. At least one processor implemented to execute computer readable instructions. Including, by said at least one processor, Reviewing Large Language Models (LLM) results generated based on the LLM in response to a user prompt; generating an instance for the information provider's content using the LLM results and the information provider's pre-registered assets; Providing the generated instance such that the generated instance is displayed in association with the LLM result. A computer device comprising:

17. generating an instance for the content, by the at least one processor; Combining the pre-registered assets according to keywords and materials extracted from the LLM results, previous conversations between the LLM-based artificial intelligence and the user, and prompts from the information provider, and dynamically generating an instance for the content by modifying at least one of expression, format, tone of message, and manner in the combined assets.

17. The computer device of claim 16.

18. generating an instance for the content, by the at least one processor; extracting a plurality of prompts from the results of the LLM and the assets, and inputting the extracted plurality of prompts into the LLM to dynamically generate instances for the information provider's content; 17. The computer device of claim 16.

19. The assets include at least one of a URL (Uniform Resource Locator) related to the content that the information provider intends to provide, a title of the content, an identification name of the content, a category of the content, multimedia related to the content, content of the content, and content of an article related to the content. A computer device according to any one of claims 16 to 18, characterized in that

20. A mode switching function to a mode that provides answers as the LLM results through a dialogue between the LLM-based artificial intelligence and the user is provided through a page provided for a search service. A computer device according to any one of claims 16 to 18, characterized in that

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