A method, apparatus, and computer-readable recording medium for providing a chatbot service that outputs customized fishing information.

Abstract

This document provides a method for providing a chatbot service that outputs customized fishing information. [Solution] The method, upon receiving a question about fishing from a user account registered as a member of the fishing information provision platform, starts a service provision process to provide customized fishing information in response to the question based on the verified user information. An artificial intelligence algorithm analyzes the question about fishing, reflects the detailed information contained in the user information in a vector weighting value, and generates a response sentence to answer the question about fishing. If an image corresponding to the question about fishing and the response sentence is identified, customized fishing information is generated based on the identified image and the response sentence, and the chatbot system outputs the customized fishing information.

Description

Technical Field

[0001] The present invention relates to a method for providing a chatbot service that outputs customized fishing information. Specifically, when a fishing-related question is input from a user account, the user information of the corresponding account is confirmed, and based on this, a service provision process for providing customized fishing information is started. After analyzing the question using an artificial intelligence algorithm and generating a response sentence reflecting the user information, an image corresponding to the fishing-related question and the response sentence is identified, and customized fishing information is generated based on the corresponding image and the response sentence. The present invention relates to a technology for providing to a user account by a chatbot system.

Background Art

[0002] The size of the global chatbot market in 2024 is estimated to be approximately $7.01 billion, growing at an annual average rate of 24.32%, and is expected to reach $20.81 billion in 2029. The growth of such a chatbot market is due to the increasing demand for messenger apps and the change in the customer analysis methods of enterprises. In particular, the emergence of Chat GPT (registered trademark), which is a generative AI, shows better performance than chatbots based on existing rules, and tends to lead the growth of the chatbot market. In response to such a trend, enterprises have been, until recently, worried about problems of AI such as information security leakage and information distortion, and have been negative about the generative AI field. However, as the market and consumer demand have increased rapidly and the service development competition has become fierce, enterprises have been actively participating in the development of generative AI. However, in such a trend, problems such as misoperation, discrimination, and offensive remarks of chatbots have been raised, and ensuring the reliability and ethics of chatbots has become important.

[0003] As a result, enterprises are developing technologies for diversifying the learning data of chatbots and utilizing them in various industrial fields.

[0004] For example, Korean registered patent 10-2653266 (Chatbot dialogue consultation system and method based on artificial intelligence) discloses a technique for collecting knowledge of a target domain and fine-tuning an artificial intelligence algorithm.

[0005] However, the aforementioned prior art only discloses a technology that simply collects knowledge data, separates it into data for embedding and fine-tuning and stores it in a database, generates a custom artificial intelligence model using a training unit, and then outputs responses to questions using a chatbot equipped with the artificial intelligence model based on the data stored in the database. However, it does not disclose a technology that, when a question about fishing is entered from a user account, verifies the user information of the account, initiates a service provision process to provide customized fishing information based on this information, analyzes the question using an artificial intelligence algorithm, generates a response sentence that reflects the user information, identifies images corresponding to the question and response sentence, generates customized fishing information based on the corresponding images and response sentence, and provides it to the user account via a chatbot system. Therefore, there is a need for a technology that can solve this problem. [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] The present invention was made to solve the problems of the prior art described above. When a question about fishing is entered from a user account, the user information of the account is checked, and a service provision process is initiated to provide customized fishing information based on this information. An artificial intelligence algorithm is used to analyze the question, a response sentence is generated that reflects the user information, then images corresponding to the question and response sentence are identified, customized fishing information is generated based on the corresponding images and response sentences, and this is provided to the user account by the chatbot system. The purpose of this invention is to provide accurate information that meets the user's needs through the chatbot, and to provide the user with visual information that is easy to understand. [Means for solving the problem]

[0007] A method for providing a chatbot service that outputs customized fishing information, which is embodied in a computing device including one or more processors and one or more memories that store commands executable by the processors according to an embodiment of the present invention, comprising: a process initiation step in which, upon receiving a question about fishing from a user account registered as a member of a fishing information provision platform, the user information registered in the user account is confirmed, and a service provision process is initiated to provide customized fishing information in response to the question about fishing, based on the confirmed user information; and once the service provision process has been initiated, the stored artificial intelligence algorithm provides the information about fishing The method is characterized by including: a response sentence generation step of analyzing a question and reflecting the detailed information contained in the user information in a vector weighting value to generate a response sentence to answer the question about fishing; and a customized fishing information provision step of, when the stored artificial intelligence algorithm identifies images corresponding to the question about fishing and the response sentence as the generation of the response sentence is completed, generating customized fishing information for the question about fishing based on the identified images and the response sentence, and having the chatbot system of the fishing information provision platform output the customized fishing information and provide it to the user account.

[0008] The aforementioned process initiation step, upon receiving a question about fishing from the user account, includes a detailed information verification step that confirms the detailed information contained in the user information registered in the user account, which includes environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information. The process includes, once the execution of the detailed information verification step is complete, an analysis initiation step which starts a service provision process to generate and provide customized fishing information by analyzing the fishing-related questions based on the verified detailed information.

[0009] The response sentence generation step includes, when the service provision process is started, a first tokenization execution step in which the first model of the stored artificial intelligence algorithm performs a tokenization process on the question about fishing to tokenize a first sentence corresponding to the question about fishing; and, when the tokenization of the first sentence is completed, the first model performs a vectorization process on the first token of the first sentence to calculate a vector value for the first sentence by calculating a vector value for the first token that has been quantified based on the position of the first token in the first sentence, and confirming which of a predetermined number of categories the first sentence belongs to based on the calculated vector value.

[0010] The response sentence generation step includes, when the service provision process is started, a second tokenization execution step in which the first model of the stored artificial intelligence algorithm performs tokenization processing on the detailed information contained in the user information, thereby tokenizing each of the second sentences corresponding to the detailed information contained in the user information, Once the tokenization of each of the second sentences is complete, the first model performs a vectorization process on each of the second tokens in the second sentences to calculate a vector value for each of the second sentences, which is a numerical representation of the second token based on the position of the second token. The vector value for each of the second sentences is then classified into weighted values ​​to be applied to the vector value of the first sentence, and further includes a weighted value calculation and classification step of classifying them into a predetermined number of categories.

[0011] The aforementioned predetermined multiple categories are categories in which representative vector values ​​are matched for each of the multiple categories and which include multiple reference sentences that are candidate groups for responding to the fishing-related questions. The reference category information is updated by the administrator of the fishing information provision platform, along with the representative vector values ​​that have been matched for each of the multiple categories, and which include multiple reference sentences for each of the multiple categories.

[0012] The response sentence generation step further includes a response sentence derivation step, which, once the calculation of the vector values ​​of the first sentence and the second sentence is complete, starts a vector value analysis step in which the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, and once the execution of the vector value analysis step is complete, the function of each reference vector value of the reference sentences included in the category containing the first sentence from among the predetermined plurality of categories The method includes: a reference sentence identification step of comparing the vector value of the first sentence with the vector value of the first sentence, reflecting the vector value of the second sentence classified as the weighted value in the vector value of the first sentence, and identifying a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected; and a sentence derivation completion step of, once the identification of the reference sentence is complete, checking the portion of the identified reference sentence that is identified as the basis for the question about fishing, then proceeding with a summarization process to summarize the reference sentence using the first model, documenting the summarized reference sentence, and completing the derivation of it as a response sentence to answer the question about fishing.

[0013] The customized fishing information provision step includes, once the generation of the response sentence is complete, an image information identification step in which a second model of the stored artificial intelligence algorithm analyzes the question about fishing and the response sentence and identifies at least one image from among a plurality of image information stored in the image database that corresponds to the result of the analysis of the question about fishing and the response sentence; and a chatbot base information provision step in which, once the identification of the image is complete, customized fishing information including the identified image and the response sentence is generated and the customized fishing information is provided to the user account by a chatbot system linked to the fishing information provision platform.

[0014] The stored artificial intelligence algorithm includes a first model, which is a large-scale language model that learns a first pattern value derived by processing natural language data of reference sentences included in a predetermined set of categories, different fishing questions for a predetermined set of categories, other user information registered in other user accounts that have provided other fishing questions, and other response sentences that respond to other fishing questions reflecting other user information, and analyzing their correlations; and a second model, which is an image retrieval model that learns a second pattern value derived by analyzing the correlations between reference images included in a predetermined set of categories, different fishing questions for a predetermined set of categories, other response sentences that respond to other fishing questions reflecting other user information, other fishing questions, and other images corresponding to other response sentences.

[0015] A method for providing a chatbot service that outputs customized fishing information, which is embodied in a computing device including one or more processors and one or more memories for storing commands executable by the processors, the method comprising: a process initiation step in which, upon receiving a question about fishing from a user account registered as a member of a fishing information provision platform, the user information registered in the user account is confirmed, and a service provision process for providing customized fishing information in response to the question about fishing is initiated based on the confirmed user information; and once the service provision process has been initiated, the stored artificial intelligence algorithm analyzes the question about fishing, reflects the detailed information contained in the user information in a vector weighted value, and The system is characterized by comprising: a response statement generation step of generating a response statement to answer a question about fishing; an image normalization progress step of using the stored artificial intelligence algorithm to identify images corresponding to the question about fishing and the response statement as the generation of the response statement is completed, and proceeding with normalization processing of the identified image if the identified image satisfies predetermined tuning conditions; and a customized fishing information provision step of generating customized fishing information for the question about fishing based on the normalized image and the response statement when the normalization processing of the image is completed, and outputting the customized fishing information to the user account using the chatbot system of the fishing information provision platform.

[0016] The aforementioned process initiation step, upon receiving a question about fishing from the user account, includes a detailed information verification step that confirms the detailed information contained in the user information registered in the user account, which includes environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information. The process includes, once the execution of the detailed information verification step is complete, an analysis initiation step which starts a service provision process to generate and provide customized fishing information by analyzing the fishing-related questions based on the verified detailed information.

[0017] The response sentence generation step includes, when the service provision process is started, a first tokenization execution step in which the first model of the stored artificial intelligence algorithm performs a tokenization process on the question about fishing to tokenize a first sentence corresponding to the question about fishing; and, when the tokenization of the first sentence is completed, the first model performs a vectorization process on the first token of the first sentence to calculate a vector value for the first token, which is a numerical representation of the first token in the first sentence based on the position of the first token, and a vector value-based category identification step in which the first sentence is included among a predetermined number of categories based on the calculated vector value.

[0018] The response sentence generation step further includes: a second tokenization execution step in which, when the service provision process is started, the first model of the stored artificial intelligence algorithm performs a tokenization process on the detailed information contained in the user information to tokenize each of the second sentences corresponding to the detailed information contained in the user information; and a weighted value calculation and classification step in which, once the tokenization of each of the second sentences is completed, the first model performs a vectorization process on the second tokens of each of the second sentences, calculates a vector value for each of the second sentences by calculating a vector value for the second token that has been quantified based on the position of the second token in each of the second sentences, classifies the vector values ​​for each of the second sentences into weighted values ​​to be applied to the vector values ​​of the first sentence, and classifies them into a predetermined number of categories.

[0019] The aforementioned predetermined multiple categories are categories that include multiple reference sentences, which are candidate groups for responding to the fishing-related questions, with representative vector values ​​matched for each of the multiple categories. The reference category information is updated by the administrator of the fishing information provision platform, along with the representative vector values ​​matched for each of the multiple categories, and includes multiple reference sentences for each of the multiple categories.

[0020] The response sentence generation step further includes a response sentence derivation step, which, once the calculation of the vector values ​​of the first sentence and the second sentence is complete, starts a vector value analysis step in which the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, and once the execution of the vector value analysis step is complete, the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the reference sentences included in the category containing the first sentence, among the predetermined plurality of categories The method includes: a reference sentence identification step of comparing the value with the vector value of the first sentence, reflecting the vector value of the second sentence classified as the weighted value into the vector value of the first sentence to identify a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected; and a sentence derivation completion step of, once the identification of the reference sentence is complete, checking the portion of the identified reference sentence that is identified as the basis for the question about fishing, then proceeding with a summarization process to summarize the reference sentence using the first model, documenting the summarized reference sentence, and completing the derivation of a response sentence to answer the question about fishing.

[0021] The image normalization progress step includes, once the image identification is complete, a normalization process start step in which the normalization process is started if it is determined that the predetermined tuning conditions are met and the normalization process is started if an abnormal region exists in the identified image, and a normalization correction completion step in which, once the normalization process has started, the pixel value distribution for the abnormal region in the image is identified, and the brightness and contrast of the identified pixel value distribution are adjusted to correct the abnormal region based on the remaining normal region.

[0022] The stored artificial intelligence algorithm includes a first model, which is a large-scale language model that learns a first pattern value derived by processing natural language data of reference sentences included in a predetermined set of categories, different fishing questions for a predetermined set of categories, other user information registered in other user accounts that have provided other fishing questions, and other response sentences that respond to other fishing questions reflecting other user information, and analyzing their correlations; and a second model, which is an image retrieval model that learns a second pattern value derived by analyzing the correlations between reference images included in a predetermined set of categories, different fishing questions for a predetermined set of categories, other response sentences that respond to other fishing questions reflecting other user information, other fishing questions, and other images corresponding to other response sentences.

[0023] A device that provides a chatbot service that outputs customized fishing information, which is embodied in a computing device including one or more processors according to another aspect of the present invention and one or more memories for storing commands executable by the processors, and when it receives a question about fishing from a user account registered as a member of a fishing information provision platform, it checks the user information registered in the user account and starts a service provision process to provide customized fishing information in response to the question about fishing based on the checked user information, and when the service provision process is started, it uses a stored artificial intelligence algorithm to provide customized fishing information The system includes: a response sentence generation unit that analyzes the question and reflects the detailed information contained in the user information in a vector weighting value to generate a response sentence to the question about fishing; and a customized fishing information provision unit that, as the generation of the response sentence is completed, if the stored artificial intelligence algorithm identifies an image corresponding to the question about fishing and the response sentence, generates customized fishing information for the question about fishing based on the identified image and the response sentence, and outputs the customized fishing information to the user account via the chatbot system of the fishing information provision platform.

[0024] A device that provides a chatbot service that outputs customized fishing information, which is embodied in a computing device including one or more processors according to another aspect of the present invention and one or more memories for storing commands executable by the processors, and when it receives a question about fishing from a user account registered as a member of a fishing information provision platform, it checks the user information registered in the user account and starts a service provision process to provide customized fishing information in response to the question about fishing based on the checked user information, and when the service provision process is started, it analyzes the question about fishing using a stored artificial intelligence algorithm and reflects the detailed information contained in the user information in a vector weighted value The system is characterized by including: a response sentence generation unit that generates a response sentence to answer the question about fishing; an image normalization unit that, as the generation of the response sentence is completed, uses the stored artificial intelligence algorithm to identify images corresponding to the question about fishing and the response sentence, and when the identified images satisfy predetermined tuning conditions, proceeds with normalization processing on the identified images; and a customized fishing information provision unit that, when the normalization processing on the images is completed, generates customized fishing information for the question about fishing based on the normalized image and the response sentence, and outputs the customized fishing information to the user account via the chatbot system of the fishing information provision platform.

[0025] A computer-readable recording medium according to another aspect of the present invention, wherein the computer-readable recording medium stores a command for causing a computing device to perform the following steps: when receiving a fishing-related question from a user account registered as a member of a fishing information providing platform, checking user information registered in the user account, and based on the checked user information, starting a service providing process for providing customized fishing information for the fishing-related question; a process start step; when the service providing process is started, analyzing the fishing-related question by a stored artificial intelligence algorithm, reflecting detailed information included in the user information in a vector weight value, and generating a response sentence for responding to the fishing-related question; a response sentence generation step; as the generation of the response sentence is completed, when an image corresponding to the fishing-related question and the response sentence is identified by the stored artificial intelligence algorithm, generating customized fishing information for the fishing-related question based on the identified image and the response sentence, and outputting the customized fishing information by a chatbot system of the fishing information providing platform to provide it to the user account; a customized fishing information providing step, characterized by including.

[0026] A computer-readable recording medium according to another aspect of the present invention, wherein the computer-readable recording medium stores a command for causing a computing device to perform the following steps, the steps comprising: when receiving a fishing-related question from a user account registered as a member of a fishing information providing platform, confirming user information registered in the user account; and based on the confirmed user information, starting a service providing process for providing customized fishing information for the fishing-related question, which is a process start step; when the service providing process is started, analyzing the fishing-related question by a stored artificial intelligence algorithm, and generating a response sentence for responding to the fishing-related question by reflecting detailed information included in the user information in a vector weighting value, which is a response sentence generation step; as the generation of the response sentence is completed, identifying an image corresponding to the fishing-related question and the response sentence by the stored artificial intelligence algorithm, and when the identified image meets a predetermined tuning condition, proceeding with a normalization process for the identified image, which is an image normalization proceeding step; when the normalization process for the image is completed, generating customized fishing information for the fishing-related question based on the normalized image and the response sentence, and outputting the customized fishing information by a chatbot system of the fishing information providing platform to provide it to the user account, which is a customized fishing information providing step.

Effect of the Invention

[0027] By the method of providing a chatbot service for outputting customized fishing information of the present invention, accurate information that meets the user's requirements is provided, and the user can be provided with visual information together, making it easier to understand.

[0028] Also, by providing customized fishing information in real time by the chatbot, a response desired by the user can be provided without being restricted by time.

Brief Description of the Drawings

[0029] [Figure 1] Figure 1 is a flowchart illustrating a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 2] Figure 2 is a flowchart illustrating the process initiation step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 3] Figure 3 is a block diagram illustrating the response text generation unit of a device that provides a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 4] Figure 4 is another block diagram illustrating the response text generation unit of a device that provides a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 5] Figure 5 is a flowchart illustrating the response text derivation step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 6] Figure 6 is a flowchart illustrating the customized fishing information provision step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 7] Figure 7 is a block diagram illustrating a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 8] Figure 8 is a flowchart illustrating the image normalization process steps for a method of providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention. [Figure 9] Figure 9 is a diagram illustrating an example of the internal configuration of a computing device according to an embodiment of the present invention. [Modes for carrying out the invention]

[0030] Various embodiments and / or modes will be described below with reference to the drawings. In the following description, numerous specific details are disclosed for illustrative purposes to aid in the general understanding of one or more modes. However, it will be recognized by those ordinary skill in the art of the present invention that these modes can also be carried out without such specific details. The following description and accompanying drawings detail specific exemplary modes of one or more modes. However, these modes are illustrative, and some of the various methods of the principles of various modes are available, and the description is intended to include all such modes and their equivalents.

[0031] The terms "embodiments," "examples," "modes," and "exemplifications" used herein do not necessarily mean that any mode or design described is superior to or has advantages over other modes or designs.

[0032] Furthermore, the terms “includes” and / or “includes” should be understood to mean that the feature and / or component in question is present, but not to exclude the presence or addition of one or more other features, components, and / or groups thereof.

[0033] Furthermore, terms including ordinal numbers, such as "first," "second," etc., are used to describe various components, but the components are not limited by such terms. The terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may be referred to as the first component. The terms "and" and / or include a combination of multiple related items, or any of the multiple related items.

[0034] Furthermore, in embodiments of the present invention, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as generally understood by those with ordinary skill in the art to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as ideally or excessively formal unless explicitly defined in embodiments of the present invention.

[0035] Figure 1 is a flowchart illustrating a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0036] As shown in Figure 1, a method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, includes a process start step (S101), a response statement generation step (S103), and a customized fishing information provision step (S105).

[0037] In the following description, the method for providing a chatbot service that outputs customized fishing information according to each embodiment of the present invention is understood to be performed by an apparatus that provides a chatbot service that outputs customized fishing information according to each embodiment of the present invention shown in Figures 3 to 4 and 7 (hereinafter referred to as "the apparatus of the present invention"), and / or a computing device shown in Figure 9. That is, the apparatus of the present invention is understood to be realized by combining one or more computing devices shown in Figure 9.

[0038] In step S101, when one or more processors (hereinafter referred to as "processors") receive a question about fishing from a user account registered as a member of the fishing information provision platform, they verify the user information registered in the user account and, based on the verified user information, initiate a service provision process to provide customized fishing information in response to the question about fishing.

[0039] According to the embodiment, the fishing information provision platform is a platform linked to a chatbot system, and is a platform for providing customized fishing information to users registered as members through the chatbot of the chatbot system.

[0040] According to one embodiment, when the processor receives a question about fishing from a user account, it can verify the user information registered in the user account.

[0041] In this regard, user information is information that includes detailed information created by the user of the user account, and includes environmental weather information (temperature, humidity, precipitation, sunshine amount at the aquaculture site), aquatic product growth information (growth rate, size, number of individuals, etc.), marine environment information (water temperature, salinity, currents, etc.), aquatic product distribution information (sales price, sales volume), and aquatic product log information (records of aquaculture work, time, weather, harvest yield, etc.).

[0042] According to the embodiment, once the user information has been verified, the processor analyzes the fishing-related questions received from the user account based on the detailed information contained in the verified user information, and performs a service provision process for providing customized fishing information.

[0043] According to the embodiment, the service provision process is a process for providing a service that outputs the customized fishing information to a user account via a chatbot.

[0044] According to the embodiment, when the service provision process is started, the processor performs the response statement generation step (S103).

[0045] In step S103, when the service provision process is started, the processor can use a stored artificial intelligence algorithm to analyze the question about fishing, reflect the detailed information contained in the user information in a vector weighting value, and generate a response statement to answer the question about fishing.

[0046] According to the embodiment, when the service provision process is initiated, the processor analyzes the question regarding the fishing using the stored artificial intelligence algorithm.

[0047] According to the embodiment, the processor analyzes the fishing-related questions using the stored artificial intelligence algorithm and calculates vector values ​​for the fishing-related questions.

[0048] Here, the processor can use the stored artificial intelligence algorithm to convert the detailed information contained in the user information into vector weights and reflect the vector weights in the vector values ​​of the fishing-related questions.

[0049] In other words, the processor analyzes the fishing-related questions based on the detailed information contained in the user information, and generates a response statement to the fishing-related questions based on the detailed information contained in the user information.

[0050] In this regard, the response sentence is a sentence intended to respond to the question regarding fishing based on the detailed information contained in the user information, and is a sentence that summarizes the answer and related news derived by the stored artificial intelligence algorithm.

[0051] According to the embodiment, once the processor has completed generating the response statement, it performs the fishing information provision step (S105).

[0052] In step S105, as the generation of the response sentence is completed, the processor, if the stored artificial intelligence algorithm identifies an image corresponding to the fishing question and the response sentence, generates customized fishing information for the fishing question based on the identified image and the response sentence, and outputs the customized fishing information to the user account via the chatbot system of the fishing information provision platform.

[0053] According to the embodiment, once the processor has finished generating the response statement, it analyzes the question about fishing and the response statement using the stored artificial intelligence algorithm.

[0054] More specifically, the processor can analyze the fishing-related questions and response sentences using the stored artificial intelligence algorithms to identify images containing objects that include the characteristics of the target corresponding to the keywords included in the fishing-related questions and response sentences.

[0055] According to the embodiment, once the image identification is complete, the processor generates customized fishing information including the identified image and the response statement. Here, the generated customized fishing information includes content and images generated based on the results of analyzing fishing-related questions using user information as a basis.

[0056] According to the embodiment, once the processor has completed generating the customized fishing information, it can provide the user account with content and images based on the customized fishing information by outputting the generated customized fishing information via a chatbot in a chatbot system linked to the fishing information provision platform.

[0057] Figure 2 is a flowchart illustrating the process initiation step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0058] As shown in Figure 2, a method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, includes a process start step (e.g., the process start step (S101) in Figure 1).

[0059] According to the embodiment, the process initiation step is a step in which, upon receiving a question about fishing from a user account registered as a member of the fishing information provision platform, the user information registered in the user account is verified, and based on the verified user information, a service provision process is initiated to provide customized fishing information in response to the question about fishing.

[0060] According to the embodiment, the process initiation step includes, as detailed steps for performing the functions described above, a detailed information confirmation step (S201) and an analysis initiation step (S203).

[0061] In step S201, when one or more processors (hereinafter referred to as "processors") receive a question about fishing from a user account, they can check the detailed information contained in the user information registered in the user account, which includes environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information.

[0062] According to the embodiment, the user information is information recorded and generated by the user of the user account, and includes environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information.

[0063] In this regard, environmental weather information includes information such as temperature, humidity, precipitation, and sunshine at the aquaculture site (or fishing site); aquatic product growth information includes information such as the growth rate, size, and number of aquatic products targeted for aquaculture or fishing; marine environment information includes information such as water temperature, salinity, and currents at the aquaculture site (or fishing site); aquatic product distribution information includes information such as the selling price, sales volume, and distribution routes of aquatic products; and aquatic product log information is information that records the details of aquaculture (or fishing) work for aquatic products targeted for aquaculture or fishing, along with the time and weather conditions, as well as the daily harvest amount.

[0064] According to the embodiment, once the processor has finished verifying the detailed information contained in the user information, it performs the analysis start step (S203).

[0065] In step S203, once the execution of the detailed information confirmation step (S201) is complete, the processor analyzes the fishing-related questions based on the confirmed detailed information and starts a service provision process for generating and providing customized fishing information.

[0066] According to the embodiment, once the processor has completed the verification of the detailed information contained in the user information by executing the detailed information verification step (S201), it starts a service provision process to analyze the fishing-related questions based on the verified detailed information using a stored artificial intelligence algorithm, generate customized fishing information, and provide it to the user account.

[0067] Figure 3 is a block diagram illustrating the response text generation unit of a device that provides a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0068] As shown in Figure 3, the device that provides a chatbot service that outputs customized fishing information, which is implemented in a computing device that includes one or more processors and one or more memories that store commands executable by the processors, includes a response statement generation unit 300 (e.g., the same function as the response statement generation step (S103) in Figure 1).

[0069] According to the embodiment, when the service provision process is started by the process start unit (e.g., the same function as the process start step (S101) in Figure 1), the response statement generation unit 300 analyzes the question 301a regarding fishing using the stored artificial intelligence algorithm 305, reflects the detailed information contained in the user information in the vector weighting value, and generates a response statement to answer the question 301a regarding fishing.

[0070] According to the embodiment, the response statement generation unit 300 includes, as a detailed configuration for performing the above-mentioned functions, a first tokenization execution unit 301 and a vector value-based category identification unit 303.

[0071] According to the embodiment, when the service provision process is started, the first tokenization execution unit 301 performs tokenization processing on the fishing-related question 301a using the first model of the stored artificial intelligence algorithm 305, and tokenizes the first sentence corresponding to the fishing-related question 301a.

[0072] According to the embodiment, the first tokenization execution unit 301 completes the identification of a first sentence, which is the sentence corresponding to the fishing question 301a, by performing natural language processing on the fishing question 301a using the first model of the stored artificial intelligence algorithm 305.

[0073] According to the embodiment, once the identification of the first sentence is complete, the first tokenization execution unit 301 performs the tokenization process on the first sentence.

[0074] In this regard, when the first tokenization execution unit 301 proceeds with the tokenization process, it generally performs morpheme tokenization rather than word tokenization because Korean, unlike English, is an agglutinative language in which morphemes are not composed solely of independent words. The first tokenization execution unit 301 recognizes the multiple morphemes and types of morphemes contained in the first sentence corresponding to the question 301a about fishing, classifies the types of morphemes, recognizes that a combination of an independent morpheme and a dependent morpheme constitutes a single token, and designates it as a single keyword.

[0075] Here, the first tokenization execution unit 301 recognizes either the keyword as one token or the morpheme as one token.

[0076] According to the embodiment, once the tokenization of the first sentence is complete, the vector value-based category identification unit 303 performs a vectorization process on the first token of the first sentence using the first model, calculates a vector value for the first token which has been quantified based on its position in the first sentence, calculates a vector value for the first sentence, and based on the calculated vector value, can confirm which of a predetermined number of categories the first sentence belongs to.

[0077] According to the embodiment, once the execution of the function of the first tokenization execution unit 301 is complete, the vector value-based category identification unit 303 reflects the tokenized morphemes into the first model of the stored artificial intelligence algorithm 305, proceeds with vectorizing the tokens, quantifies them based on their respective occurrence frequency and position within a sentence, and calculates a vector value for each of the tokens.

[0078] According to the embodiment, the vectorization process is carried out by at least one model from among the Bag of Words (BoW) model, TF-IDF model, Word2Vec model, GloVe model, and BERT model. The said model is also part of a model included in a stored large-scale language artificial intelligence algorithm.

[0079] In this regard, the Bag of Words (BoW) model is a model that vectorizes words by their frequency. For example, in the sentence "Please tell me the growth rate of the abalone at the farm once," the model extracts the words "once," "farm," "abalone," "growth," "speed," and "tell me," calculates the frequency of each word, and vectorizes them.

[0080] Furthermore, the TF-IDF model compensates for the shortcomings of BoW, as it can vectorize words while considering both word frequency and document importance, assigning higher weights to words with higher document importance. Additionally, the Word2Vec model learns word similarity and vectorizes them, generating vectors while considering word context, thus obtaining more accurate results than BoW or TF-IDF.

[0081] Furthermore, the GloVe model, like Word2Vec, learns and vectorizes word similarities, but unlike Word2Vec, it learns using large amounts of text data. Finally, the BERT model uses a Transformer model to vectorize sentences, and because it generates vectors while considering the context, it yields more accurate results than Word2Vec and GloVe, and is a model that is generally widely used.

[0082] According to the embodiment, once the vector value-based category identification unit 303 has completed calculating the vector value for the first sentence, it performs a similarity calculation process between the calculated vector value and the representative vector values ​​matched to each of the predetermined multiple categories to confirm whether the first sentence is most similar to one of the predetermined multiple categories, and can classify the first sentence into one of the predetermined multiple categories with the highest similarity to the first sentence.

[0083] More specifically, the vector value-based category identification unit 303 normalizes the vector value of the first sentence using the first model of the stored artificial intelligence algorithm 305, and then calculates the similarity by comparing it with representative vector values ​​matched to the predetermined multiple categories using similarity measurement methods such as cosine similarity, Euclidean distance, and Manhattan distance. Based on the calculated similarity, it can identify the category among the predetermined multiple categories that has the representative vector value most similar to the vector value of the first sentence.

[0084] In this regard, the predetermined multiple categories are categories in which representative vector values ​​are matched for each of the multiple categories and which contain multiple reference sentences that are candidate groups for responding to the fishing question 301a, and the reference category information is updated by the administrator of the fishing information provision platform along with the representative vector values ​​that are matched for each of the multiple categories and which contain multiple reference sentences for each of the multiple categories.

[0085] In this regard, the representative vector value matched to each of the predetermined multiple categories is the average value of the vector values ​​of the multiple reference sentences included in each of the predetermined multiple categories.

[0086] For example, among several predetermined categories, the first category is a category related to abalone growth, and includes articles, papers, etc. on abalone growth as multiple reference texts, and the second category is a weather-related category for abalone farms, and includes articles, papers, etc. on the impact of weather on abalone farming as multiple reference texts.

[0087] According to the embodiment, the first model of the stored artificial intelligence algorithm 305 is a model that learns a first pattern value derived by processing in natural language a reference sentence included in a predetermined plurality of categories, a different fishing-related question 301a for a predetermined plurality of categories, other user information registered in other user accounts that have provided other fishing-related questions, and other response sentences that respond to other fishing-related questions that reflect other user information, and analyzing the correlation.

[0088] In this regard, the first model is a Large Language Model (LLM), a deep learning algorithm that recognizes text and various types of content based on knowledge gained from large datasets, and can summarize, translate, predict, and generate. It is a high-level artificial intelligence technology centered on text understanding and analysis, capable of understanding the complexity of natural language, and is a more accurate algorithm than existing machine learning algorithms.

[0089] In this regard, LLM is a neural network architecture that revolutionized natural language processing (NLP) work, and is a comprehensive algorithm that includes tokenization, which divides the input text into small units such as words or subwords; an encoder (e.g., vectorization) that processes the input sentence and represents it in vector form; a decoder that generates an output sentence using the vector output from the encoder; a loss function, which measures the difference between the output sentence generated by the model and the actual sentence, as a function used for training the model; and a learning algorithm that adjusts the model's parameters to minimize the loss function.

[0090] Figure 4 is another block diagram illustrating the response text generation unit of a device that provides a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0091] As shown in Figure 4, the device that provides a chatbot service that outputs customized fishing information, which is implemented in a computing device that includes one or more processors and one or more memories that store commands executable by the processors, includes a response statement generation unit 300 (e.g., the same function as the response statement generation step (S103) in Figure 1).

[0092] According to the embodiment, when the service provision process is started by the process start unit (e.g., the same function as the process start step (S101) in Figure 1), the response statement generation unit 300 analyzes the question 401a regarding fishing using the stored artificial intelligence algorithm 405, reflects the detailed information contained in the user information in the vector weighting value, and generates a response statement to answer the question 401a regarding fishing.

[0093] According to the embodiment, the response statement generation unit 400 includes, as a detailed configuration for performing the above-mentioned functions, a second tokenization execution unit 401 and a weighted value calculation and classification unit 403.

[0094] According to the embodiment, when the service provision process is started, the second tokenization execution unit 401 performs tokenization processing on the detailed information contained in the user information using the first model of the stored artificial intelligence algorithm 405, and tokenizes each of the second sentences corresponding to the detailed information contained in the user information.

[0095] According to the embodiment, the detailed information included in the user information is information created and generated by the user of the user account, and the second tokenization execution unit 401 performs natural language processing on the detailed information included in the user information according to the first model.

[0096] According to the embodiment, the second tokenization execution unit 401 performs natural language processing on the detailed information contained in the user information using the first model, thereby identifying a second sentence which is a sentence corresponding to the detailed information contained in the user information.

[0097] According to the embodiment, once the second tokenization execution unit 401 has completed identifying the second sentence which is a sentence corresponding to the detailed information contained in the user information, it performs tokenization processing on the identified second sentence.

[0098] Here, the second tokenization execution unit 401 recognizes each keyword constituting the second sentence as a single token, or recognizes a single morpheme as a single token.

[0099] According to the embodiment, once the tokenization of each of the second sentences is complete, the weighted value calculation and classification unit 403 performs vectorization processing on each of the second tokens of the second sentences using the first model, thereby calculating a vector value for each of the second sentences, which is a numerical representation of the second token based on its position. The unit then classifies the vector values ​​for each of the second sentences into weighted values ​​to be applied to the vector values ​​of the first sentences, and classifies them into a predetermined number of categories.

[0100] According to the embodiment, the weighted value calculation classification unit 403 performs vectorization processing on each second token of the second sentence using the first model.

[0101] As a result, the weighted value calculation and classification unit 403 completes the calculation of vector values ​​for each of the second sentences by quantifying the vector values ​​for the second tokens contained in each of the second sentences based on their positions in each of the second sentences.

[0102] Here, each vector value of the second sentence calculated is a weighted value applied to the vector value of the first sentence, and is classified into the predetermined multiple categories. The similarity between each vector value of the second sentence and the representative vector value matched to the predetermined multiple categories is compared to classify each of the second sentences into the predetermined multiple categories.

[0103] More specifically, the weighted value calculation and classification unit 403 normalizes the vector values ​​of the second sentences, and then calculates the similarity by comparing them with representative vector values ​​matched to predetermined categories using similarity measurement methods such as cosine similarity, Euclidean distance, and Manhattan distance. Based on the calculated similarity, each of the second sentences can then be classified into predetermined categories.

[0104] Figure 5 is a flowchart illustrating the response text derivation step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0105] As shown in Figure 5, a method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, includes a response statement derivation step (e.g., the process start step (S101) in Figure 1).

[0106] According to the embodiment, the response statement derivation step is a detailed step included in the response statement generation step (e.g., response statement generation step (S103) in Figure 1), which includes a first tokenization execution step (e.g., the same function as the first tokenization execution unit 301 in Figure 3), a vector value-based category identification step (e.g., the same function as the vector value-based category identification unit 303 in Figure 3), a second tokenization execution step (e.g., the same function as the second tokenization execution unit 401 in Figure 4), and a weighted value calculation and classification step (e.g., the same function as the weighted value calculation and classification unit 403 in Figure 4).

[0107] According to the embodiment, the response statement derivation step is performed after the functional execution of the vector value-based category identification step and the weighted value calculation classification step is completed.

[0108] According to the embodiment, the response sentence derivation step includes, as detailed steps for performing the function, a vector value analysis step (S501), a reference sentence identification step (S503), and a sentence derivation completion step (S505).

[0109] In step S501, once the calculation of the vector values ​​of the first sentence and the second sentence is complete, the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the second sentence, and the vector values ​​of the reference sentence included in the category containing the first sentence.

[0110] According to the embodiment, once the processor has completed the execution of the vector value-based category identification step and the weighted value calculation classification step, the first model starts an analysis of the vector values ​​of the first sentence, the respective vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category in which the first sentence is included.

[0111] Here, the method by which the processor analyzes the vector values ​​of the first sentence, the respective vector values ​​of the second sentence, and the vector values ​​of the reference sentence included in the category containing the first sentence is to compare the similarity between the respective vector values ​​of each sentence, or to analyze them using the evaluation metrics included in the first model.

[0112] According to the embodiment, when the processor starts an analysis of the vector values ​​of the first sentence, the respective vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, it performs a reference sentence identification step (S503).

[0113] In step S503, once the execution of the vector value analysis step (S501) is complete, the processor compares the vector value of the first sentence with the respective reference vector values ​​of the reference sentences included in the category containing the first sentence from among the predetermined plurality of categories, reflects the vector value of the second sentence classified as the weighted value into the vector value of the first sentence, and identifies a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected.

[0114] According to the embodiment, when the processor starts analyzing the vector values ​​of the first sentence, the respective vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, according to the first model, it identifies the respective reference vector values ​​of the reference sentences included in the category containing the first sentence from among the predetermined plurality of categories.

[0115] According to the embodiment, once the processor has finished identifying the reference vector values ​​of the reference sentences included in the category containing the first sentence, it reflects the second sentence's vector value in the first sentence's vector value and performs a similarity comparison process between the first sentence's vector value, which now reflects the second sentence's vector value, and the reference vector values ​​of the reference sentences included in the category containing the first sentence.

[0116] According to the embodiment, the processor identifies, based on the results of the similarity comparison process, the reference sentence among the reference sentences included in the category containing the first sentence that has the highest similarity to the vector value of the first sentence, which reflects the vector value of the second sentence.

[0117] According to another embodiment, the processor reflects the vector value of the second sentence classified as the weighted value into the vector value of the first sentence, and when it identifies a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected, it completes the identification of the reference sentence based on an evaluation index included in the first model, rather than performing a similarity comparison process.

[0118] In this regard, the evaluation metrics include BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR (Metric for Evaluation of Translation with Explicit ORdering), Perplexity, Accuracy, F1 Score, and Human Judgment.

[0119] According to the embodiment, once the processor has completed the identification of a reference sentence having a vector value that is highly similar to the vector value of the first sentence in which the weighted value is reflected, it performs the sentence derivation completion step (S505).

[0120] In step S505, once the processor has completed the identification of the reference sentence, it checks the portion of the identified reference sentence that is identified as the basis for the question about fishing, then proceeds with a summarization process to summarize the reference sentence using the first model, documents the summarized reference sentence, and completes the derivation of a response sentence to answer the question about fishing.

[0121] According to the embodiment, once the processor has completed the identification of a reference sentence having a vector value that is highly similar to the vector value of the first sentence, which reflects the weighted value, it checks the portion of the identified reference sentence that is identified as the basis for the question regarding the fishing.

[0122] Here, the processor identifies the relationships between keywords constituting the question about fishing using the first model, and identifies the keywords included in the identified relationships in the base sentence, thereby allowing the processor to check keywords in the base sentence based on the keywords included in the identified relationships.

[0123] According to the embodiment, the processor checks the portion of the reference sentence that is identified as the basis for the question relating to fishing, and then proceeds with a summarization process that summarizes the reference sentence according to the first model.

[0124] In this regard, the processor, using the first model, performs a summarization process that maintains grammatical, vocabulary, and semantic consistency of the papers and news articles by applying tokenization and normalization to the papers and news articles based on the reference sentences. The result of the summarization process is a result that includes portions identified as evidence for the questions concerning fishing.

[0125] According to the embodiment, once the summarization process is complete, the processor documents a summarized reference sentence containing a portion identified as the basis for the question relating to the fishing, and derives it into a response sentence.

[0126] Here, as the aforementioned reference sentence is documented, the derived (or generated) response sentence is a sentence that matches and groups together questions about fishing and their corresponding response sentences.

[0127] Figure 6 is a flowchart illustrating the customized fishing information provision step of a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0128] As shown in Figure 6, a method for providing a chatbot service that outputs customized fishing information, which is embodied in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, includes a customized fishing information provision step (e.g., the customized fishing information provision step (S105) in Figure 1).

[0129] According to the embodiment, the customized fishing information provision step is a step in which, as the generation of the response sentence is completed, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, generates customized fishing information for the fishing question based on the identified images and the response sentence, and outputs the customized fishing information to the user account via the chatbot system of the fishing information provision platform.

[0130] According to the embodiment, the customized fishing information provision step includes, as detailed steps for performing the above-mentioned functions, an image information identification step (S601) and a chatbot base information provision step (S603).

[0131] In step S601, once the generation of the response statement is complete, the one or more processors (hereinafter referred to as processors) analyze the fishing question and the response statement using the second model of the stored artificial intelligence algorithm, and identify at least one image from among the multiple image information stored in the image database that corresponds to the result of the analysis of the fishing question and the response statement.

[0132] According to the embodiment, once the generation of the response statement is complete, the processor analyzes the question about fishing and the response statement using a second model of the stored artificial intelligence algorithm, and identifies an image from among a plurality of image information stored in the image database that corresponds to the analysis result.

[0133] More specifically, the processor can identify from a plurality of images stored in the image database an image containing an object corresponding to a keyword included in the question and response statement relating to the fishing.

[0134] According to the embodiment, the second model of the stored artificial intelligence algorithm is a model that learns a second pattern value derived by analyzing the correlation between reference images included in a predetermined set of categories, different fishing-related questions for the predetermined set of categories, other response sentences responding to other fishing-related questions that reflect other user information, and other images corresponding to other fishing-related questions and other response sentences.

[0135] In this regard, the second pattern value is a pattern value that calculates similarity by comparing the feature vector value of the image with the vector value of the sentence, and is a pattern value that identifies an image containing an object corresponding to a keyword included in the sentence.

[0136] According to the embodiment, once the image identification is complete, the processor performs the chatbot infrastructure information provision step (S603).

[0137] In the chatbot infrastructure information provision step (S603), once the image identification is complete, the processor generates customized fishing information including the identified image and the response text, and provides the customized fishing information to the user account via a chatbot system linked to the fishing information provision platform.

[0138] According to the embodiment, when the image identification is completed by executing the function of the image information identification step (S601), the processor generates customized fishing information including the identified image and the response statement.

[0139] In this regard, once the processor has completed generating the customized fishing information, it outputs the generated customized fishing information via a chatbot in a chatbot system linked to the fishing information provision platform, so that the user of the user account can confirm the image and response text based on the customized fishing information output by the chatbot.

[0140] Figure 7 is a block diagram illustrating a method for providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0141] As shown in Figure 7, a method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, includes a process start unit 701 (e.g., the same function as the process start step (S101) in Figure 1), a response statement generation unit 703 (e.g., the same function as the response statement generation step (S103) in Figure 1), an image normalization progress unit 705, and a customized fishing information provision unit 707 (e.g., the same function as the customized fishing information provision step (S105) in Figure 1).

[0142] According to the embodiment, when the process initiation unit 701 receives a question 701a about fishing from a user account registered as a member of the fishing information provision platform, it verifies the user information registered in the user account and, based on the verified user information, starts a service provision process to provide customized fishing information in response to the question 701a about fishing.

[0143] According to the embodiment, the fishing information provision platform is a platform linked to a chatbot system, and is a platform for providing customized fishing information to users registered as members through the chatbot of the chatbot system.

[0144] According to the embodiment, when the process initiation unit 701 receives a question 701a regarding fishing from the user account, it verifies the user information registered in the user account.

[0145] In this regard, user information includes detailed information created by the user of the user account, such as environmental weather information (temperature, humidity, precipitation, sunshine in the aquaculture site), aquatic product growth information (growth rate, size, number of individuals, etc.), marine environment information (water temperature, salinity, currents, etc.), aquatic product distribution information (sales price, sales volume), and aquatic product log information (records of aquaculture work, time, weather, harvest yield, etc.).

[0146] According to the embodiment, once the user information verification is complete, the process initiation unit 701 analyzes the fishing-related questions 701a received from the user account based on the detailed information contained in the verified user information, and performs a service provision process to provide customized fishing information 707a.

[0147] According to the embodiment, the service provision process is a process for providing a service to a user account that outputs the customized fishing information 707a via a chatbot.

[0148] According to the embodiment, when the service provision process is started, the response statement generation unit 703 analyzes the question 701a regarding fishing using the stored artificial intelligence algorithm 709, reflects the detailed information contained in the user information in the vector weighting value, and generates a response statement to answer the question 701a regarding fishing.

[0149] According to the embodiment, when the service provision process is started, the response sentence generation unit 703 analyzes the question 701a regarding fishing using the stored artificial intelligence algorithm 709.

[0150] According to the embodiment, the response sentence generation unit 703 analyzes the question 701a regarding fishing using the stored artificial intelligence algorithm 709 and calculates a vector value for the question 701a regarding fishing.

[0151] Here, the response sentence generation unit 703 converts the detailed information contained in the user information into a vector weighted value using the stored artificial intelligence algorithm 709, and reflects the vector weighted value in the vector value of the question 701a regarding fishing.

[0152] In other words, the response statement generation unit 703 analyzes the question 701a regarding fishing based on the detailed information contained in the user information, and generates a response statement to the question 701a regarding fishing based on the detailed information contained in the user information.

[0153] In this regard, the response sentence is a sentence for responding to the question 701a regarding fishing, based on the detailed information contained in the user information, and is a sentence that summarizes the response and related news derived by the stored artificial intelligence algorithm 709.

[0154] According to the embodiment, as the generation of the response sentence is completed, the image normalization unit 705 identifies the image 705a corresponding to the question 701a about fishing and the response sentence from the stored artificial intelligence algorithm 709, and if the identified image 705a satisfies predetermined tuning conditions, it can proceed with normalization processing on the identified image. According to the embodiment, once the image normalization unit 705 has finished identifying the image 705a corresponding to the question 701a and the response statement relating to the fishing, it determines whether a region included in the image 705a satisfies the predetermined tuning conditions.

[0155] As a result, if the image normalization unit 705 determines that a region included in the image 705a satisfies the predetermined tuning conditions, it proceeds with the normalization process for the identified image 705a.

[0156] In this regard, the normalization process includes at least one of the following: mean normalization, which normalizes the image pixel values ​​by dividing them by the mean and then multiplying them again by the original size; standard deviation normalization, which normalizes the image pixel values ​​by dividing them by the standard deviation and then multiplying them again by the original size; min-max normalization, which normalizes the image pixel values ​​by dividing them into minimum and maximum values ​​and then multiplying them again by the original size; normalization mapping, which converts each image pixel value to a value between 0 and 1; normalization mapping, which converts the image pixel values ​​to the mean and standard deviation; and PCA (Principal Component Analysis), which extracts and normalizes the main components of the image.

[0157] According to the embodiment, when the normalization process for the image 705a is completed, the customized fishing information providing unit 707 generates customized fishing information 707a for the fishing question based on the normalized image and the response text, and outputs the customized fishing information 707a to the user account via the chatbot system of the fishing information providing platform.

[0158] According to the embodiment, when the customized fishing information providing unit 707 completes the image normalization process and the generation of the response statement, it generates customized fishing information 707a including the normalized image and the response statement.

[0159] Here, the generated customized fishing information 707a is information that includes content generated based on the analysis of fishing-related questions 701a using user information as a basis, and normalized images.

[0160] According to the embodiment, once the customized fishing information provision unit 707 has completed generating the customized fishing information 707a, it can provide the user account with content and images based on the customized fishing information by outputting the generated customized fishing information 707a through a chatbot in a chatbot system linked to the fishing information provision platform.

[0161] In other words, once the customized fishing information provision unit 707 completes the execution of the function of the image normalization progress unit 705, it generates customized fishing information 707a including the normalized image and the response text, and provides the customized fishing information 707a to the user account via a chatbot system linked to the fishing information provision platform.

[0162] Figure 8 is a flowchart illustrating the image normalization process steps for a method of providing a chatbot service that outputs customized fishing information according to an embodiment of the present invention.

[0163] As shown in Figure 8, a method for providing a chatbot service that outputs customized fishing information, embodied in a computing device including one or more processors and one or more memories for storing commands executable by the processors, includes an image normalization progression step (e.g., the same function as the image normalization progression unit 705 in Figure 7).

[0164] According to the embodiment, the image normalization progress step is a step in which, as the generation of the response sentence is completed, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, and if the identified image satisfies predetermined tuning conditions, a normalization process is performed on the identified image.

[0165] According to the embodiment, the image normalization progress step includes, as detailed steps for performing the above-mentioned functions, a normalization process start step (S801) and a normalization correction completion step (S803).

[0166] In step S801, when one or more processors (hereinafter referred to as "processors") have completed the identification of the image, if an abnormal region exists in the identified image, they determine that the predetermined tuning conditions are met and start the normalization process.

[0167] According to the embodiment, once the processor has completed the identification of the image, it can analyze the image using a second model of a stored artificial intelligence algorithm to determine whether there are any abnormal regions in the identified image.

[0168] In this regard, the aforementioned abnormal region includes areas of abnormal resolution, areas of abnormal brightness, areas of object blurring, etc., located within the image.

[0169] According to the embodiment, the processor analyzes not only the multiple images stored in the image database, but also the images contained in the papers and news articles based on the response sentences, and when predetermined tuning conditions are met, it starts normalization processing on the abnormal regions contained in the images contained in the papers and news articles based on the response sentences.

[0170] According to the embodiment, when the normalization process is started, the processor performs the normalization correction completion step (S803).

[0171] In step S803, when the normalization process is started, the processor identifies the pixel value distribution for the abnormal region in the image, and adjusts the brightness and contrast of the identified pixel value distribution to correct the abnormal region based on the remaining normal region.

[0172] According to the embodiment, when normalization processing of the image is started, the processor identifies the pixel value distribution for abnormal regions in the image and adjusts the brightness and contrast of the identified pixel value distribution. Here, the settings to be adjusted can be derived by a second model.

[0173] According to the embodiment, the processor can perform normalization processing corresponding to the tuning conditions among predetermined tuning conditions that satisfy the abnormal region included in the image, and correct the abnormal region based on the normal region of the image.

[0174] Figure 9 is a diagram illustrating an example of the internal configuration of a computing device according to an embodiment of the present invention.

[0175] Figure 9 shows an example of the internal configuration of a computing device according to an embodiment of the present invention. In the following description, descriptions of unnecessary embodiments that overlap with the descriptions of Figures 1 to 8 mentioned above will be omitted.

[0176] As shown in Figure 9, the computing device 10000 includes at least one processor 11100, memory 11200, peripheral device interface 11300, input / output subsystem 11400, power circuit 11500, and communication circuit 11600. Here, the computing device 10000 corresponds to either a user terminal (A) connected to a haptic interface device or the computing device (B).

[0177] Memory 11200 includes, for example, high-speed random-access memory, magnetic disks, SRAM, DRAM, ROM, flash memory, or non-volatile memory. Memory 11200 also includes various data such as software modules, command sets, and other data necessary for the operation of computing device 10000.

[0178] Here, access to memory 11200 from other components such as the processor 11100 and the peripheral device interface 11300 is controlled by the processor 11100.

[0179] The peripheral device interface 11300 connects the input and / or output peripheral devices of the computing device 10000 to the processor 11100 and memory 11200. The processor 11100 performs various functions for the computing device 10000 and processes data by executing software modules or command sets stored in memory 11200.

[0180] The input / output subsystem 11400 connects various input / output peripherals to the peripheral interface 11300. For example, the input / output subsystem 11400 includes a controller for connecting peripherals such as monitors, keyboards, mice, printers, or, if necessary, touchscreens and sensors to the peripheral interface 11300. Alternatively, input / output peripherals can also be connected to the peripheral interface 11300 without going through the input / output subsystem 11400.

[0181] The power circuit 11500 can supply power to all or some of the components of the terminal device. For example, the power circuit 11500 may include a power management system, one or more power sources such as a battery or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, a power status indicator, or any other components for power generation, management, and distribution.

[0182] The communication circuit 11600 enables communication with other computing devices using at least one external port.

[0183] Alternatively, as mentioned above, the communication circuit 11600 can, if necessary, enable communication with other computing devices by transmitting and receiving RF signals, also known as electromagnetic signals, including an RF circuit.

[0184] The embodiment shown in Figure 9 is merely one example of a computing device 10000. The computing device 10000 may omit some of the components shown in Figure 9, or may include further components not shown, or may have a configuration or arrangement that combines two or more components. For example, a computing device for a communication terminal in a mobile environment may include, in addition to the components shown in Figure 9, a touchscreen, sensors, etc., and the communication circuit 11600 may include circuits for RF communication of various communication methods (WiFi, 3G, LTE, Bluetooth, NFC, Zigbee, etc.). The components included in the computing device 10000 can be embodied as hardware, software, or a combination of both hardware and software, including integrated circuits specialized for one or more signal processing or applications.

[0185] The methods according to embodiments of the present invention are embodied in the form of program instructions performed by various computing devices and recorded on a computer-readable medium. In particular, the programs according to these embodiments consist of PC-based programs or applications specifically for mobile terminals. Applications to which the present invention is applied are installed on the user terminal through files provided by a file distribution system. As an example, the file distribution system includes a file transfer unit (not shown) that transfers the files in response to a request from the user terminal.

[0186] The devices described above are embodied by hardware components, software components, and / or combinations of hardware and software components. For example, the devices and components described in the embodiments can be embodied using one or more general-purpose or special-purpose computers, such as a processor, controller, ALU (arithmetic logic unit), digital signal processor, microcomputer, FPGA (field programmable gate array), PLU (programmable logic unit), microprocessor, or any other device capable of executing and responding to instructions. The processing device performs an operating system (OS) and one or more software applications performed on the OS. The processing device can also access, store, manipulate, process, and generate data in response to software execution. For convenience of understanding, it has sometimes been stated that one processing device is used, but a person with ordinary skill in the art will see that the processing device can include multiple processing elements and / or multiple types of processing elements. For example, the processing device includes multiple processors or one processor and one controller. Other processing configurations, such as parallel processors, are also possible.

[0187] Software includes computer programs, code, instructions, or one or more combinations thereof, which can configure a processing unit to perform a desired operation, or instruct the processing unit independently or in combination. Software and / or data can be permanently or temporarily embodied in any type of machine, component, physical device, virtual device, computer storage medium, or device for analysis by the processing unit or for providing instructions or data to the processing unit. Software can also be distributed across networked computing devices and stored or executed in a distributed manner. Software and data are stored on one or more computer-readable recording media.

[0188] The methods according to the embodiments are embodied in the form of program instructions performed by various computer means and recorded on a computer-readable medium. The computer-readable medium includes program instructions, data files, data structures, etc., individually or in combination. The program instructions recorded on the medium may be specifically designed and configured for the embodiments, or may be publicly known and usable by those skilled in the computer software art. Computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as Propticol disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memory. The program instructions include not only machine code produced by a compiler, but also high-level language code executed by a computer using an interpreter or the like. The hardware devices described above are configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

[0189] As described above, although the embodiments are described by limited embodiments and drawings, a person with ordinary skill in the art can make various modifications and variations from the above description. For example, suitable results can be achieved even if the described technology is performed in a procedure different from the described method, and / or if the components of the described system, structure, apparatus, circuit, etc. are combined or combined in a manner different from the described method, or are substituted or replaced by other components or equivalents. Therefore, other embodiments, other embodiments, and equivalents to the claims also fall within the scope of the claims described below. [Explanation of symbols]

[0190] 300...Response statement generation unit, 301...First tokenization execution unit, 301a...Question about fishing, 303...Vector value-based category identification unit, 305...Stored artificial intelligence algorithm, 10000...Computing device, 11100...Processor, 11200...Memory, 11300...Peripheral device interface, 11400...Input / output subsystem, 11500...Power circuit, 11600...Communication circuit

Claims

1. A method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation step involves verifying the user information registered in the user account and, based on the verified user information, initiating a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, the stored artificial intelligence algorithm analyzes the question about fishing, reflects the detailed information contained in the user information in the vector weighting values, and generates a response sentence to answer the question about fishing (response sentence generation step). A method for providing a chatbot service that outputs customized fishing information, comprising: a customized fishing information provision step, in which, upon completion of generating the response sentence, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, generates customized fishing information for the fishing question based on the identified images and the response sentence, and the chatbot system of the fishing information provision platform outputs the customized fishing information and provides it to the user account.

2. The aforementioned process initiation step is: When a question about fishing is received from a user account, a detailed information verification step is performed to check the detailed information contained in the user information registered in the user account, such as environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information. A method for providing a chatbot service that outputs customized fishing information according to claim 1, comprising: an analysis initiation step, in which, upon completion of the execution of the detailed information confirmation step, an analysis initiation step is initiated to analyze the questions regarding fishing based on the confirmed detailed information and to initiate a service provision process for generating and providing customized fishing information.

3. The response statement generation step is as follows: When the service provision process is initiated, a first tokenization execution step is performed in which the first model of the stored artificial intelligence algorithm performs tokenization processing on the fishing-related question, thereby tokenizing a first sentence corresponding to the fishing-related question. A method for providing a chatbot service that outputs customized fishing information according to claim 1, comprising: a vector value-based category identification step, in which, once the tokenization of the first sentence is completed, the first model performs a vectorization process on the first token of the first sentence to calculate a vector value for the first sentence by quantifying the first token based on its position in the first sentence, and then confirming which of a predetermined plurality of categories the first sentence belongs to based on the calculated vector value.

4. The response statement generation step is as follows: When the service provision process is initiated, a second tokenization execution step is performed in which the first model of the stored artificial intelligence algorithm performs tokenization processing on the detailed information contained in the user information, thereby tokenizing each of the second sentences corresponding to the detailed information contained in the user information. A method for providing a chatbot service that outputs customized fishing information according to claim 3, further comprising: a weighted value calculation and classification step, which, once the tokenization of each of the second sentences is completed, performs a vectorization process on each of the second tokens of the second sentences using the first model to calculate a vector value for each of the second sentences, which is a numerical representation of the second token based on the position of the second token; classifies the vector values ​​for each of the second sentences into weighted values ​​to be applied to the vector values ​​of the first sentences, and classifies them into a predetermined number of categories.

5. A method for providing a chatbot service that outputs customized fishing information according to claim 4, characterized in that the predetermined multiple categories are categories in which representative vector values ​​are matched for each of the multiple categories and which include a group of reference sentences that are candidates for responding to the questions about fishing, and the reference category information is updated along with the representative vector values ​​that have been matched for each of the multiple categories by the administrator of the fishing information provision platform, along with the group of reference sentences included for each of the multiple categories.

6. The response statement generation step further includes a response statement derivation step, The response statement derivation step is as follows: Once the calculation of the vector values ​​of the first sentence and the second sentence is complete, the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, in a vector value analysis step. Once the execution of the vector value analysis step is complete, the following steps are performed to identify a reference sentence: compare the reference vector value of each reference sentence included in the category containing the first sentence from among the predetermined plurality of categories with the vector value of the first sentence, reflect the vector value of the second sentence classified as a weighted value in the vector value of the first sentence, and identify a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected. A method for providing a chatbot service that outputs customized fishing information according to claim 4, comprising: a sentence derivation completion step, in which, after the identification of the reference sentence is completed, the portion of the identified reference sentence identified as the basis for the question regarding fishing is checked, the first model proceeds with a summarization process to summarize the reference sentence, the summarized reference sentence is documented, and the derivation is completed as a response sentence to answer the question regarding fishing.

7. The aforementioned customized fishing information provision step is: Once the generation of the response sentence is complete, the second model of the stored artificial intelligence algorithm analyzes the question about fishing and the response sentence, and identifies at least one image from among a plurality of image information stored in the image database that corresponds to the result of the analysis of the question about fishing and the response sentence, in an image information identification step. A method for providing a chatbot service that outputs customized fishing information according to claim 1, comprising: a chatbot infrastructure information provision step of generating customized fishing information including the identified image and the response text once the identification of the image is completed, and providing the customized fishing information to the user account via a chatbot system linked to the fishing information provision platform.

8. The stored artificial intelligence algorithm is, A first model is a large-scale language model that learns a first pattern value derived by processing natural language data of reference sentences included in predetermined multiple categories, different fishing-related questions for predetermined multiple categories, other user information registered in other user accounts that have provided other fishing-related questions, and other response sentences that respond to other fishing-related questions that reflect other user information, and analyzing their correlations. A method for providing a chatbot service that outputs customized fishing information according to claim 1, comprising: a reference image included in a predetermined set of categories; different fishing-related questions for the predetermined set of categories; other response sentences responding to other fishing-related questions that reflect other user information; other fishing-related questions; and a second model which is an image search model that learns a second pattern value derived by analyzing the correlation between other images corresponding to other response sentences.

9. A method for providing a chatbot service that outputs customized fishing information, which is implemented in a computing device including one or more processors and one or more memories for storing commands that can be executed by the processors, When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation step involves verifying the user information registered in the user account and, based on the verified user information, initiating a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, the stored artificial intelligence algorithm analyzes the question about fishing, reflects the detailed information contained in the user information in the vector weighting values, and generates a response sentence to answer the question about fishing (response sentence generation step). As the generation of the response sentence is completed, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, and if the identified images satisfy predetermined tuning conditions, the image normalization process proceeds by performing a normalization process on the identified images. A method for providing a chatbot service that outputs customized fishing information, comprising: a customized fishing information provision step, which, once the normalization process for the image is completed, generates customized fishing information for the question about fishing based on the normalized image and the response text, and has the chatbot system of the fishing information provision platform output the customized fishing information and provide it to the user account.

10. The aforementioned process initiation step is: When a question about fishing is received from a user account, a detailed information verification step is performed to check the detailed information contained in the user information registered in the user account, such as environmental weather information, fishery product growth information, marine environment information, fishery product distribution information, and fishery product log information. A method for providing a chatbot service that outputs customized fishing information according to claim 9, comprising: an analysis initiation step, which, once the execution of the detailed information confirmation step is complete, analyzes the questions about fishing based on the confirmed detailed information and initiates a service provision process for generating and providing customized fishing information.

11. The response statement generation step is as follows: When the service provision process is initiated, a first tokenization execution step is performed in which the first model of the stored artificial intelligence algorithm performs tokenization processing on the fishing-related question, thereby tokenizing a first sentence corresponding to the fishing-related question. A method for providing a chatbot service that outputs customized fishing information according to claim 9, comprising: a vector value-based category identification step, in which, once the tokenization of the first sentence is completed, the first model performs a vectorization process on the first token of the first sentence to calculate a vector value for the first token, which is quantified based on the position of the first token in the first sentence; a vector value for the first sentence is calculated, and based on the calculated vector value, the first sentence is confirmed to belong to one of a predetermined plurality of categories.

12. The response statement generation step is as follows: When the service provision process is initiated, a second tokenization execution step is performed in which the first model of the stored artificial intelligence algorithm performs tokenization processing on the detailed information contained in the user information, thereby tokenizing each of the second sentences corresponding to the detailed information contained in the user information. A method for providing a chatbot service that outputs customized fishing information according to claim 11, further comprising: a step of calculating and classifying weighted values, wherein once the tokenization of each of the second sentences is completed, the first model performs a vectorization process on each of the second tokens of the second sentence, calculates a vector value for each of the second sentences by quantifying the second token based on the position of the second token, classifies the vector values ​​for each of the second sentences into weighted values ​​to be applied to the vector values ​​of the first sentences, and classifies them into a predetermined plurality of categories.

13. A method for providing a chatbot service that outputs customized fishing information according to 12, characterized in that the predetermined plurality of categories are categories that include a plurality of reference sentences which are candidate groups for responding to questions about fishing, with representative vector values ​​matched for each of the plurality of categories, and the plurality of reference sentences included for each of the plurality of categories are updated by the administrator of the fishing information provision platform along with the representative vector values ​​matched for each of the plurality of categories.

14. The response statement generation step further includes a response statement derivation step, The response statement derivation step is as follows: Once the calculation of the vector values ​​of the first sentence and the second sentence is complete, the first model of the stored artificial intelligence algorithm starts an analysis of the vector values ​​of the first sentence, the vector values ​​of the second sentence, and the vector values ​​of the reference sentences included in the category containing the first sentence, in a vector value analysis step. Once the execution of the vector value analysis step is complete, the following steps are performed to identify a reference sentence: compare the reference vector value of each reference sentence included in the category containing the first sentence from among the predetermined plurality of categories with the vector value of the first sentence, reflect the vector value of the second sentence classified as a weighted value in the vector value of the first sentence, and identify a reference sentence having a reference vector value that is highly similar to the vector value of the first sentence with the weighted value reflected. A method for providing a chatbot service that outputs customized fishing information according to 12, comprising: a sentence derivation completion step, in which, after the identification of the reference sentence is completed, the first model proceeds with a summarization process to summarize the reference sentence, the summarized reference sentence is documented, and the derivation of a response sentence to respond to the fishing question is completed.

15. The aforementioned image normalization process steps are as follows: Once the identification of the image is complete, if an abnormal region exists within the identified image, it is determined that the predetermined tuning conditions are met, and the normalization process is started in the normalization process start step. A method for providing a chatbot service that outputs customized fishing information according to claim 9, characterized in that, once the normalization process is started, the chatbot service includes a normalization correction completion step of identifying the pixel value distribution for abnormal regions in the image, adjusting the brightness and contrast of the identified pixel value distribution to correct the abnormal regions based on the remaining normal regions.

16. The stored artificial intelligence algorithm is, A first model is a large-scale language model that learns a first pattern value derived by processing natural language data of reference sentences included in predetermined multiple categories, different fishing-related questions for predetermined multiple categories, other user information registered in other user accounts that have provided other fishing-related questions, and other response sentences that respond to other fishing-related questions that reflect other user information, and analyzing their correlations. A method for providing a chatbot service that outputs customized fishing information according to claim 9, comprising: a reference image included in a predetermined set of categories; different fishing-related questions for the predetermined set of categories; other response sentences responding to other fishing-related questions that reflect other user information; other fishing-related questions; and a second model which is an image search model that learns a second pattern value derived by analyzing the correlation between other images corresponding to other response sentences.

17. A device that provides a chatbot service that outputs customized fishing information, which is implemented in a computing device that includes one or more processors and one or more memories that store commands executable by the processors, When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation unit verifies the user information registered in the user account and, based on the verified user information, initiates a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, a response statement generation unit analyzes the fishing-related questions using a stored artificial intelligence algorithm, reflects the detailed information contained in the user information in a vector weighting value, and generates a response statement to answer the fishing-related questions. A device that provides a chatbot service that outputs customized fishing information, comprising: a customized fishing information providing unit that, upon completion of generating the response sentence, identifies images corresponding to the fishing question and the response sentence using the stored artificial intelligence algorithm, generates customized fishing information for the fishing question based on the identified images and the response sentence, and outputs the customized fishing information to the user account using the chatbot system of the fishing information providing platform.

18. A device that provides a chatbot service that outputs customized fishing information, which is implemented in a computing device that includes one or more processors and one or more memories that store commands executable by the processors, When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation unit verifies the user information registered in the user account and, based on the verified user information, initiates a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, a response statement generation unit analyzes the fishing-related questions using a stored artificial intelligence algorithm, reflects the detailed information contained in the user information in a vector weighting value, and generates a response statement to answer the fishing-related questions. As the generation of the response sentence is completed, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, and if the identified images satisfy predetermined tuning conditions, the image normalization unit proceeds with normalization processing on the identified images. A device that provides a chatbot service for outputting customized fishing information, comprising: a customized fishing information providing unit that, upon completion of normalization processing on the aforementioned image, generates customized fishing information for the question about fishing based on the normalized image and the response text, and outputs the customized fishing information to the user account via the chatbot system of the fishing information provision platform.

19. A computer-readable recording medium, The computer-readable recording medium stores a command to cause a computing device to perform the following steps, the steps being: When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation step involves verifying the user information registered in the user account and, based on the verified user information, initiating a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, the stored artificial intelligence algorithm analyzes the question about fishing, reflects the detailed information contained in the user information in the vector weighting values, and generates a response sentence to answer the question about fishing (response sentence generation step). A computer-readable recording medium comprising a customized fishing information provision step, in which, upon completion of generating the response sentence, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, generates customized fishing information for the fishing question based on the identified images and the response sentence, and the chatbot system of the fishing information provision platform outputs the customized fishing information and provides it to the user account.

20. A computer-readable recording medium, The computer-readable recording medium stores a command to cause a computing device to perform the following steps, the steps being: When a question about fishing is received from a user account registered as a member of the fishing information provision platform, the process initiation step involves verifying the user information registered in the user account and, based on the verified user information, initiating a service provision process to provide customized fishing information in response to the question about fishing. When the service provision process is initiated, the stored artificial intelligence algorithm analyzes the question regarding fishing, and generates a response sentence to answer the question regarding fishing by reflecting the detailed information contained in the user information in a vector weighting value. As the generation of the response sentence is completed, the stored artificial intelligence algorithm identifies images corresponding to the fishing question and the response sentence, and if the identified images satisfy predetermined tuning conditions, the image normalization process proceeds by performing a normalization process on the identified images. A computer-readable recording medium characterized by including a customized fishing information provision step, which, once the normalization process for the image is completed, generates customized fishing information for the question about fishing based on the normalized image and the response text, and has the chatbot system of the fishing information provision platform output the customized fishing information and provide it to the user account.

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