Abstract: The present disclosure relates to a method, device, and computer program capable of detecting whether a brand logo is illegally being used by an unauthorized person using an image and text of the brand logo. The present disclosure provides a detection technique capable of accurately detecting illegal use of a brand logo even when variously transformed forms and background images are used to avoid legal use detection. According to embodiments of the present disclosure, since the logo identification model can be generated through the machine learning dataset based on the logo transformation images and the image illegal use information can be generated using the logo identification model, it is possible to more accurately detect the illegal use of the logo that is used in variously transformed forms. In addition, since the illegal use information on the logo text can be considered in addition to the logo image, it is possible to further increase the accuracy of the illegal use detection.

Abstract: A method of detecting a brand theft according to an embodiment of the present disclosure includes: acquiring protected object data related to a brand to be protected, the protected object data including brand character data and brand logo image data related to a brand, and CDN keyword data indicating a source in which the brand logo image data is stored; acquiring crawled data by crawling an e-commerce web page; parsing the crawled data to acquire first data corresponding to a title or content of the web page, second data corresponding to an image, and third data related to a source of the image; analyzing each of the first data, the second data, and the third data to detect whether the protected object data is included in the crawled data for the web page; and monitoring e-commerce websites based on the detection result.

Abstract: A method of detecting a brand theft according to an embodiment of the present disclosure includes: acquiring protected object data related to a brand to be protected, the protected object data including brand character data and brand logo image data related to a brand, and CDN keyword data indicating a source in which the brand logo image data is stored; acquiring crawled data by crawling an e-commerce web page; parsing the crawled data to acquire first data corresponding to a title or content of the web page, second data corresponding to an image, and third data related to a source of the image; analyzing each of the first data, the second data, and the third data to detect whether the protected object data is included in the crawled data for the web page; and monitoring e-commerce websites based on the detection result.

Abstract: The present disclosure relates to a method of generating, by a terminal, an integrated graph using a distributed graph including: receiving source node information from a user; identifying, by a graph types module, a location of a graph database related to a type of a source node based on the source node information; requesting, by a graph manager module, graph data to an application corresponding to the location of the graph database; receiving the graph data from the application; and integrating, by a graph integration module, a node and an edge based on the source node and the graph data to generate the integrated graph.

Abstract: A method of extracting the semantic distance from the mathematical sentence and classifying the mathematical sentence by the semantic distance, includes: receiving a user query; extracting at least one keyword included in the received user query; and extracting a semantic distance by, indexing one or more of natural language tokens and mathematical equation tokens including semantic information, extracting the semantic distance, between the at least one extracted keyword and the one or more indexed semantic information by referring indexed information, and acquiring a similarity of the received user query and the semantic information.

Abstract: The present disclosure relates to a method and apparatus for inputting equations. An equation input apparatus includes a learning context recognizer for extracting learning context information regarding a learner; a target equation group extractor for generating a target equation group including target mathematical symbols based on the extracted learning context information; and a predicted equation provider for providing a predicted mathematical symbol list relating to information, input or selected from the target equation group by user command, and for reflecting an equation selected from the predicted mathematical symbol list in the input or selected information.

Abstract: The present disclosure provides a mathematics education service system includes a problem storing unit to store at least one mathematical problem; a transform method executing unit to execute a plurality of problem transform methods; a problem separating unit to collect equations used in the mathematical problem, separate one or more terms from the collected equations by parsing the collected equations, and separate a constant and a variable from each of the separated one or more terms; and a problem generating unit to generate another applied problem of the mathematical problem by applying at least one of the problem transform methods (i) to the collected equations, and (ii) to the constant and the variable of each of the separated one or more terms, wherein the system is configured to provide the mathematical problem and the generated another problem are provided to a learner terminal.

Abstract: The present disclosure relates to a method and apparatus for inputting equations. An equation input apparatus includes a learning context recognizer for extracting learning context information regarding a learner; a target equation group extractor for generating a target equation group including target mathematical symbols based on the extracted learning context information; and a predicted equation provider for providing a predicted mathematical symbol list relating to information, input or selected from the target equation group by user command, and for reflecting an equation selected from the predicted mathematical symbol list in the input or selected information.

Abstract: A method of extracting the semantic distance from the mathematical sentence and classifying the mathematical sentence by the semantic distance, includes: receiving a user query; extracting at least one keyword included in the received user query; and extracting a semantic distance by, indexing one or more of natural language tokens and mathematical equation tokens including semantic information, extracting the semantic distance, between the at least one extracted keyword and the one or more indexed semantic information by referring indexed information, and acquiring a similarity of the received user query and the semantic information.

Abstract: The present disclosure provides an apparatus and method for processing a natural language and a mathematical formula. The apparatus includes a natural language and mathematical formula input unit configured to receive a natural language and a mathematical formula inputted; an information generation unit configured to generate parsing semantic information of the mathematical formula from combined data composed of the natural language combined with the mathematical formula; an operation information extraction unit configured to extract operation information generated by using a logical condition from the combined data; a natural language and mathematical formula structuralizing unit configured to analyze, classify in terms of specific meaning and recombine the combined data; an operation structuralizing unit configured to structuralize the operation information; and a natural language and mathematical formula indexing unit configured to index the combined data.

Abstract: The present disclosure relates to an apparatus and method for diagnosing a learning ability. The apparatus for diagnosing learning ability includes a receiving unit configured to receive from a terminal chapter-related information or problem-related information which is desirably diagnosed by a learner, and a semantic information generator configured to generate, responsive to each piece of problem information included in the chapter-related information or problem-related information, semantic information with structural information of the problem information, the problem information having subject-specific problem information distinguished from the semantic information.