WO-2026095542-A1 - ELECTRONIC DEVICE FOR EVALUATING SPECIFICITY OF ANSWER IN EMPLOYMENT DOCUMENT AND METHOD FOR EVALUATING SPECIFICITY OF ANSWER IN EMPLOYMENT DOCUMENT USING SAME

WO2026095542A1WO 2026095542 A1WO2026095542 A1WO 2026095542A1WO-2026095542-A1

Abstract

An electronic device for evaluating the specificity of an answer in an employment document according to the present disclosure may comprise: a memory for storing at least one instruction; and at least one processor for executing the at least one instruction. The at least one processor may extract answer data from employment document data, analyze the answer data on the basis of at least four evaluation criteria, and calculate a specificity evaluation score of the answer data. The at least four evaluation criteria may include at least one of sentence length evaluation, keyword evaluation, morpheme evaluation, and personal experience evaluation.

Inventors

HONG, JUNG HO
KIM, DA AE

Assignees

주식회사 무하유

Dates

Publication Date: 20260507
Application Date: 20251028
Priority Date: 20241028

Claims (10)

In an electronic device for evaluating the specificity of answers in recruitment documents, Memory for storing at least one instruction; and It includes at least one processor that executes the above at least one instruction, and The above-mentioned at least one processor is, Extract response data from recruitment document data, and Analyze the above response data based on at least four evaluation criteria, and Calculate the specificity evaluation score of the above answer data, and The above at least four evaluation criteria are, including at least one of sentence length evaluation, keyword evaluation, morphological evaluation, and personal experience evaluation, Electronic device.
In paragraph 1, The above-mentioned at least one processor is, Perform binary classification or natural language processing operations on the above answer data at the sentence level, and Analyzing the above answer data using at least one artificial intelligence model, Electronic device.
In paragraph 1, The above sentence length evaluation is, Divide the above answer data into sentence units to calculate the number of words in each sentence, and By comparing the pre-set standard length range with the sentence length of the above answer data, evaluating the specificity of the answer based on sentence length, Electronic device.
In paragraph 1, The above keyword evaluation is, Extract keywords from the above answer data that include at least one of example keywords, spatiotemporal keywords, performance keywords, and core keywords, and Evaluating the specificity of answers according to keywords based on the usage frequency of the above keywords in the above answer data, Electronic device.
In paragraph 4, The above-mentioned at least one processor is, In the above answer data, the higher the frequency of at least one of the above example keyword, the above time/space keyword, and the above performance keyword, the higher the answer specificity is evaluated, and In the above answer data, the higher the density of the above core keywords, the higher the evaluation of the above answer specificity, Electronic device.
In paragraph 1, The above morpheme evaluation is, Analyze the above answer data at the morpheme level to calculate the ratio of adjectives and adverbs, and In the above answer data, evaluating the specificity of answers according to morphemes based on whether the above ratio of adjectives and adverbs exceeds a preset standard ratio, Electronic device.
In paragraph 1, The above personal experience evaluation is, Using a deep learning model, experience sentences are extracted from the above answer data, and Evaluating the specificity of the answer based on personal experience by evaluating at least one of reliability, initiative, and emotional expression from the above experience sentence, Electronic device.
In paragraph 1, The above-mentioned at least one processor is, Weights are applied to the individual evaluation scores calculated from the above sentence length evaluation, the above keyword evaluation, the above morphological evaluation, and the above personal experience evaluation, and Perform score normalization based on evaluation criteria, and Calculating the specificity evaluation score by summing the normalized scores for each of the above evaluation criteria, Electronic device.
In paragraph 8, The above-mentioned at least one processor is, Scaling the scores for each evaluation criterion by standardizing them using [Formula 1] below so that the mean of the scores for each evaluation criterion is 0 and the standard deviation of the scores for each evaluation criterion is 1, [Formula 1] x' = (X - μ)/σ (Here, x' is the normalized score, X is the raw score for each evaluation criterion, μ is the mean of the scores for each evaluation criterion, and σ is the standard deviation of the scores for each evaluation criterion) Electronic device.
In a method performed by the processor of a device, Step of extracting response data from recruitment document data; A step of analyzing the above-mentioned answer data based on at least four evaluation criteria; and, The method includes the step of calculating a specificity evaluation score for the above-mentioned answer data; The above at least four evaluation criteria are, including at least one of sentence length evaluation, keyword evaluation, morphological evaluation, and personal experience evaluation, Method for evaluating the specificity of answers in recruitment documents.

Description

Electronic device for evaluating the specificity of answers in recruitment documents and method for evaluating the specificity of answers in recruitment documents using the same The present disclosure relates to a technology for evaluating the specificity of a response, and more specifically, to an electronic device for evaluating the specificity of an applicant's response in a recruitment document and a method for evaluating the specificity of a response in a recruitment document using the same. Recruitment documents are crucial materials used to comprehensively evaluate an applicant's capabilities, experience, and achievements during the hiring process. In particular, documents such as personal statements and career summaries serve as key sources of information for assessing who the applicant is and whether they possess the competencies suitable for the job. Traditionally, the analysis of these documents has been performed manually by recruiters and has been regarded as the most important initial step in evaluating an applicant's qualifications. In recruitment documents, the specificity of an applicant's responses serves as a crucial factor in assessing their sincerity and competence. The more specific the response, the clearer one can understand the applicant's experience and tangible contributions, which helps determine if they are a suitable candidate for the job. Conversely, if the response is not specific, it may give the impression that the applicant failed to properly explain their abilities or lacks sincerity. Therefore, evaluating the specificity of responses is a critical process in the analysis of recruitment documents. Traditional methods for reviewing application documents relied on recruiters manually analyzing each document one by one. However, this approach is not only subjective but also time-consuming and inefficient when reviewing a large volume of documents. In particular, the criteria for judging the specificity of an applicant's responses may be unclear or inconsistent. Accordingly, there is a need for an automated system that enables HR personnel to review application documents more quickly and consistently, and to objectively evaluate the specificity of responses. FIG. 1 is a drawing showing the block configuration of an electronic device of the present disclosure. FIG. 2 is a conceptual diagram illustrating the operation of an electronic device of the present disclosure. FIG. 3 is a flowchart illustrating the operation of the electronic device of the present disclosure. FIG. 4 is a flowchart illustrating the operation of an electronic device of the present disclosure analyzing answer data based on sentence length evaluation. FIG. 5 is a flowchart illustrating the operation of an electronic device of the present disclosure analyzing answer data based on keyword evaluation. FIG. 6 is a flowchart illustrating the operation of an electronic device of the present disclosure analyzing answer data based on morphological evaluation. FIG. 7 is a flowchart illustrating the operation of an electronic device of the present disclosure analyzing response data based on a personal experience evaluation. FIG. 8 is a flowchart illustrating the operation of an electronic device of the present disclosure to calculate a specificity evaluation score of answer data. Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components. Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network. Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components. The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms. Singular expressions include plural expressions unless there is an obvious exception in the context. In each step, identification codes are used for convenience