9 results about "Interview methods" patented technology

The invention provides an assessment interview method and device for enterprise employees and a storage medium, and the method comprises the steps: determining an initial problem in a preset assessment interview problem library based on the post type and historical performance data of the enterprise employees; questioning by using the initial question so as to receive answer information of the enterprise employees; determining the semantic integrity of the answer information; if the semantic integrity is smaller than a preset integrity threshold value, generating a question-chasing question of the initial question according to a semantic element type missing in the answer information; asking questions by utilizing the question-asking questions to receive supplementary answer information of the enterprise employees; and according to the answer information, supplementing the answer information, and determining an assessment interview evaluation result of the enterprise employees. According to the method and the device, the questioning content can be adjusted according to the post types and the historical performance data of the enterprise employees, and the questioning can be selectively performed according to the answer information of the enterprise employees, so that the questioning of the assessment interviews is more accurate and deep, and the accuracy of the result obtained by the assessment interviews is improved.

This invention relates to the field of artificial intelligence interview system technology, specifically to an integrated AI interview method and system based on interpretable deep assessment. The method includes: collecting and simultaneously preprocessing the interviewee's audio and video data using an integrated device; analyzing non-verbal behavioral features and speech semantic content features using a multimodal deep learning model; inputting the extracted multimodal features into an attention-based interpretable fusion assessment model for deep correlation and weighted calculation to generate an assessment of non-cognitive abilities; and finally outputting a structured assessment report including a comprehensive score, multidimensional analysis, and citations of key evidence fragments. This invention, through deep multimodal fusion and interpretability technology, achieves an objective and in-depth assessment of candidates' soft skills, and makes the assessment results transparent and traceable, effectively improving the accuracy, credibility, and practical value of AI interviews.

The application relates to the technical field of artificial intelligence and psychological interviews, in particular to an emotional conversation intelligent interview method and system, which comprises the following steps: collecting voice, text and micro-expression features through a multi-modal sensing unit; constructing a semantic-emotion coupling feature model, monitoring the mapping relationship between semantic logic and emotional fluctuation and calculating a conflict coefficient; when the semantic feedback and the emotional valence are negatively correlated, triggering a contradiction seeking logic; using a heterogeneous graph converter to construct interview multi-dimensional features into a heterogeneous graph, generating a context vector through an attention mechanism; generating a guiding prompt word with an emotional regulation factor based on the vector, guiding the interviewee to release real psychological motivation. The application can accurately identify the real intention hidden under the disguise, eliminate individual expression differences, balance deep mining and empathy injection, and realize self-adaptive and high-precision collection of interview information.

The invention discloses an intelligent interview method and device, and a storage medium, and relates to the technical field of interview evaluation, and the method comprises the steps: carrying out the question screening analysis of a target post question bank based on an interview resume, target post recruitment information, and a plurality of target assessment dimensions, obtaining target assessment questions corresponding to each target assessment dimension of an interviewer, and obtaining the target assessment questions corresponding to each target assessment dimension; and each target evaluation standard. Receiving answer voices of the interviewer for each target assessment question, aggregating the answer voices under the same target assessment dimension through a capability assessment large model, and determining a target answer voice text in each target assessment dimension; and performing interview assessment analysis on the corresponding target answer voice text by using each target assessment standard through the ability assessment large model to obtain each target first score, and determining the interview score of the interviewer, thereby solving the technical problem of ability misjudgment of the interviewer caused by strong subjectivity of interview assessment in traditional interviews, and improving the interview experience of the interviewer. And the interview evaluation efficiency and fairness are improved.

The application relates to the field of intelligent interviews, and discloses an intelligent interview method and device based on a large language model, equipment and a storage medium, which are used for fusing a large language model to realize intelligent and accurate interviews. The method comprises the following steps: acquiring post descriptions and candidate resume information, generating a dynamic post portrait and an interview cognitive graph; relying on a pre-trained first large language model, combining a current state of the cognitive graph to determine a deep exploration strategy and a current deep evaluation target; generating interactive content through a pre-trained second large language model, receiving candidate text, acoustic and visual multi-modal response information; analyzing the multi-modal response to obtain corresponding features, and updating the interview cognitive graph; judging whether a cognitive convergence threshold is reached, recursively asking questions if the threshold is not reached, and promoting the interview or ending the interview if the threshold is reached; after the interview is ended, generating a main evaluation conclusion based on a final cognitive graph, questioning and debating through a pre-trained third large language model, and generating a comprehensive evaluation report containing core arguments of the pro and con sides.

This application relates to the field of artificial intelligence technology, disclosing an AI interview method, electronic device, storage medium, and program product. The method includes: acquiring interview text; determining the candidate's first speaking speed and first pause frequency based on the interview text; the first pause frequency describing the number of pauses during the candidate's input of the interview audio corresponding to the interview text; determining the semantic integrity probability of the interview text; determining the probability that the candidate will not continue speaking based on the first speaking speed, the first pause frequency, and the semantic integrity probability; and determining whether to continue asking questions based on the probability that the candidate will not continue speaking. This allows the AI ​​interviewer to adaptively adjust the timing of continuing to ask questions according to the candidate's speaking style, enabling the AI ​​interviewer to take over the conversation at an appropriate time, thereby improving the candidate's interview experience.

The invention relates to the technical field of talent evaluation, in particular to a school recruitment talent evaluation system and method based on an immersive training camp and a multi-dimensional portrait, and the system comprises a precise interview preliminary screening module which is used for carrying out the preliminary screening of candidates through a behavior interview method, and an immersive training camp management module which is configured to simulate a real working scene, the method comprises the following steps: organizing candidates to participate in a training task for several days, a multi-dimensional behavior data acquisition module for recording behavior indexes of the candidates in training in real time, including speaking duration, collaborative proposal times and emotional stability data, a talent portrait generation module, analyzing the behavior data based on an algorithm model, and a decision support module, according to the school recruitment talent evaluation system and method based on the immersive training camp and the multi-dimensional portrait, a two-stage evaluation mechanism is adopted, candidates are rapidly screened through a behavior interview method, and then the comprehensive ability of the candidates is observed by simulating the training camp depth of a real working scene.

The invention discloses an AI interview method and system based on a multi-modal large model. The method comprises the steps that 1, an interview problem is automatically generated through a problem generation module; 2, in the process that the user answers the question, recording the voice and video of the user through an interview recording module, and sending the voice and video to an answer analysis module; 3, an answer analysis module analyzes the answer of the user to the interview question, and quantitatively scores the question answer condition of the user based on the scoring dimension; step 4, performing comprehensive analysis on the interview process by a scoring and questioning generation module, and generating questioning questions; and 5, performing comprehensive score calculation according to all questions answered by the user, generating an interview report, and sending the interview report to the HR. The invention relates to the technical field of artificial intelligence, and can solve the existing technical problems.

The application discloses an interview method and device based on a video interview system, relates to the technical field of artificial intelligence, and mainly aims at reducing the possibility of artificial review of interview results and improving interview efficiency. The main technical scheme of the application is as follows: obtaining recruitment requirements of a target post, resume information of a candidate and a post evaluation result; generating a current interview question based on a capability item of the recruitment requirements and asking the candidate the question so as to collect, through a video interaction collection module, the answer content of the candidate to the current interview question and facial emotional information when the candidate answers; determining the effectiveness of the current answer according to the answer content and the facial emotional information; determining a target question determination strategy in a question follow-up strategy and a question promotion strategy according to whether the answer effectiveness meets preset effectiveness requirements, and determining a next interview question according to the target question determination strategy until a preset question asking deadline requirement is met; and generating an interview result based on all the interview questions, the answer content and the answer effectiveness.