4 results about "Interview methods" patented technology

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 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.

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.