US20250063239
2025-02-20
Electricity
H04N21/8545
The AI-assisted interview system is designed to enhance the process of conducting interviews by providing real-time analysis and suggestions. It utilizes advanced algorithms to evaluate questions, responses, audio, and video during interviews, offering follow-up topics and questions that optimize for both breadth and depth. This system is particularly useful in addressing the challenges faced when interviewing individuals who may not naturally articulate their thoughts clearly, such as experts with deeply internalized knowledge.
The system offers a graphical user interface (GUI) for interviewers to specify information about the interviewee. It generates prompts for a large language model (LLM) to create customized questions based on the interviewee's profile. These questions are then used during the interview, which is recorded in segments. The AI can suggest follow-up questions or rephrase existing ones based on real-time analysis of the interviewee's responses and emotional tone, ensuring a comprehensive exploration of topics.
Interviews are recorded in segments, each corresponding to a specific question. These segments are compiled into an interactive video that includes an index mapping segments to their semantic content and timestamps. This allows users to navigate the video efficiently, focusing on particular areas of interest. The system ensures that interviews are not only interactive but also informative by leveraging AI to guide the conversation dynamically.
The system provides an interactive video player within the GUI for users to access recorded interviews. Users can input questions into a text field, and the system identifies relevant segments by matching semantic vector encodings of user queries with those of recorded segments. If no direct match is found, the system uses retrieval augmented generation techniques to generate responses based on associated documents, ensuring comprehensive answers.
This AI-assisted system addresses common challenges in interviewing by dynamically adapting to the interviewee's responses. It employs natural language processing and sentiment analysis to refine question strategies, making interviews more engaging and effective. By personalizing the interaction based on real-time feedback, it facilitates better communication and knowledge transfer between interviewers and interviewees, particularly benefiting those who struggle with articulating complex ideas.