AI Content Keyword Timestamping for Precise Navigation
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Solution Overview
Problem
Users face difficulty in locating specific parts of recorded content data, such as lectures, due to the lack of efficient methods to navigate and play back desired segments within large datasets.
Innovation Solution
An electronic device equipped with an input unit, memory, and processor that analyzes content data to extract keywords and associate them with timestamps, allowing users to input commands to search and play specific parts of the content based on keyword matching and weighting, with the option to visualize the content structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search through large amounts of recorded content data, then they can find specific topics, but it becomes difficult and time-consuming as the dataset grows larger
Solution Approach 1:
The system performs preliminary analysis of content data during recording, automatically extracting keywords and generating timestamps for each keyword occurrence. This pre-processing creates an indexed structure that enables rapid retrieval without manual searching through the entire dataset.
Solution Approach 2:
Keywords serve as intermediaries between the user's search query and the actual content data. Instead of directly searching through raw audio or video content, users search through extracted keywords that represent key concepts, significantly reducing the search space and time required to locate specific topics.
2Measurement precision
If the system analyzes all content data to provide accurate keyword matching, then retrieval precision improves, but processing complexity and time increase
Solution Approach 1:
The system applies different processing levels to different parts of the content data. Rather than uniformly analyzing all content at maximum detail, it extracts keywords selectively based on local characteristics such as speech patterns, topic transitions, and importance weighting, reducing overall processing complexity while maintaining retrieval accuracy.
Solution Approach 2:
The system changes parameters of content analysis dynamically, adjusting keyword extraction sensitivity, matching thresholds, and processing depth based on the specific characteristics of the content being analyzed. This allows accurate retrieval without consistently applying maximum processing complexity to all datasets.
Data Source
AI summary
Disclosed are an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. The present disclosure provides an electronic device comprising: an input unit for receiving content data; a memory for storing information on the content data; an audio output unit for outputting the content data; and a processor, which acquires a plurality of data keywords by analyzing the inputted content data, matches and stores time stamps, of the content data, respectively corresponding to the plurality of acquired keywords, based on a user command being inputted, searches for a data keyword corresponding to the inputted user command among the stored data keywords, and plays the content data based on the time stamp corresponding to the searched data keyword.