Transcript-Based Video Rewinds for Accurate Playback Jumps
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Solution Overview
Problem
Adjusting video playback based on fixed time intervals often results in overshooting or undershooting the desired timestamp, requiring multiple manual adjustments, which worsens the user experience.
Innovation Solution
A computing system applies machine learning models to a video transcript and user data to dynamically interpret user intent, identifying and ranking noncurrent timestamps based on individual preferences, allowing for intelligent and accurate playback adjustments without specifying exact timestamps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If video playback is adjusted based on fixed time intervals, then the playback adjustment process is simple, but it results in overshooting or undershooting the desired timestamp, requiring multiple manual adjustments
Solution Approach 1:
The system changes the parameter basis for playback adjustment from fixed time intervals to dynamic intervals determined by machine learning models. The models analyze user preferences, video content characteristics, and contextual information to generate adaptive rewind/fast-forward intervals that precision-target meaningful video segments, eliminating overshooting while maintaining operational simplicity
Solution Approach 2:
The patent replaces the mechanical fixed-interval playback adjustment system with an intelligent system using machine learning models. These models process user data, video transcripts, and playback history to dynamically determine optimal playback positions, substituting rigid mechanical timing with adaptive intelligent decision-making
2Measurement precision
If users manually adjust playback position multiple times to reach desired timestamp, then timestamp accuracy can be achieved, but user experience deteriorates due to repeated adjustments
Solution Approach 1:
The system performs preliminary actions by pre-analyzing video content and user preferences to predict the optimal playback position before the user actually requests playback adjustment. The machine learning models prepare targeted rewind/fast-forward positions in advance, so when the user initiates playback adjustment, the system can directly jump to the predicted desired timestamp in a single action
Solution Approach 2:
The system implements feedback mechanisms by analyzing user playback adjustment patterns and continuously learning from user behavior. The machine learning models use this feedback to refine predictions of user intent, progressively improving timestamp accuracy with each interaction and reducing the number of adjustments needed over time
3Device complexity
If fixed time interval playback adjustment is used, then the system is simple to implement, but it does not account for individual user preferences
Solution Approach 1:
The system transitions from static fixed-interval playback adjustment to dynamic adaptive playback adjustment. Machine learning models continuously adapt playback parameters based on real-time analysis of user preferences, video content, and contextual factors, making the system flexible and responsive to individual user needs while maintaining a unified implementation architecture
Data Source
AI summary
A computing system receives a transcript for a video and an input indicative of a request to adjust a playback position of the video, in which the request does not specify a timestamp of the video to which to adjust the playback position. The computing system applies, based on the request to adjust the playback position, a first machine learning model to the transcript and a current timestamp of the video to identify one or more noncurrent time stamps. The computing system applies a second machine learning model to the transcript, the current timestamp, and the one or more noncurrent time stamps to rank, based on user data, the one or more noncurrent time stamps. The computing system then adjusts, based on the ranking of the one or more noncurrent timestamps, the playback position to a noncurrent timestamp from the one or more noncurrent timestamps.