AI-Guided DVR Recording for Live Broadcast Timing Shifts
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Solution Overview
Problem
Existing digital video recorder (DVR) services rely heavily on electronic program guides (EPGs) for scheduling and recording, which are often inaccurate due to unforeseen changes in live broadcasts, particularly in sports events, leading to incomplete or incorrect recordings.
Innovation Solution
Implementing artificial intelligence (AI)/machine learning (ML) classifiers to analyze the actual content of video streams for detecting events, adjusting recording times, and identifying user-specific objects and interests, enabling intelligent seek and bookmarking capabilities, and creating personalized viewing wish lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DVR services rely on electronic program guides (EPGs) for scheduling and recording, then the system is simple to operate, but the recording accuracy deteriorates due to unforeseen changes in live broadcasts
Solution Approach 1:
The patent introduces an intermediary system consisting of AI/ML classifiers that analyze video stream content and generate corrections to EPG data. These classifiers act as a mediator between the raw broadcast signal and the DVR recording system, detecting actual program start/end times and events, then adjusting EPG-based schedules accordingly. This resolves the contradiction by maintaining the simplicity of EPG-based operation while improving accuracy through automated content analysis.
2Adaptability or versatility
If DVR services use fixed recording schedules based on EPG information, then the device complexity is low, but the adaptability to actual broadcast changes deteriorates
Solution Approach 1:
The patent implements self-service through automated AI/ML classifiers that independently analyze video streams, detect program boundaries and events, and automatically adjust recording schedules without user intervention. The system serves itself by correcting EPG inaccuracies and adapting to broadcast changes autonomously, thereby improving adaptability while minimizing the complexity burden on the user.
Solution Approach 2:
The patent establishes a feedback loop where AI classifiers continuously monitor the video stream during recording, detect actual program content and timing, and feed corrections back to the DVR system to adjust recording parameters in real-time. This feedback mechanism enables the system to adapt to broadcast changes dynamically while maintaining manageable complexity through automated closed-loop control.
3Loss of time
If the DVR records entire broadcasts based on EPG schedules, then the recording process is simple, but the loss of time for users deteriorates due to having to navigate through unwanted content
Solution Approach 1:
The patent applies preliminary action by having AI classifiers analyze and tag video content during the recording phase, identifying key events, scenes, and timestamps before user playback. This pre-processing creates an intelligent index of the recorded content, enabling users to quickly jump to relevant sections during playback without manual navigation, thereby reducing user time loss while managing complexity through upfront automated analysis.
Data Source
AI summary
A media stream is accessed, and a classification model is selected for identifying objects in the media stream. Using the classification model, it is determined whether a content item of interest to the user is currently being received in the media stream. If the content item is currently being received in the media stream, at least a portion of the content item is recorded from the media stream. If the content item is not currently being received, then recording is paused or delayed until it is determined that the content item is being received.