Video Insight Generation via LLM Text Representation
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Solution Overview
Problem
Manually generating annotations or context for videos is time-consuming and resource-intensive, and existing machine learning approaches face challenges in efficiently processing and understanding video content due to the need for extensive human annotation and computational resources.
Innovation Solution
The technology generates video insights efficiently by creating a text representation of a video using a large language model, which is then used to automatically generate contextual insights such as emotions, persuasion strategies, topics, actions, and reasons, reducing the need for manual review and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video annotation is performed, then video context and insights can be accurately understood, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent creates text representations (copies) of video content that capture essential contextual information without requiring direct human viewing of the entire video. These text representations serve as simplified proxies that preserve key insights while dramatically reducing the time and resources needed for analysis.
Solution Approach 2:
The patent introduces text representations as an intermediary layer between raw video content and human analysis. Instead of directly analyzing video footage, systems first convert video into text representations, which then serve as the basis for generating insights, thereby reducing the computational and temporal overhead of direct video processing.
2Loss of information
If manual video review is performed to generate annotations, then comprehensive video context is obtained, but computing resources and operational complexity increase
Solution Approach 1:
The patent extracts essential contextual information from video content and consolidates it into text representations. This extraction process isolates the most relevant insights (emotions, persuasion strategies, topics, actions, reasons) from the full video, allowing comprehensive understanding without requiring access to or processing of the entire video stream.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated text-based analysis systems. Instead of human reviewers watching and analyzing video footage, the system automatically generates text representations and derives insights from them, substituting labor-intensive mechanical operations with automated computational processes.
3Productivity
If automated text-based analysis is used, then processing speed and resource efficiency improve, but the ability to understand nuanced video content may be reduced
Solution Approach 1:
The patent performs preliminary conversion of video content into text representations that are optimized for analysis. By pre-processing video into structured text formats that capture essential contextual elements, the system prepares the data in a form that enables both rapid processing and accurate insight generation, balancing speed and precision.
Solution Approach 2:
The patent transforms video data into different parameter representations (text-based descriptors of emotions, topics, actions, etc.). This parameter transformation allows the system to analyze video content through text-based metrics that maintain nuanced understanding while enabling faster, more efficient processing compared to direct video analysis.
Data Source
AI summary
Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating video insights based on text representations of videos. In embodiments, text data associated with a video is obtained. Thereafter, a model prompt to be input into a large language model is generated. The model prompt includes the text data associated with the video. As output from the large language model, a text representation that represents the video in natural language based on the text data is obtained. The text representation is provided as input into a machine learning model to generate a video insight that indicates context of the video.