Data Snippet Tagging for Faster Event Review and Storage
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Solution Overview
Problem
Reviewing or investigating events in large data files such as video feeds, audio clips, images, and documents is laborious, time-consuming, and inefficient due to the need to pause and document significant events, with current approaches failing to effectively manage large file sizes and irrelevant information.
Innovation Solution
A computing system allows users to interact with data files by tagging entities during playback, saving snippets, and associating them with metadata, which can be stored and analyzed within an enterprise data platform, aligning and augmenting snippets with transactional data, and optimizing storage based on access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users review or investigate events in a data file by pausing and documenting manually, then they can track significant events, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent segments the continuous data file into discrete snippets based on detected events or user selections. Each snippet represents a meaningful portion of the data file that can be independently analyzed and stored, eliminating the need to review the entire file manually.
Solution Approach 2:
The system performs preliminary analysis of the data file to automatically detect and mark significant events before user review. This pre-processing creates a structured framework that guides user attention to relevant portions, reducing manual search time.
2Loss of information
If users pause the data file to document events, then they can record timelines, but the review process becomes cumbersome and ineffective
Solution Approach 1:
The system automatically generates event documentation including timestamps, locations, and descriptions without requiring manual intervention. The automated snippet generation and metadata extraction perform the documentation function that would otherwise require user effort.
Solution Approach 2:
The patent replaces manual documentation operations with automated computational processes. The system uses algorithms to detect events, extract relevant information, and create structured records, substituting mechanical user actions with automated digital processing.
3Loss of information
If the entire data file is archived, then all information is preserved, but storage efficiency decreases due to large file sizes and irrelevant information
Solution Approach 1:
The system extracts only the relevant portions of the data file that contain significant events or user-selected content. These extracted snippets are stored with associated metadata, while the rest of the data file can be discarded or archived separately, significantly reducing storage requirements.
Solution Approach 2:
Different portions of the data file are treated differently based on their significance. Snippets containing important events are stored in high-detail formats with comprehensive metadata, while non-critical portions are either excluded or stored in compressed formats.
4Loss of information
If manual documentation of events is performed, then event timelines can be tracked, but productivity decreases
Solution Approach 1:
The system provides automated feedback by generating event detections, snippet recommendations, and metadata suggestions during the review process. This feedback loop allows users to quickly verify and adjust automated findings without starting from scratch, significantly improving productivity.
Data Source
AI summary
Systems and methods are provided for generating and storing data snippets. A data file can be obtained from a data source through a plug-in interface. The data file can be played to a user through an application running on a computing device. An indication can be received from the user, through the application, to tag an entity depicted in the data file during playback of the data file. A snippet of the data file can be stored, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.