Neural Network for Digital Media Metadata Anomaly Prediction
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Solution Overview
Problem
Current systems for creating digital media compilations, such as videos from photos and videos, face challenges in efficiency, quality, sequencing, and content enrichment, particularly in handling metadata anomalies and user automation, leading to a limited value proposition for users.
Innovation Solution
A machine learning model with a neural network is trained to predict data anomalies, enrich content, and automate the handling of metadata, grouping, and sequencing of digital media, using techniques like facial recognition, landmark detection, and object detection to improve media quality and organization, reducing user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning models are used to predict data anomalies and enrich content, then productivity and ease of operation are improved, but device complexity increases
Solution Approach 1:
The system divides the video creation process into distinct functional modules: an anomaly detection module that identifies problematic metadata, a content enrichment module that adds missing information using machine learning, and a media processing module that handles the actual video compilation. This segmentation allows each module to specialize in specific tasks, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediary processing layers between raw media input and final video output. Machine learning models serve as intermediaries that analyze metadata, predict anomalies, and enrich content before the main video compilation process. This intermediary approach automates complex analysis tasks, significantly improving productivity without requiring end users to directly manage the complexity.
2Manufacturing precision
If comprehensive anomaly detection and content enrichment are implemented, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary anomaly detection and content enrichment actions before the main video compilation process. Machine learning models pre-analyze metadata, predict potential anomalies, and enrich content with missing information in advance. This preliminary action ensures high manufacturing precision by identifying and correcting issues early, while the complexity is contained in separate preprocessing stages rather than complicating the core video creation process.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated machine learning-based anomaly detection and content enrichment systems. Instead of requiring human operators to manually inspect and enhance each media file (which would be extremely complex), neural networks automatically perform these tasks with high precision, substituting computational intelligence for manual processing while maintaining quality standards.
3Manufacturing precision
If user feedback loops are implemented for content selection, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service capabilities where machine learning models automatically perform content selection, anomaly detection, and enrichment tasks without requiring continuous user intervention. The neural networks self-adjust based on predicted anomalies and automatically enrich content, improving selection accuracy while reducing the operational burden on users. The system serves itself by making autonomous decisions based on learned patterns from training data.
Data Source
AI summary
Systems, methods, and other embodiments for selecting, enriching and sequencing digital media content to produce a narrative-oriented, ordered sub-collection of media such as for movie creation. The method identifies, evaluates, assesses, stores, enriches, groups, and sequences content. The method identifies the content metadata. When metadata are missing or anomalous, the method attempts to populate or correct the metadata and store that new content in the database. The method evaluates content for focus quality and may exclude content based on rules. The method assesses the content storing the people and their emotional level, animals, objects, locations, landmarks and date/time in the database. The method can then enrich the remaining content by providing map, photo, video, text, and audio content. The method uses selecting criteria for grouping and sequencing content by date, time, person, etc. and compiling the sequenced groups into the final narrative ready for distribution, e.g., movie creation.