Temporal Analysis Model for Animated Media Content

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Solution Overview

Problem

Conventional systems fail to effectively extract and convey temporal information from animated media content items, such as GIFs, leading to inadequate categorization and search results, as they do not account for the dynamic nature of these media formats.

Innovation Solution

A computer-implemented method using a machine-learned temporal analysis model that processes data describing a media content item with multiple image frames, outputting temporal analysis data that describes the semantic meaning and emotional content conveyed when the frames are viewed sequentially, rather than individually.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional systems process animated media content item by item without temporal analysis, then processing speed is maintained, but temporal information is lost leading to poor categorization and search results

Engineering Contradiction:

Improvetemporal informationVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system performs preliminary extraction of temporal information from animated media content items before storage or retrieval operations. By pre-processing the content to extract temporal patterns, the system preserves temporal information without adding complexity to the main processing pipeline, as the extraction is done in advance and stored for later use.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary component that bridges conventional media processing systems and temporal analysis capabilities. This intermediary layer handles the complex temporal extraction tasks separately, allowing the main system to remain relatively simple while still benefiting from comprehensive temporal information through the intermediary's analysis and integration functions.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If manual intervention is used to categorize animated media content, then categorization accuracy improves, but time consumption and resource expenditure increase

Engineering Contradiction:

Improvecategorization accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables animated media content items to self-categorize by automatically extracting and analyzing their own temporal information. The content items pass through the system and receive categorization labels based on their inherent temporal patterns, eliminating the need for manual intervention while maintaining accuracy through automated temporal analysis.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical categorization processes with automated computational temporal analysis. Instead of human operators manually reviewing and categorizing content, the system uses computational algorithms to extract temporal features and automatically assign categories, dramatically reducing time consumption while maintaining or improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If individual image frames are analyzed separately, then processing efficiency is maintained, but temporal patterns and emotional content are missed

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtemporal patterns

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system merges multiple individual image frame analyses into a unified temporal analysis process. By combining the processing of sequential frames and analyzing their transitions and patterns together, the system recovers temporal information that would be lost in separate frame analysis while maintaining reasonable processing efficiency through integrated computation.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent applies dynamic analysis methods that adapt to the temporal characteristics of animated content. Rather than using static analysis on each frame, the system dynamically adjusts its processing to capture temporal patterns, transitions, and evolutionary characteristics across frames, preserving temporal information while optimizing processing efficiency for animated content specifically.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20240338925A1Systems and Methods for Extracting Temporal Information from Animated Media Content Items Using Machine Learning

Publication Date: 2024.10.10 GOOGLE LLC

AI summary

1. A computer-implemented method can include receiving, by a computing system including one or more computing devices, data describing a media content item that includes a plurality of image frames for sequential display. The method can include inputting, by the computing system, the data describing the media content item into a machine-learned temporal analysis model that is configured to receive the data describing the media content item, and in response to receiving the data describing the media content item, output temporal analysis data that describes temporal information associated with sequentially viewing the plurality of image frames of the media content item. The method can include receiving, by the computing system and as an output of the machine-learned temporal analysis model, the temporal analysis data.