ML-Based Media Timeline Bookmarking

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Solution Overview

Problem

Media item creators face inefficiencies in highlighting interesting content segments for users, as they must manually consume and annotate long media items, wasting resources and time, and may incorrectly predict user interest, leading to ineffective GUI elements over time.

Innovation Solution

A machine learning model is trained on historical media items to predict content segments of interest to users, associating them with bookmarks for easy access, reducing the need for creators to manually highlight segments and increasing resource availability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If creators manually consume and annotate long media items to highlight interesting segments, then the bookmarks can be provided to users for easy access, but it wastes creators' time and computing resources

Engineering Contradiction:

Improveease of accessing interesting content segmentsVSAvoidtime spent by creators on manual annotation

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables automatic time marking of media items by the platform without requiring creator intervention. The platform autonomously analyzes media items, identifies interesting segments, and generates bookmarks, allowing the system to serve itself rather than relying on manual creator input.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of creator annotation with an automated machine learning-based system. The platform uses algorithms to automatically detect and mark interesting content segments, substituting human manual work with computational automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If creators manually annotate interesting segments, then bookmarks can be provided for user access, but creators may incorrectly predict user interest leading to ineffective GUI elements

Engineering Contradiction:

Improveease of accessing interesting content segmentsVSAvoidaccuracy of predicting user interest

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where user interactions with media items are continuously monitored. This feedback is used to refine and update the time markings and bookmarks dynamically, ensuring that the system's predictions of user interest become increasingly accurate over time based on actual user behavior data.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent dynamically adjusts the parameters of interesting segment identification based on user interaction patterns. As user behavior data accumulates, the system modifies its criteria for what constitutes an interesting segment, changing the parameters of the analysis to better match actual user preferences rather than relying on static creator predictions.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If the platform processes long media items to identify interesting segments, then accurate bookmarks can be provided, but computing resources are consumed

Engineering Contradiction:

Improveaccuracy of identifying interesting content segmentsVSAvoidcomputing resources consumed by the platform

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential features and segments from media items that are most likely to be interesting to users. Rather than analyzing every portion of long media items in detail, the platform selectively identifies and processes key segments, reducing the overall computational burden while maintaining identification accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12192550B2Time marking of media items at a platform using machine learning

Publication Date: 2025.01.07 GOOGLE LLC

AI summary

Methods and systems for time marking of media items at a platform using machine learning are provided herein. A media item to be provided to users of a platform is identified. The media item includes two or more content segments. An indication of the identified media item is provided as input to a machine learning model. The machine learning model is trained using to predict, for a given media item, content segments of the given media item depicting an event of interest to the one or more users. One or more outputs of the machine learning model are obtained. The one or more obtained outputs include event data identifying each content segment of the media item and an indication of a level of confidence that each respective content segment depicts an event of interest. In response to determining that at least one content segment is associated with a level of confidence that satisfies a level of confidence criterion, the at least one content segment is associated with a bookmark for a timeline of the media item. The media item and an indication of the bookmark is provided for presentation to the at least one user.