Machine-Learned Content Selection for Real-Time Collection Integration

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Solution Overview

Problem

Assembling relevant content for user requests in real-time is challenging due to time constraints and the risk of displaying irrelevant content, leading to user dissatisfaction and loss of viewers.

Innovation Solution

Implementing a machine learning classifier to generate relevancy scores for content items based on historical user data, selecting the most relevant content within 200 milliseconds for immediate integration and transmission, ensuring minimal delay and user engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If content is assembled on-the-fly in real-time, then content relevance to user is improved, but response time delay increases

Engineering Contradiction:

Improvecontent relevanceVSAvoidresponse time delay

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system pre-generates multiple candidate content assemblies in advance using machine learning models trained on historical user data. When a user requests content, the system selects from pre-prepared options rather than assembling content in real-time, thus eliminating assembly delay while maintaining relevance through ML-based prediction of user preferences

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adapts content selection based on real-time user context and historical behavior patterns. Machine learning models continuously refine content recommendations by analyzing user interactions, allowing the system to adjust content relevance without requiring time-consuming assembly processes

Inventive Principle:

Principle #15Dynamics

2Productivity

If content assembly is performed quickly, then response time is reduced, but content relevance may deteriorate

Engineering Contradiction:

Improvecontent assembly speedVSAvoidcontent relevance

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system creates multiple candidate content assemblies in advance using machine learning predictions of what content users are likely to prefer. These pre-generated copies are stored and can be rapidly delivered without assembly delay, maintaining relevance through ML-based content selection rather than real-time assembly

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system replaces the mechanical process of real-time content assembly with machine learning-based content selection. Instead of assembling content when requested, the system uses trained models to predict and select relevant content from pre-generated options, substituting computational ML processes for time-consuming assembly operations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250322316A1Real-time content integration based on machine learned selections

Publication Date: 2025.10.16 SNAP INC

AI summary

A candidate content item is identified for integration into a content collection. The candidate content item is associated with a first value. Using at least one machine learning model, a select value and a skip value are automatically generated for the candidate content item. The select value indicates a likelihood that the user will select the candidate content item, and the skip value indicates a likelihood that the user will bypass the candidate content item. A second value is generated for the candidate content item based on the first value, the select value, and the skip value. The candidate content item is automatically selected from a plurality of candidate content items based on the second value meeting at least one predetermined criterion. The selected candidate content item is then automatically integrated into the content collection, which is caused to be presented on a device of a user.