Content Delivery Service Event Detection and Context Generation

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Solution Overview

Problem

Traditional content streaming services are limited in automatically selecting and packaging content streams based on context, requiring manual selection and processing, and are unable to dynamically adjust content distribution based on real-time events or user preferences.

Innovation Solution

A content delivery service that processes multiple inputs to detect significant events, associates context information with these events, and dynamically generates content streams tailored to user profiles and preferences, using on-demand code execution to reduce latency and enhance content delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If manual selection and processing of content streams is used, then content delivery can be controlled, but automation extent is low and productivity is reduced

Engineering Contradiction:

Improveautomatic content stream selectionVSAvoidsystem complexity for event detection

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system automatically detects events in content streams and generates context information without human intervention. The event detection component autonomously processes content streams, identifies significant events, and creates context records that drive content distribution decisions, enabling the system to serve itself rather than requiring manual configuration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-processes content streams to detect events and generate context information before content distribution is needed. By performing event detection and context generation in advance, the system prepares contextual data that can be quickly utilized for real-time content packaging and distribution decisions.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If dynamic content stream generation based on real-time events is implemented, then adaptability improves, but processing time and complexity increase

Engineering Contradiction:

Improvedynamic content distributionVSAvoidcontent processing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system divides content streams into discrete segments or chunks for processing. The event detection component analyzes content in manageable segments rather than processing entire streams at once, identifying events at segment boundaries. This segmentation enables parallel processing and reduces the time required for event detection while maintaining adaptability to real-time events.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system processes only the necessary portions of content streams to detect significant events rather than analyzing every detail. The event detection component focuses on identifying key events that trigger content distribution decisions, performing partial processing that is sufficient for adaptive content delivery without the overhead of complete stream analysis.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of information

If context-based content packaging is used, then content relevance to users improves, but manufacturing precision requirements increase

Engineering Contradiction:

Improvecontext information retentionVSAvoidcontent stream packaging accuracy

Core Design Contradiction:

Loss of informationVSManufacturing precision

Solution Approach 1:

The system extracts only the essential context information from detected events that is necessary for content distribution decisions. The context generation component identifies and extracts key event attributes and contextual data points, separating critical information from extraneous details. This extraction approach maintains content relevance while reducing the complexity and precision requirements of content packaging.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system includes sufficient context information to enable effective content packaging without over-processing. The context generation component captures the necessary event details and contextual data required for accurate content matching, providing adequate but not excessive information that balances relevance with manageable packaging requirements.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11228791B1Automatically processing inputs to generate content

Publication Date: 2022.01.18 AMAZON TECH INC

AI summary

A content delivery service can process requests for content from requesting user devices. The content delivery service can receive multiple inputs from content sources and determine events in the depictions of the inputs. The content delivery service can generate context information based on processing multiple inputs. The content delivery service can generate content streams from the detected events.