Shoppable Video URL Normalization for Scalable Product Linking

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Solution Overview

Problem

Existing video corpuses are created in non-scalable ways, limiting the number of shoppable videos and frustrating users who may miss products of interest.

Innovation Solution

Generating shoppable URLs by extracting and normalizing URLs from generic videos, applying filters to reduce noise and duplicates, and presenting them as shoppable links for seamless shopping experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If shoppable videos are created manually with product links embedded, then product shopping experience is enabled, but video creation scalability is limited and production complexity increases

Engineering Contradiction:

Improveshoppable video capabilityVSAvoidvideo creation scalability

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system automatically copies and extracts URL information from video metadata and external sources, transforming generic videos into shoppable videos at scale without manual intervention. This copying approach enables mass conversion of video content while maintaining the shoppable functionality.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs self-service by automatically generating shoppable video capabilities through automated URL extraction, normalization, and integration. The process operates autonomously without requiring manual video editing or link embedding, thereby enabling scalable production.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated URL extraction is applied to generic videos, then video processing productivity increases, but data quality and accuracy may deteriorate due to noise and duplicates

Engineering Contradiction:

Improvevideo processing efficiencyVSAvoidURL data accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system performs preliminary actions by normalizing URLs and removing duplicates before final integration. This preprocessing step ensures that extracted URL data is clean and accurate, preventing quality deterioration from automated extraction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual URL verification with automated normalization and deduplication algorithms. This mechanical substitution maintains data accuracy while enabling high-volume processing of video metadata.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Manufacturing precision

If comprehensive URL normalization and filtering are implemented, then product information accuracy improves, but processing time and computational complexity increase

Engineering Contradiction:

Improveproduct link accuracyVSAvoidURL processing duration

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system applies selective normalization and filtering based on confidence thresholds and data quality indicators. By performing partial processing on high-confidence URLs and more extensive processing only when necessary, the system maintains accuracy while reducing overall processing time.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12482028B2Building shoppable video corpus out of a generic video corpus via video meta data link

Publication Date: 2025.11.25 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Aspects of the present disclosure relate to providing a shoppable video corpus by generating a shoppable URL. A URL is extracted from a video corpus, in which the extracted URL may be a long URL or a short URL. Extracted URLs are then combined and normalized. From the normalized URL, noise is removed and quality control is performed. As a result, shoppable URL may be presented at the user's computing device as personal recommendation. The video and metadata of the cleaned URL is also ingested and stored in a database for future reference.