Content Discovery System for Automatic Rail Organization

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Solution Overview

Problem

Users face challenges in discovering relevant content due to overwhelming lists of rails, with new rails often being overlooked as they are not popular enough to be displayed prominently, and curators struggle to determine appropriate content for diverse user groups based on geographic regions and interests.

Innovation Solution

A content discovery system that analyzes historical rail information to automatically determine the type and order of rails, using machine learning models to generate new rails and identify relevant content based on current dates, seasons, and geographic regions, and determines display order based on user preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If rails are ordered by popularity, then popular content is prominently displayed, but new rails are overlooked and receive less visibility

Engineering Contradiction:

Improvecontent discovery effectivenessVSAvoidnew rail visibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by analyzing historical rail information and determining characteristics of time periods before ordering rails. It proactively identifies new rails and places them in prominent positions without waiting for popularity accumulation, ensuring new content receives immediate visibility while maintaining overall content discovery effectiveness

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The rail ordering system is made dynamic by continuously analyzing historical data, determining time period characteristics, and adjusting rail positions based on current context. The system adapts rail ordering to balance popular content with new content based on determined characteristics, allowing the display to evolve rather than remaining static

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If curators manually create rails for diverse user groups, then content can be tailored to specific regions and interests, but the complexity of determining appropriate content increases

Engineering Contradiction:

Improvecontent customization for diverse usersVSAvoidcuration process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables self-service by automatically determining rail characteristics and content associations without requiring manual curator intervention. The computer system analyzes historical rail information, determines time period characteristics, and automatically identifies appropriate content for different user groups and geographic regions, reducing curation complexity while maintaining adaptability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of curator analysis and content selection is replaced with an automated computer system. The system uses historical data analysis and characteristic determination to substitute human judgment with algorithmic processing, reducing complexity while maintaining or improving content customization capabilities

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If a large number of rails are provided, then more content categories are available, but users become overwhelmed and may not discover relevant content

Engineering Contradiction:

Improvecontent category varietyVSAvoiduser interface simplicity

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system applies local quality by determining specific characteristics for different time periods and user contexts. Instead of applying a uniform ordering approach to all rails, it analyzes historical information locally for each rail and time period combination, then orders rails according to determined characteristics, providing variety while maintaining interface simplicity through context-appropriate presentation

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11838597B1Systems and methods for content discovery by automatic organization of collections or rails

Publication Date: 2023.12.05 VERIZON PATENT & LICENSING INC

AI summary

In some implementations, a device may receive historical content data indicating historical characteristics associated with one or more groups of content. The device may determine, based on the historical content data, one or more characteristics associated with a time period. The device may determine, based on the one or more characteristics, a new group of content associated with the time period. The device may generate a display element for accessing content included in the new group of content during the time period.