Dynamic Content Recommendations Using Schedule-Aware Candidate Selection

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Solution Overview

Problem

Content providers face challenges in quickly producing accurate content recommendations that align with a user's schedule and maximize available time for content consumption.

Innovation Solution

A method that determines user profiles based on historical data, including content recommendation periods and classifications, and selects content candidates based on remaining time and correlations with contextual features to transmit personalized content recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If content recommendations are generated based on comprehensive historical data and user profiles, then recommendation accuracy is improved, but system complexity increases

Engineering Contradiction:

Improverecommendation accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the recommendation process into distinct components: user profile determination, content recommendation period identification, content candidate selection, and transmission. Each component processes specific data types and produces defined outputs, reducing overall system complexity while maintaining comprehensive analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by determining user profiles and identifying content recommendation periods in advance of actual content delivery. This pre-processing organizes data structures and relationships beforehand, enabling faster and more accurate real-time recommendations without increasing operational complexity

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If content recommendations are customized to fit user schedules and maximize available time, then user satisfaction is improved, but processing time increases

Engineering Contradiction:

Improveschedule alignmentVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system determines user profiles containing content recommendation periods and classifications in advance, storing this information for rapid retrieval. This preliminary organization of schedule data enables quick matching with available time slots without extensive real-time processing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces complex mechanical scheduling processes with automated electronic determination of content recommendation periods based on historical data patterns. This substitution enables rapid identification of optimal content delivery times without manual intervention or complex real-time calculations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If content candidates are selected based on correlation with contextual features and remaining time, then recommendation relevance is improved, but computational load increases

Engineering Contradiction:

Improverecommendation relevanceVSAvoidcomputational load

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies local quality by evaluating contextual features and correlation metrics specifically for content candidates within identified recommendation periods, rather than uniformly processing all content. This targeted approach concentrates computational resources on relevant candidates, reducing overall computational load while maintaining high recommendation relevance

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs partial action by selecting and transmitting only the most relevant content candidates based on correlation thresholds and remaining time constraints, rather than processing and evaluating all possible content. This selective approach reduces computational load while ensuring high recommendation quality

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12361049B2Dynamic content recommendations

Publication Date: 2025.07.15 COMCAST CABLE COMM LLC

AI summary

According to some aspects, disclosed methods and systems may include determining, by a device and based on historical data associated with a first user, a first user profile comprising one or more content recommendation periods each associated with a time period and a content classification, and in response to detecting a user interaction, selecting a first content recommendation period of the one or more content recommendation periods. The methods and system may also include determining one or more content candidates corresponding to the content classification from a plurality of content assets based on an amount of remaining time in the time period associated with the first content recommendation period and a correlation between the historical data associated with the first user and one or more contextual features associated with the plurality of content assets, and transmitting, to a client device, an indication of the one or more content candidates.