Media Playback Scheduling Around Audience Conversations

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems fail to distinguish between short chats and longer conversations during media playback, leading to missed interesting sections and abrupt interruptions of conversations when adapting media schedules.

Innovation Solution

A system that identifies conversations through speech recognition and natural language processing to determine engagement levels and topics, adjusting media playback to avoid interruptions by postponing or speeding up media items based on conversation duration and relevance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the system alerts viewers to prevent missing interesting sections, then viewers do not miss important content, but the conversation is abruptly interrupted

Engineering Contradiction:

Improveviewers not missing interesting sectionsVSAvoidconversation flow

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system dynamically adjusts the media playback schedule based on real-time conversation detection and prediction. When a conversation is detected, the system postpones the start of interesting media sections until the conversation naturally concludes, creating a flexible, adaptive schedule that responds to viewer behavior rather than following a rigid timetable.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by predicting conversation end times in advance using machine learning models. This prediction allows the system to prepare and queue interesting media sections to start automatically when the conversation is expected to end, ensuring seamless transitions without abrupt interruptions or missed content.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If the system uses speech recognition and natural language processing to identify conversations, then conversation detection accuracy improves, but system complexity increases

Engineering Contradiction:

Improveconversation detection accuracyVSAvoidsystem architecture

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces intermediary components including speech recognition engines, natural language processing modules, and machine learning prediction models that act as mediators between the audio input and the media playback control. These intermediaries process and interpret conversation data, enabling accurate detection and prediction while maintaining a modular system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback loops where conversation detection results and prediction outcomes continuously inform media schedule adjustments. The machine learning models learn from past conversation patterns and media engagement data, refining their accuracy over time and creating a self-improving system that reduces complexity through智能化 adaptation.

Inventive Principle:

Principle #23Feedback

3Duration of action of stationary object

If the system postpones media items to maintain conversations, then conversation continuity is preserved, but media playback schedule deviates from original plan

Engineering Contradiction:

Improveconversation durationVSAvoidmedia schedule timing

Core Design Contradiction:

Duration of action of stationary objectVSLoss of time

Solution Approach 1:

The media playback schedule is transformed from a static, predetermined timetable to a dynamic, real-time adaptive schedule. The system continuously monitors conversation status and automatically adjusts media start times, durations, and sequencing to align with actual viewer behavior, minimizing schedule deviations while preserving both conversation flow and media content delivery.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12537995B2Systems and methods to adapt a schedule to be played by a media player

Publication Date: 2026.01.27 ADEIA GUIDES INC

AI summary

Systems and methods are provided for adapting playout of a plurality of media items. One example method includes receiving one or more inputs representing a conversation between an audience of two or more people experiencing the playout of the plurality of media items, processing the input to determine a level of engagement of the audience with the playout of at least one of the plurality of media items, and adapting playout of the at least one of the plurality of media items before the scheduled start of the next media item in the schedule to take account of the inputs representing the conversation.