Conversation Detection for Digital Content Visibility
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Solution Overview
Problem
Computing devices struggle to seamlessly integrate conversation detection during digital content presentation, often causing distractions as they fail to automatically adjust content visibility or engagement levels based on user interactions.
Innovation Solution
Implementing a conversation detection system that uses audio and video data from sensors to identify alternating human speech segments, allowing the device to modify digital content presentation by hiding, moving, or altering the size and transparency of content items to reduce noticeability during conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital content is presented continuously to maintain engagement, then content consumption is uninterrupted, but user attention is distracted during conversations
Solution Approach 1:
The system dynamically adjusts content visibility based on detected user interactions. When a conversation is detected, the content automatically transitions to a hidden or reduced-visibility state, and resumes normal presentation when the conversation ends. This dynamic adaptation resolves the contradiction by allowing continuous content availability while eliminating distraction during conversations.
Solution Approach 2:
The system uses audio sensors to detect conversation feedback loops between users. By monitoring alternating speech segments and identifying conversational patterns, the system receives real-time feedback about user engagement state and automatically adjusts content presentation accordingly, maintaining productivity while eliminating distraction.
2Ease of operation
If the system automatically detects and adjusts content based on conversations, then user experience is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting conversations and adjusting content without requiring manual user input. The computational tasks for speech detection, conversation identification, and content adjustment are performed autonomously by the system, improving ease of operation while managing complexity through automated decision-making.
Solution Approach 2:
The computing device integrates multiple functions including audio sensing, speech recognition, conversation analysis, and content presentation management into a unified system. This multi-functionality approach consolidates complexity into a single integrated platform rather than separate components, improving ease of operation while managing overall system complexity.
3Object-affected harmful factors
If content visibility is reduced during conversations to minimize distraction, then user engagement with others is improved, but content accessibility is reduced
Solution Approach 1:
The system implements dynamic content visibility that transitions between two states: full visibility during solitary content consumption and reduced visibility during conversations. The content is not permanently lost but dynamically restored when the conversation ends, resolving the contradiction between minimizing distraction and maintaining accessibility.
Solution Approach 2:
The content presentation follows a periodic pattern of full visibility and reduced visibility based on detected user behavior. During conversations, content is temporarily hidden, then periodically restored when the conversation ends. This periodic action ensures content remains accessible while minimizing distraction during appropriate time windows.
Data Source
AI summary
Various embodiments relating to detecting a conversation during presentation of content on a computing device, and taking one or more actions in response to detecting the conversation, are disclosed. In one example, an audio data stream is received from one or more sensors, a conversation between a first user and a second user is detected based on the audio data stream, and presentation of a digital content item is modified by the computing device in response to detecting the conversation.