Ambient Content Display Using Feedback-Based Preference Learning
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Solution Overview
Problem
Existing speech-processing enabled devices struggle to provide relevant, user-specific content in an ambient state without user interaction, lacking effective methods to learn and adapt to user preferences and contextual data.
Innovation Solution
A content recommender system utilizing machine learning models that integrate feature extraction and prediction/ranking algorithms to determine and output content based on user interactions, timing, location, and device context, while ensuring user privacy and compliance with data regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech-processing enabled devices operate in ambient state without user interaction, then device availability and responsiveness are improved, but the ability to provide relevant user-specific content deteriorates due to lack of direct user input
Solution Approach 1:
The system performs preliminary actions by collecting and processing user interaction data, timing information, location data, and device context during active states. This pre-processing enables the device to generate relevant content recommendations during ambient states without requiring real-time user input, thus maintaining both availability and content relevance.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with displayed content and using this feedback to refine future content recommendations. The machine learning models learn from user responses (such as selection, dismissal, or engagement duration) to improve the accuracy and relevance of ambient content suggestions over time.
2Adaptability or versatility
If machine learning models process and store user data for personalization, then content relevance and user engagement are improved, but user privacy concerns and data security requirements worsen
Solution Approach 1:
The system applies local quality by processing and storing user data locally on the device rather than centrally on servers. This enables personalized content recommendations while maintaining user privacy, as the data never leaves the device. The machine learning models operate on local user interaction history, timing patterns, and context data stored in device memory.
Solution Approach 2:
The system uses an intermediary approach by implementing data anonymization and aggregation techniques before any data is stored or processed. User-specific identifiers are removed or encrypted, and data is aggregated into patterns that preserve privacy while still enabling personalization. This intermediary processing layer protects user privacy while maintaining the ability to provide relevant content.
3Measurement precision
If the device continuously monitors and learns from user interactions, then content recommendation accuracy improves over time, but device complexity and computational requirements worsen
Solution Approach 1:
The system segments the content recommendation functionality into distinct modular components: data collection modules, feature extraction modules, machine learning model modules, and content selection modules. Each module has a specific function and can be independently optimized or updated. This segmentation reduces overall system complexity while enabling continuous learning and improved accuracy through specialized processing in each segment.
Solution Approach 2:
The system applies partial action by implementing learning and processing only during specific conditions or time periods rather than continuously. For example, heavy computational learning may occur during low-usage periods or in batches, while real-time operations use lighter processing. This approach improves recommendation accuracy over time without requiring excessive computational resources during all operational states.
Data Source
AI summary
Devices and techniques are generally described for sending a first instruction for a device to output first content while the speech-processing device is in an ambient state during a first time period. First feedback data is received indicating that a first action associated with the first content was requested at a first time. A determination is made that the first time is during the first time period. Timing data related to a current time of the device is determined. Second content is determined based at least in part on the first action being requested during the first time period and the timing data. A second instruction is sent effective to cause the device to output second content while in the ambient state during a second time period.