Voice Query Acoustic Ranking for Age-Appropriate Content
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Solution Overview
Problem
Current NLP and voice recognition systems fail to consider user attributes and context in ranking content relevance, leading to inappropriate content presentation based on entity type and user demographics.
Innovation Solution
Leverage acoustic features to personalize content presentation by assigning relevance scores based on user demographics, adjusting scores for adult or child entities, and tailoring content selection accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP and voice recognition systems use context in the phrase from the user's voice query to determine entity type and rank content, then content matching accuracy is improved, but user attribute consideration and context-based personalization are not addressed
Solution Approach 1:
The system segments the content ranking process into multiple independent components: acoustic feature extraction, user entity determination, content entity type labeling, and relevance score calculation. Each component processes specific aspects separately before integrating results, allowing user attributes to be considered independently while maintaining overall matching accuracy.
Solution Approach 2:
The patent adds a new dimension to the content ranking system by incorporating acoustic features and user entity types as additional ranking criteria beyond traditional phrase-matching. This multi-dimensional approach enables simultaneous optimization of matching accuracy and user attribute consideration through separate relevance scores for adult and child entity types.
2Ease of operation
If the system provides content based on phrase matching without considering user demographics, then processing simplicity is maintained, but content appropriateness for different user groups deteriorates
Solution Approach 1:
The system performs preliminary actions by determining user entity type (adult or child) and labeling content entities before the final ranking process. These pre-computed attributes are stored and reused during content delivery, enabling appropriate content filtering without adding complexity to the real-time query processing.
Solution Approach 2:
The patent introduces intermediary components including acoustic feature extractors and entity type labelers that mediate between the simple voice query input and the content delivery system. These intermediaries handle the complex analysis of user attributes and content appropriateness, allowing the core system to maintain processing simplicity while ensuring content reliability.
3Productivity
If the system ranks content based on entity type weights from ontology without acoustic feature analysis, then computational efficiency is improved, but personalization based on user characteristics is lost
Solution Approach 1:
The system implements self-service by automatically extracting acoustic features from voice queries and determining user entity types without requiring manual input or complex configuration. The acoustic model and entity type labeler autonomously process each query, enabling personalization while maintaining computational efficiency through automated, reusable processing pipelines.
Solution Approach 2:
The patent changes key parameters by incorporating acoustic features (spectral characteristics, pitch, tone) and entity type probabilities as new ranking parameters alongside traditional ontology weights. This multi-parameter approach enables personalization through acoustic-based user identification while maintaining computational efficiency by using pre-computed feature extractions and probabilistic models.
Data Source
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AI summary
The methods and systems described herein leveraging acoustic features of a user to generate and present a personalized content to a user. In one example, the method receives a voice query and determines that the query refers to either a first content item or a second content item. The first content item is associated with a first type assigned with a first score and the second content item is associated with a second entity type assigned with a second score. The method also determines whether the query is from the second entity type. The method ranks the first and the second content items based on this determination and generates for presentation of the first and the second content items based on the ranking. The method also changes the first or the second scores based on this determination and selects one of the first or the second content item for presentation.