Event Based Feature Computation for Voice Command Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for processing audio data from electronic devices face challenges in accurately interpreting voice commands and improving user experience due to limitations in feature computation and timeline-based data processing, which affect the accuracy of future predictions and command interpretation.
Innovation Solution
A feature computation system that processes event data to generate statistical features from timelines of user interactions, using a fleet of hosts for event correlation and aggregation, and stores these features in databases to enhance automatic speech recognition, natural language understanding, and machine learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional timeline-based data processing is used, then system simplicity is maintained, but feature computation accuracy and prediction performance deteriorate
Solution Approach 1:
The patent segments the monolithic timeline-based processing system into multiple independent event streams, each processed by dedicated feature computation engines. This allows parallel processing of different event types (voice commands, user interactions, device events) with specialized algorithms for each, improving computational accuracy while distributing system complexity across modular components rather than concentrating it in a single processing pipeline.
Solution Approach 2:
The patent introduces event correlation services and feature aggregation layers as intermediary components between raw event data and the final prediction models. These intermediaries pre-process and enrich event data with contextual features before feeding them to prediction algorithms, thereby improving feature computation accuracy without requiring fundamental changes to the underlying system architecture.
2Measurement precision
If comprehensive feature computation is performed on all user interactions, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements selective feature computation that applies comprehensive analysis only to critical event types (e.g., voice commands, purchase events) while using simplified processing for routine interactions. This partial action approach focuses computational resources on events that most impact prediction accuracy, achieving high prediction performance without the full time and resource cost of processing every event with maximum detail.
Solution Approach 2:
The system employs periodic batch processing for comprehensive feature computation combined with real-time streaming processing for time-sensitive events. By dividing processing into periodic batches for non-critical features and continuous streaming for critical features, the system achieves accurate predictions without requiring all computations to complete synchronously, thereby reducing overall processing time.
3Speed
If real-time feature computation is implemented, then user experience responsiveness improves, but system resource consumption increases
Solution Approach 1:
The patent implements continuous feature computation for critical user experience metrics (e.g., voice command interpretation, immediate feedback features) while using periodic batch processing for less time-sensitive features. This continuity approach maintains responsive user experience for critical functions without sustaining maximum resource consumption across all processing operations, thereby achieving good response speed with moderated resource usage.
Solution Approach 2:
The system dynamically adjusts the level of feature computation based on contextual factors such as device state, user activity type, and available resources. During active user interactions, real-time computation is prioritized for responsiveness; during idle periods or resource constraints, the system reduces computation intensity. This dynamic adaptation maintains user experience quality while optimizing resource consumption patterns.
Data Source
AI summary
Techniques for performing feature computation are described. A system may gather and analyze event data to generate a timeline of the event data and corresponding feature data (e.g., statistical values representing the event data). The system can create a customized timeline that allows information to be sorted and aggregated in different timescales to improve speech processing and other functionality. For example, a feature computation system may calculate statistics and other information based on interactions with a speech processing system. These statistics provide information about previous interactions that may be leveraged to interpret future voice commands.