Trending Data Ingestion Pipeline for Speech Systems
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Solution Overview
Problem
Current speech recognition systems face challenges in efficiently handling user commands related to trending topics, as they often rely on internal content and lack the ability to dynamically source and prioritize information from various external sources based on user preferences and topic relevance.
Innovation Solution
A speech processing system that gathers content from multiple sources, segments and prioritizes trending data using decay models and user profiles, allowing it to efficiently handle user commands by storing and retrieving relevant information from a dedicated trending storage, and outputs content based on user preferences and source credibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system relies on internal content for speech recognition, then system simplicity is maintained, but the ability to dynamically source and prioritize trending information from external sources is lost
Solution Approach 1:
The patent introduces an intermediary component (trending information module) that bridges the speech recognition system and external content sources. This module acts as a mediator that gathers, segments, and prioritizes trending data from multiple external sources, then makes it available to the speech recognition system without requiring complex integration of each external source directly into the core system.
2Loss of information
If the system gathers content from multiple external sources, then information diversity and relevance are improved, but data processing and management complexity increases
Solution Approach 1:
The patent segments the trending information gathering process into distinct functional components: a gathering component that collects data from multiple sources, a segmentation component that divides the gathered content into manageable portions, and a prioritization component that ranks segments based on relevance. This segmentation reduces the complexity of handling raw data from multiple sources by breaking it down into structured, prioritized information.
Solution Approach 2:
The system changes the parameter of data organization by introducing prioritization based on trending relevance. Instead of treating all external content equally, the system transforms the data stream by ranking segments according to their trending status, user preferences, and relevance to current queries, making the data more manageable and relevant.
3Loss of information
If the system stores all trending data from multiple sources, then information availability is improved, but storage efficiency and retrieval speed deteriorate due to data volume
Solution Approach 1:
The patent applies preliminary action by pre-segmenting and pre-prioritizing trending information before it needs to be retrieved. The system gathers and segments trending data in advance, assigns priority levels based on relevance and user preferences, and organizes it in a structured format. This preliminary processing ensures that when a user query arises, the system can quickly retrieve relevant information without having to process raw data from multiple sources in real-time.
4Measurement precision
If the system prioritizes trending data based on user preferences and source credibility, then response accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent changes the parameters of data prioritization by using pre-established user preference profiles and source credibility ratings. Instead of evaluating all possible factors in real-time, the system uses pre-computed parameters (user preferences, source credibility, trending status) to quickly rank and select relevant information, reducing processing time while maintaining high accuracy.
Data Source
AI summary
Techniques for expanding system capabilities to execute user commands relating to trending topics (e.g., real-time news questions, trending questions, sports questions, game questions, politic questions, etc.) are described. The system gathers data from a variety of sources (e.g., news feeds, social media feeds, RSS feeds, news websites, etc.). The system segments gathered data corresponding to, for example, topic and or entity. The system may only store data corresponding to a topic or entity in a dedicated trending storage if the system receives data corresponding to the topic or entity from a number of different sources satisfying a threshold number of sources. Data in the dedicated trending storage may be maintained using decay models or algorithms. For example, the more often the system receives data corresponding to a topic or entity from one or more sources, the longer the data is maintained in the storage, and vice versa.