Predictive Query Execution for Lower-Latency Voice Search

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Solution Overview

Problem

Latency issues persist in speech recognition processing for voice-based services, particularly in voice search systems, due to the need for complete transcription before processing user intent, leading to delays exceeding the acceptable threshold for instantaneous human perception.

Innovation Solution

Implementing query auto completion (QAC) to autocomplete an intermediate transcription, predicting voice queries before completion, and caching responses for immediate retrieval upon matching the final query, thereby reducing latency by processing predicted queries in parallel with ongoing speech.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the system waits for complete transcription before processing, then transcription accuracy is improved, but system latency increases beyond acceptable thresholds

Engineering Contradiction:

Improvetranscription accuracyVSAvoidsystem latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing by generating predicted completions for partial transcriptions before the user finishes speaking. The query completion service receives intermediate transcriptions, generates predicted queries, and processes them through downstream components in advance, so that when the final transcription is received, the response is already prepared or can be quickly retrieved from cache.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system segments the query processing into multiple stages: receiving partial transcription, generating predicted completions, processing predicted queries through downstream components, and finally matching against actual completed queries. This segmentation allows parallel processing of multiple predicted queries simultaneously rather than waiting sequentially.

Inventive Principle:

Principle #1Segmentation

2Reliability

If the system processes queries in real-time as they complete, then processing accuracy is maintained, but response time exceeds human perception thresholds for instantaneous feedback

Engineering Contradiction:

Improveprocessing accuracyVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSSpeed

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuous processing by always having predicted queries being processed through downstream components while the user is still speaking. This continuous parallel processing ensures that when a query completes, the system is already ahead in the processing pipeline, maintaining high speed without sacrificing accuracy through proper query validation and matching.

Inventive Principle:

Principle #20Continuity of useful action

3Loss of time

If the system processes predicted queries in parallel, then overall latency is reduced, but computational complexity and resource usage increase

Engineering Contradiction:

Improveoverall latencyVSAvoidcomputational complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system applies partial action by generating a limited set of predicted completions (e.g., top-K predictions) rather than exhaustively processing all possible query variations. This selective approach processes only the most likely predicted queries in parallel, reducing computational complexity while still achieving significant latency reduction for the actual completed query.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250225982A1Predictive query execution

Publication Date: 2025.07.10 COMCAST CABLE COMM LLC

AI summary

First audio data associated with a first portion of a voice query (e.g., an incomplete voice query) may be received (e.g., by a device or a server). A first transcript may be determined by a speech recognition engine and based on the first audio data. A plurality of predicted queries may be determined by applying a prediction process to the first transcript. A response for each of the plurality of predicted queries may be determined by processing the plurality of the predicted queries. Second audio data associated with a second portion of the voice query (e.g., a complete voice query) may be received. A second transcript may be determined by the speech recognition engine and based on the second audio data. Based on comparing the second transcript to one of the plurality of predicted queries, a response for the voice query may be returned.