Bit Vector Content Matching for Third-Party Digital Assistant Actions

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Solution Overview

Problem

Existing systems face challenges in efficiently and accurately matching content items to third-party digital assistant actions due to limited access to conversational paths and previous branches, leading to resource-intensive and inaccurate content selection, which affects user experience.

Innovation Solution

A data processing system generates bit vectors using a deep learning model to represent relevant content items for voice actions, simulating conversations to identify dialog flows and using k-dimensional trees for real-time content selection, reducing resource consumption without sacrificing accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional content matching methods are used for third-party digital assistant actions, then content selection can be performed, but the process is resource-intensive and inaccurate due to limited access to conversational paths and previous branches

Engineering Contradiction:

Improvecontent matching accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs content matching in advance by generating bit vectors that encode content items and their relationships to digital assistant actions. These pre-computed bit vectors are stored and retrieved during runtime, eliminating the need for resource-intensive real-time analysis of conversational paths and branches. This preliminary action resolves the contradiction by shifting computational workload from runtime to offline processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates compressed representations (bit vectors) of content items that capture essential semantic information without storing the full conversational context. These bit vector copies enable accurate content matching while consuming minimal computational resources during runtime, as they contain pre-extracted features rather than requiring re-analysis of original conversational data.

Inventive Principle:

Principle #26Copying

2Productivity

If real-time content selection is performed without pre-generated bit vectors, then accurate matching can be attempted, but excessive computational resources are required during runtime

Engineering Contradiction:

Improvereal-time content selection speedVSAvoidruntime computational resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

Bit vectors are generated offline before runtime, encoding content item features and relationships to digital assistant actions. During runtime, the system simply retrieves and compares these pre-computed bit vectors, enabling fast real-time content selection without requiring heavy computational resources. This preliminary computation resolves the contradiction between speed and resource consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The bit vector representation captures only the essential features of content items needed for matching, rather than processing complete conversational contexts in real-time. This partial action approach achieves sufficient matching accuracy while dramatically reducing runtime computational requirements, resolving the contradiction between thoroughness and efficiency.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of information

If traditional matching approaches are used, then content items can be selected, but the system lacks access to dialog flows and conversation branches needed for accurate matching

Engineering Contradiction:

Improveaccess to conversational contextVSAvoidsystem architecture complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system extracts essential conversational context information (dialog flows, conversation branches) during offline processing and encodes it into bit vectors. This extraction removes the need for runtime access to complete conversational contexts, resolving the information access problem while maintaining matching accuracy. The complex conversational structures are distilled into compact bit vector representations.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Bit vectors serve as intermediary representations that bridge the gap between complex conversational contexts and simple matching operations. Instead of directly accessing and processing full dialog flows during runtime, the system uses these intermediary bit vectors that capture essential matching information, simplifying the system architecture while preserving necessary contextual information.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12380885B2Bit vector-based content matching for third-party digital assistant actions

Publication Date: 2025.08.05 GOOGLE LLC

AI summary

Matching content to third-party digital assistant actions using a bit vector is provided. A system receives an application with voice-assistant compatible actions. The system identifies the actions in the application. The system identifies content items provided by third-party computing devices. The system generates, via a machine learning model and performance data for the content items, bit vectors or the actions. The system selects, responsive to a request for content from a client device that executes an action of the application, a content item based on the bit vector that corresponds to the action.