Latent Embedding Attention for Scalable Neural Network Outputs

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing machine learning models face challenges in efficiently processing large numbers of data element embeddings due to high computational complexity, particularly when the embeddings lack a fixed spatial arrangement, leading to resource-intensive operations.

Innovation Solution

A neural network system utilizing cross-attention and self-attention blocks processes a predefined number of latent embeddings independently of the number of data element embeddings, adaptively selecting a subset for attention, and incorporates positional encodings to handle spatial arrangements flexibly, reducing computational resource consumption while maintaining accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If existing machine learning models process large numbers of data element embeddings, then comprehensive data analysis is achieved, but computational complexity increases significantly

Engineering Contradiction:

Improvedata analysis completenessVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts only the most relevant data element embeddings by having latent embeddings selectively attend to specific data elements through attention mechanisms. This extraction principle allows the model to focus on critical information while ignoring redundant data, thereby reducing computational complexity while maintaining analysis completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the processing into two distinct stages: first processing a fixed number of latent embeddings, then having these latent embeddings attend to data element embeddings. This segmentation decouples the computational complexity from the total number of data elements, allowing efficient processing of large datasets.

Inventive Principle:

Principle #1Segmentation

2Reliability

If attention operations are performed over all data element embeddings, then accurate entity characterization is achieved, but resource consumption increases

Engineering Contradiction:

Improveentity characterization accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

Instead of performing attention operations over all data element embeddings, the patent applies partial action by having latent embeddings attend only to a subset of relevant data elements. This selective attention maintains characterization accuracy while significantly reducing computational resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The attention operation is segmented into two phases: first, processing a fixed number of latent embeddings independently of data element count; second, having these latent embeddings attend to data elements. This segmentation reduces resource consumption by decoupling the first processing stage from the total number of data elements.

Inventive Principle:

Principle #1Segmentation

3Productivity

If models assume fixed spatial arrangements for data elements, then processing efficiency is improved, but flexibility to handle diverse data types is reduced

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddata type flexibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent employs positional encodings that can represent various spatial arrangements (fixed or flexible, 1D, 2D, or 3D) within a unified framework. This universal approach allows the model to handle diverse data types with different spatial structures while maintaining processing efficiency through consistent attention mechanisms.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent uses dynamic positional encodings that can adapt to different spatial arrangements rather than assuming fixed patterns. This dynamic approach allows the model to flexibly handle various data types while maintaining processing efficiency through the unified attention mechanism.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12462163B2Generating neural network outputs by enriching latent embeddings using self-attention and cross-attention operations

Publication Date: 2025.11.04 GDM HOLDING LLC

AI summary

This specification describes a method for using a neural network to generate a network output that characterizes an entity. The method includes: obtaining a representation of the entity as a set of data element embeddings, obtaining a set of latent embeddings, and processing: (i) the set of data element embeddings, and (ii) the set of latent embeddings, using the neural network to generate the network output characterizing the entity. The neural network includes: (i) one or more cross-attention blocks, (ii) one or more self-attention blocks, and (iii) an output block. Each cross-attention block updates each latent embedding using attention over some or all of the data element embeddings. Each self-attention block updates each latent embedding using attention over the set of latent embeddings. The output block processes one or more latent embeddings to generate the network output that characterizes the entity.