Multi-Task Two-Tower Neural Network for Shared Guideline Evaluation
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Solution Overview
Problem
Existing machine learning models for evaluating content against multiple guidelines are unreliable due to the lack of knowledge transfer between models, requiring separate training for each guideline and making it difficult to onboard new guidelines, and are limited in handling text and image content effectively.
Innovation Solution
A multi-task two-tower neural network that embeds content into a shared latent n-dimensional space, allowing guideline-specific layers to share knowledge and learn from each other, facilitating easier addition of new guidelines and improving reliability in evaluating text and image content against multiple guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate machine learning models are trained for each guideline, then each model can be specialized for its specific guideline, but the reliability of each model is reduced due to lack of knowledge transfer and complete retraining is required for new guidelines
Solution Approach 1:
The patent merges multiple separate guideline evaluation models into a single multi-task machine learning model that simultaneously evaluates content against multiple guidelines. This unified model shares common features and representations across all guidelines, enabling knowledge transfer between different guideline evaluations while maintaining specialized evaluation capabilities for each guideline type.
Solution Approach 2:
The patent creates a universal machine learning model that performs multiple functions by evaluating content against different guidelines simultaneously. The model uses a shared feature extraction backbone that serves all guideline evaluations, making the system adaptable to new guidelines while maintaining reliable evaluations through shared knowledge across all evaluation tasks.
2Measurement precision
If separate machine learning models are trained for each guideline, then each model can focus on specific guideline criteria, but it requires complete retraining when new guidelines are added
Solution Approach 1:
The patent combines multiple guideline evaluation tasks into a single multi-task model architecture where common feature extraction is shared across all guidelines. This reduces deployment complexity because adding a new guideline only requires training the guideline-specific classification head rather than retraining the entire model from scratch.
Solution Approach 2:
The patent segments the model into shared feature extraction components and guideline-specific classification components. This segmentation allows new guidelines to be added by only training the specific classification layer for that guideline while reusing the pre-trained shared features, thereby reducing training complexity while maintaining evaluation precision.
3Manufacturing precision
If separate machine learning models are used for text and image content, then each model can be optimized for its content type, but knowledge transfer between content types is limited
Solution Approach 1:
The patent merges text and image content evaluation into a unified multi-task model that shares common feature representations. This allows knowledge learned from text content to transfer to image content evaluation and vice versa, while still maintaining optimized analysis precision for each content type through task-specific classification layers.
Data Source
AI summary
In an example embodiment, a solution is provided where a multi-task two-tower machine learning model is utilized to evaluate content against multiple different guidelines. Each of these guidelines represent a different task in the single multi-task two-tower machine learning model, and knowledge can be shared between the layers of the model devoted to each guideline, eliminating the need for separately trained models and also greatly improving the reliability of the predictions. The multi-task two-tower machine learning model is also capable of evaluating both text content and image content, via a respective tower utilized to embed such content into a shared latent n-dimensional space.