How Google's Neural Network Patent US20250131984A1 revolutionizes Sequence Error Correction

Google's patent US20250131984A1 presents a method for sequence error correction using neural networks, focusing on improving accuracy in detecting and correcting errors in sequences such as DNA or text. The patent describes a system that leverages deep learning models—specifically artificial neural networks (ANNs)—to identify errors in sequences and suggest corrections based on the context surrounding the error.

Overview of the Patent's Core Concept

The patent centres on using neural networks to analyse sequences and correct errors by understanding the context of each element (e.g., a word in a sentence or a base in a DNA sequence). This approach contrasts with traditional rule-based error correction methods, which rely on predefined rules and often fail to handle complex or ambiguous cases.

Key Components of the System

1. Input Sequence Processing
   The system receives a sequence, such as a sentence or a DNA read, and identifies target elements that may contain errors.

2. Context Vector Generation Using Recurrent Neural Networks (RNNs)
   Two recurrent neural networks process the sequence to generate a context vector for each target element. One RNN processes the sequence before the target element, and the other processes the sequence after it. This bidirectional context helps the model understand the surrounding information that influences the correctness of the target element.

3. Classification Using Feedforward Neural Network
   The context vector is fed into a feedforward neural network that classifies the target element for an error type (e.g., grammatical error type or sequencing error type). This classification predicts whether the element is correct or erroneous and suggests the correct form if an error is detected.

4. Error Detection and Correction
   The system compares the predicted classification with the actual classification (if available) or uses the predicted classification to flag errors and provide corrections.

Training the Neural Networks

The patent outlines a method for training the neural networks using a dataset of sequences with known errors and corrections. During training, the model learns to minimise the difference between its predicted classifications and the actual classifications of target elements, improving its ability to detect and correct errors over time.

How Does This Work?

Imagine you're reading a sentence, and you come across a word that doesn't seem to fit—maybe it's misspelt or grammatically wrong. To figure out if it's wrong, you look at the words before and after it to understand the context. Google's system does the same thing but with the help of neural networks, which are computer programs designed to mimic how our brain processes information.

• The system reads the sequence from left to right and right to left to gather clues about the target word or base.

• It then uses this information to decide if the target is correct or if it should be changed.

• If it finds a mistake, it suggests the right correction.

This method is powerful because it doesn't just check for simple mistakes but understands the context, making it better at catching tricky errors.

Visual Explanation

Here is a simplified illustration of the process:

text
Sequence:  The cat is siting on the mat.

Target word: "siting"

Step 1: Context extraction
   Left context: "The cat is"
   Right context: "on the mat"

Step 2: Neural networks process left and right context separately to create context vectors.

Step 3: Feedforward neural network uses these vectors to classify "siting" as an error.

Step 4: System suggests correction: "sitting"

Similarly, the system looks at the bases before and after a target base for DNA sequences to decide if it is an error and what the correct base should be.

Relation to Other Google Technologies

Google has also developed neural network-based systems for grammatical error correction in text (WO2019024050A1) and DNA sequencing error correction using deep learning frameworks like TensorFlow and Nucleus. These systems use similar principles of context-aware neural networks to improve accuracy in error detection and correction by learning from large datasets of correct and incorrect sequences.

Summary

Google's patent on sequence error correction using neural networks introduces a sophisticated method that uses bidirectional recurrent neural networks and feedforward neural networks to analyse the context of each element in a sequence. By doing so, it can accurately detect and correct errors in sequences such as text or DNA, surpassing traditional rule-based methods. This technology has broad applications in natural language processing, genomics, and any field where sequence accuracy is critical.