Random Sequence Embeddings for Symbolic Sequence Analytics
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Solution Overview
Problem
String classification is challenging due to the lack of explicit features in sequences, high dimensionality, and difficulty in capturing sequential nature, especially in applications like computational biology and music classification, where existing methods fail to effectively capture long discriminative patterns and suffer from quadratic complexity with respect to the number of training samples.
Innovation Solution
The proposed solution involves cloud-based symbolic sequence analytics using random sequence embeddings (RSEs) that generate random sequences to compute a feature matrix, ensuring positive-definiteness and linear computational complexity, allowing for efficient classification and clustering while maintaining data privacy through a two-party system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional string classification methods are used to capture sequential patterns, then classification accuracy may improve, but computational complexity increases quadratically with the number of training samples
Solution Approach 1:
The patent replaces traditional mechanical string matching and sequential pattern analysis with a neural network-based embedding system. The neural network automatically learns sequential patterns from training data and transforms strings into numerical embeddings, eliminating the need for explicit feature extraction and reducing computational complexity from quadratic to linear scaling.
Solution Approach 2:
The patent transforms categorical string data into continuous numerical embeddings through neural network processing. This parameter transformation allows the system to capture sequential patterns in a compressed numerical space, improving both computational efficiency and classification accuracy while avoiding the quadratic complexity of traditional string comparison methods.
2Loss of information
If feature selection techniques are applied to extract meaningful characteristics, then relevant patterns may be identified, but the dimensionality of potential features remains high
Solution Approach 1:
The patent transforms high-dimensional categorical string features into a lower-dimensional continuous embedding space through neural network processing. This dimensional transformation preserves essential sequential patterns while compressing the feature space, effectively reducing dimensionality without losing critical information for classification.
Solution Approach 2:
The patent replaces manual feature selection and extraction processes with automated neural network embedding. The neural network learns optimal feature representations directly from the data, eliminating the need for explicit feature engineering and reducing the effective dimensionality through learned compression.
3Measurement precision
If explicit features are extracted from sequences to improve classification, then measurable characteristics can be obtained, but the sequential nature of features becomes difficult to capture
Solution Approach 1:
The patent replaces explicit feature extraction with neural network embedding that automatically captures sequential structure. The neural network processes strings in their native sequential form and learns hierarchical representations that preserve temporal and positional relationships without requiring manual feature engineering.
Solution Approach 2:
The patent transforms discrete sequential symbols into continuous embedding vectors that preserve sequential relationships. The neural network encoding process converts categorical sequence data into numerical representations where sequential structure is encoded in the embedding space, making it measurable while retaining structural information.
4Reliability
If cloud-based analytics are implemented to enable remote data processing, then data privacy can be maintained, but computational overhead and communication requirements increase
Solution Approach 1:
The patent introduces a cloud-based analytics service as an intermediary between data owners and data users. The service receives encrypted or anonymized data, performs classification using pre-trained neural networks, and returns results without exposing raw data. This intermediary architecture enables privacy-preserving computation while distributing the computational burden to cloud infrastructure.
Solution Approach 2:
The patent performs neural network training and model optimization in advance during an offline phase. Pre-trained embedding models and classification algorithms are deployed to the cloud service, enabling fast online inference without requiring complex real-time computation. This preliminary action reduces online computational overhead while maintaining privacy through centralized model management.
Data Source
AI summary
A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner.