Surreptitious Speech Detection via Out-of-Context Token Analysis
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Solution Overview
Problem
Conventional methods for detecting surreptitious speech in large volumes of text require manual searching, consuming significant time and resources, and are not effective in identifying out-of-context words or phrases that may indicate hidden information or communication channel changes.
Innovation Solution
A system utilizing machine learning models to process sequences of text, identifying out-of-context tokens and segments with surreptitious language by generating probability distributions and applying threshold probabilities, and optionally refining with high-value token identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching methods are used to detect surreptitious speech in large volumes of text, then the system can identify out-of-context words or phrases, but it consumes significant time and resources
Solution Approach 1:
The patent replaces manual searching with machine learning models that automatically process and analyze text sequences. The system uses pre-trained language models to generate probability distributions for tokens and segments, enabling automated detection of surreptitious speech patterns without human intervention, thus resolving the contradiction between detection accuracy and processing time
Solution Approach 2:
The system transforms the detection process by changing from manual analysis to automated computational analysis using machine learning parameters. It processes text at the token and segment levels using probability distributions generated by machine learning models, enabling efficient automated detection that maintains accuracy while significantly reducing processing time
2Productivity
If machine learning models process large volumes of text to detect surreptitious speech, then processing efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the text processing task into distinct levels: token-level analysis using probability distributions and segment-level analysis using surreptitious language detection models. This segmentation allows the system to handle large volumes of text efficiently by processing smaller units independently, maintaining productivity while managing system complexity through modular architecture
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw text input and detection output. These models act as mediators that automatically analyze text patterns and generate probability distributions, enabling efficient processing of large volumes of text without requiring complex manual analysis procedures, thus improving productivity while keeping the system architecture manageable
3Measurement precision
If the system processes text at the token level to identify out-of-context words, then detection precision improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing text into tokens and segments before analysis. The system pre-divides the text into manageable units and pre-generates probability distributions using machine learning models, enabling rapid detection of out-of-context tokens without requiring time-consuming analysis of the entire text at once, thus improving precision while reducing processing time
4Measurement precision
If the system uses multiple machine learning models to process different aspects of text, then detection accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent segments the detection task into different levels handled by specialized models: token-level probability distribution generation and segment-level surreptitious language detection. This segmentation allows each model to focus on specific aspects of the text, improving overall detection accuracy while optimizing computational resource usage by applying the right model at the right level rather than using a single overly complex model for everything
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting surreptitious speech. One of the methods includes obtaining data representing a sequence of text; obtaining a sequence of tokens for the sequence of text comprising one or more groups of tokens, wherein each group comprises two or more tokens; processing the one or more groups of tokens using a first machine learning model to identify tokens that are out of context in the sequence of tokens; and providing data representing the identified tokens.