Automated Text Novelty Detection for Financial Documents
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Solution Overview
Problem
Current methods for analyzing financial documents, such as SEC filings, are inefficient due to the lack of automated tools for processing unstructured textual information, requiring manual expertise and consuming significant time, especially in identifying market standard language and potential inaccuracies.
Innovation Solution
A system and method for analyzing clusters of conceptually-related text documents to develop a model document, calculate novelty measurements, and merge documents based on common neighbors similarity, providing direct access to a large corpus of documents scored for conformity and novelty to market standard language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to process unstructured text in financial documents, then analysis accuracy can be maintained through expert judgment, but time consumption increases significantly and productivity decreases
Solution Approach 1:
The patent creates a model document that replicates the structure and language patterns of standard financial documents. This model serves as a template against which actual documents are compared, enabling automated identification of deviations from market standard language without requiring manual expert analysis of each document.
Solution Approach 2:
The system transforms unstructured text analysis into a structured comparison process by defining specific parameters such as novelty measurements and common neighbors similarity metrics. These parameters enable quantitative automated comparison while maintaining the ability to detect subtle linguistic deviations that would indicate atypical language usage.
2Productivity
If automated processing is implemented to increase productivity, then processing speed improves, but the ability to detect subtle linguistic deviations and maintain analysis precision deteriorates
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that uses algorithmic comparisons. The system substitutes human expert judgment with automated novelty measurements and similarity calculations, achieving both speed and precision through mathematical rather than mechanical processes.
Solution Approach 2:
The model document serves as an intermediary between the actual financial documents and the analysis process. It mediates the comparison by providing a standardized reference that captures market standard language, allowing the automated system to detect deviations without needing to understand the full context of each document.
3Reliability
If comprehensive manual review is performed to ensure reliability, then detection accuracy improves, but time consumption increases and the system becomes less adaptable to large volumes of documents
Solution Approach 1:
The patent segments the analysis process into distinct components: creating a model document, calculating novelty measurements for individual documents, and computing common neighbors similarity. This segmentation allows the system to process documents independently and parallelize operations, enabling scalable handling of large document volumes while maintaining reliable detection through consistent application of the same analytical segments.
4Measurement precision
If expert manual analysis is used to identify market standard language, then measurement precision is maintained, but device complexity and operational complexity increase
Solution Approach 1:
The system enables self-service by allowing automated processing without requiring expert manual intervention. The model document and automated comparison algorithms serve themselves to identify market standard language and detect deviations, eliminating the need for external expert analysis while maintaining precision through consistent algorithmic application.
Data Source
AI summary
A method includes analyzing a cluster of conceptually-related portions of text to develop a model and calculating a novelty measurement between a first identified conceptually-related portion of text and the model. The method further includes transmitting a second identified conceptually-related portion of text and a score associated with the novelty measurement from a server to an access device via a signal. Another method includes determining at least two corpora of conceptually-related portions of text. The method also includes calculating a common neighbors similarity measurement between the at least two corpora of conceptually-related portions of text and if the common neighbors similarity measurement exceeds a threshold, merging the at least two corpora of conceptually-related portions of text into a cluster or if the common neighbors similarity measurement does not exceed a threshold, maintaining a non-merge of the at least two corpora of conceptually-related portions of text.