Unsupervised Keyword Analysis for Real-Time Multilingual Documents
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Solution Overview
Problem
Service providers face challenges in efficiently searching and summarizing large volumes of multilingual documents, as existing methods require manual effort and are time-consuming, especially in compliance investigations where missing key information is easy due to the vast number of documents.
Innovation Solution
Utilizing an unsupervised machine learning model framework for real-time keyword analysis in multilingual documents, which includes text preprocessing, keyword extraction, scoring, and summarization, to automatically identify and rank relevant keywords across different languages and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to analyze multilingual documents, then investigators can understand and review memoranda, but the process is time-consuming and requires significant computing resources
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning system that performs text preprocessing, keyword extraction, scoring, and summarization. The ML model automatically analyzes multilingual documents, extracting keywords and generating summaries without human intervention, thereby eliminating the time-consuming manual review process while maintaining or improving extraction accuracy through algorithmic precision.
Solution Approach 2:
The system enables self-service automated analysis where the ML model independently processes multilingual documents, performs keyword extraction, and generates summaries without requiring investigator involvement. The model serves itself by automatically handling the entire analysis pipeline, from text preprocessing to final keyword ranking, freeing investigators from manual review tasks.
2Loss of information
If manual keyword analysis is performed on large volumes of multilingual documents, then key information can be identified, but the burden on search processing and document storage systems increases
Solution Approach 1:
The patent extracts only the essential keywords and summaries from large volumes of multilingual documents using ML algorithms, separating critical information from the bulk data. By extracting and ranking keywords based on relevance scores, the system isolates key information that investigators need, reducing the amount of data that must be stored and processed while ensuring no critical information is lost.
Solution Approach 2:
The system segments the analysis process into distinct ML model components: text preprocessing, keyword candidate generation, keyword scoring, and summary generation. This segmentation allows each component to handle specific tasks efficiently, reducing the overall system burden by distributing processing loads across multiple specialized modules rather than requiring a single complex system to handle all operations.
3Productivity
If traditional search methods are used for multilingual documents, then documents can be stored and retrieved, but the searching and summarization processes are time-consuming
Solution Approach 1:
The patent performs preliminary keyword extraction and document summarization automatically using ML models before investigators need to review documents. By pre-processing multilingual documents to extract and rank keywords in advance, the system prepares analyzed results that can be quickly retrieved and reviewed, eliminating the need for time-consuming search and analysis operations at the time of investigation.
Data Source
AI summary
There are provided systems and methods for sentence level dialogue summaries using unsupervised machine learning for keyword selection and scoring. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users, which may be used to engage in interactions with other users and entities. When utilizing these services, memoranda may be generated that includes text from different online interactions. To provide summarization and searching of the memoranda, the service provider may implement an unsupervised machine learning framework that utilizes machine learning models to perform text preprocessing, keyword extraction, and keyword weighting when ranking and outputting relevant keywords. Once extracted and ranked, the keywords may be used to provide different insights to the memoranda. The keywords may be selected based on domains and tasks and the framework may be made pluggable and customizable for different systems and search operations.