Context Extraction Models for Early Issue Remediation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume and complexity of structured, semi-structured, and natural language data pose challenges for effective issue identification and evaluation, necessitating a more efficient data processing and analytical mechanism to identify and resolve problems in their early phases.
Innovation Solution
An automated system utilizing natural language processing techniques and machine learning models to detect contradictions in project-related communications, calculate similarity/coherence scores between sentence pairs, and categorize issues based on criticality, automatically scheduling project meetings to address them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to handle project data, then the system is simple and easy to implement, but it cannot efficiently process large volumes of complex structured, semi-structured, and natural language data to identify issues in early phases
Solution Approach 1:
The patent segments the complex data processing task into multiple specialized modules: a data ingestion module that handles different data formats (structured, semi-structured, natural language), a contextual embedding module that transforms text into vector representations, and an issue detection module that analyzes semantic relationships. This segmentation allows each module to specialize in specific processing tasks, improving overall efficiency while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces contextual embeddings as an intermediary representation layer between raw project data and issue detection algorithms. By transforming text data into vector embeddings that capture semantic meaning and contextual relationships, the system bridges the gap between unstructured data and structured analysis, enabling efficient issue identification without requiring complex rule-based processing of raw text.
2Measurement precision
If manual issue identification methods are used, then the system complexity is low, but it cannot effectively evaluate and identify problems in their early phases due to the massive volume and complexity of data
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computational approaches. Specifically, it uses natural language processing algorithms and machine learning models to automatically analyze project communications, extract issues, and evaluate their significance. This substitution enables the system to process large volumes of data with high precision while identifying issues in early stages, though it increases system complexity through the integration of advanced computational models.
3Reliability
If comprehensive data analysis is performed to identify all potential issues, then issue identification completeness is improved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent implements a two-stage analysis approach that applies partial action strategically. In the first stage, it performs lightweight filtering to identify potentially relevant data segments using simple keywords and patterns. In the second stage, it applies comprehensive semantic analysis only to these filtered segments using contextual embeddings. This approach ensures high issue identification completeness for critical issues while reducing overall processing time by avoiding exhaustive analysis of all data.
Solution Approach 2:
The system employs periodic analysis intervals where comprehensive data analysis is performed at scheduled intervals rather than continuously. Between these periodic comprehensive analyses, the system uses lighter monitoring mechanisms to detect obvious issues. This periodic approach maintains high reliability for issue detection while significantly reducing computational resource consumption and processing time compared to continuous comprehensive analysis.
Data Source
AI summary
Method and apparatus for dynamic remediation. A set of records associated with a project is accessed. The set of records is processed using one or more natural language processing techniques to generate textual data comprising a plurality of pairs of sentences corresponding to one or more topics associated with the project. An issue for at least one topic associated with the project is identified based on the textual data, comprising identifying a pair of sentences that comprises a first sentence and a second sentence, calculating a sentence similarity score by comparing the first and second sentences using a similarity metric, and determining that the sentence similarity score satisfies one or more criteria. In response to determining that the one or more criteria are satisfied, a project meeting for the issue is scheduled based at least in part on a criticality of the issue.