Entity-Specific Data Analysis Engine for False Positive Filtering
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Solution Overview
Problem
Conventional data intelligence systems lack comprehensive logic and infrastructure for entity-specific data analysis, leading to reduced accuracy, scalability issues, and inefficiencies in processing large datasets, particularly in handling structured and semi-structured human-readable content, resulting in incomplete insights and increased computational intensity.
Innovation Solution
An entity-specific data analysis engine utilizing language models (e.g., LLMs, SLMs, MoE, multi-modal models) for personalized data analysis, employing bidirectional volumetric analysis, few-shot prompting, and iterative filtering to create bespoke filters and generate entity-specific data analysis outputs, tailored to the unique characteristics and operations of an entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data intelligence systems process large datasets, then data volume handling is achieved, but analysis accuracy and scalability deteriorate
Solution Approach 1:
The patent segments the data processing task by first performing bidirectional volumetric analysis to identify relevant communications, then applying entity-specific filters and probe questions to subsets of data. This segmentation allows accurate analysis of large datasets without processing everything at once, resolving the contradiction between data volume handling and analysis accuracy.
Solution Approach 2:
The patent applies entity-specific filters and customized probe questions tailored to each entity's unique characteristics, operations, and objectives. This local quality approach ensures that analysis accuracy is optimized for each specific entity while still handling large datasets through the volumetric analysis framework.
2Productivity
If conventional systems process large datasets, then data processing is performed, but computational intensity and complexity increase
Solution Approach 1:
The patent performs preliminary bidirectional volumetric analysis to identify and filter relevant communications before applying complex entity-specific analysis. This preliminary action reduces the computational complexity of subsequent processing steps while maintaining high data processing capability through efficient filtering and classification.
Solution Approach 2:
The patent extracts and separates relevant entity communications from non-relevant content through bidirectional volumetric analysis and filtering mechanisms. By taking out only the relevant data for further analysis, the system reduces computational complexity while maintaining high productivity in processing large datasets.
3Adaptability or versatility
If generic data analysis is used, then broad coverage is achieved, but adaptability to entity-specific needs deteriorates
Solution Approach 1:
The patent implements dynamic entity-specific filters and probe questions that adapt to each entity's unique characteristics, operations, and objectives. The system dynamically adjusts its analysis parameters and filtering criteria based on the specific entity being analyzed, achieving high adaptability without requiring overly complex manual configuration.
Solution Approach 2:
The system performs self-configuration by automatically generating entity-specific filters and analysis parameters based on the input data and entity profile. This self-service capability enables high adaptability to entity-specific needs while reducing the operational complexity of system configuration and setup.
Data Source
AI summary
Methods, systems, and computer storage media for providing entity-specific data analysis using an entity-specific data analysis engine in a data intelligence system are described. The entity-specific data analysis engine can be an LM-based system that supports generating and communicating entity-specific data analysis output. In operation, a dataset associated with an entity is accessed. A bidirectional volumetric analysis output is generated based on executing a plurality of bidirectional volumetric analysis operations against the dataset. A plurality of probe questions and a plurality of data analysis axes associated with a focus area are generated for analyzing the bidirectional volumetric analysis output. Using the bidirectional volumetric analysis output, the plurality of probe questions, and the plurality of data analysis axes, an entity-specific data analysis output is generated, based in part on identifying false positive trends in the dataset and defining rules to filter out the false positives from the entity-specific data analysis output.