Incipient Entity Record Identification via Dynamic Net Scoring
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Solution Overview
Problem
Conventional methods for identifying emerging influential entities in industries are prone to human judgment bias, are laborious and time-consuming, and fail to dynamically adapt to changing associations and user requirements, leading to inefficient and unreliable record maintenance.
Innovation Solution
A method and system that analyze field-specific entity records to determine importance scores, weightage scores, and net scores based on user-input, identifying incipient field-specific entity records by segmenting data from various sources and tracking changes over time, minimizing human intervention and bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional techniques (manual tracking, surveys, recommendations) are used to identify emerging influential entities, then human judgment and adaptability are applied, but the process becomes laborious, time-consuming, and prone to bias
Solution Approach 1:
The system enables entity records to self-update their associations and attributes automatically through continuous monitoring of data sources. The automated tracking mechanism allows the database to maintain itself without manual intervention, resolving the contradiction by replacing human labor with autonomous system operations that preserve adaptability while dramatically improving efficiency
Solution Approach 2:
The patent replaces manual mechanical processes (surveys, recommendations, tracking) with automated computational systems. The system uses algorithms to automatically detect associations, calculate importance scores, and update entity records, eliminating human labor while maintaining the adaptability to identify emerging influential entities through dynamic data analysis
2Measurement precision
If manual tracking of associations between entities is performed, then accurate relationship data can be collected, but the process becomes cumbersome and cannot be regularly updated for large numbers of entities
Solution Approach 1:
The system implements continuous automated monitoring of data sources to track associations between entities. Rather than periodic manual updates, the system continuously crawls and analyzes data sources, maintaining up-to-date association information for all entity records without interruption or manual intervention, thus achieving both accuracy and timeliness
Solution Approach 2:
The automated tracking system allows the database to self-maintain association data without external human input. The system automatically detects new associations, updates existing ones, and removes outdated connections, enabling accurate and continuous tracking of entity relationships at scale without manual effort
3Productivity
If conventional techniques analyze data within constant time duration, then analysis is manageable, but changes achieved by emerging entities outside this time period are overlooked
Solution Approach 1:
The system dynamically adjusts its analysis time window based on the rate of change detected in the data. When emerging entities show rapid growth or significant changes, the system expands its temporal scope to capture these developments, while maintaining manageable analysis through automated processing. This dynamic approach ensures both completeness of detection and operational feasibility
Solution Approach 2:
The system continuously monitors changes in entity importance scores and associations, using this feedback to adjust its analysis parameters and time scope. When significant changes are detected, the system automatically expands its temporal analysis window to capture the full extent of emerging trends, ensuring no important developments are missed while maintaining analytical manageability through automated adaptation
Data Source
AI summary
A method of identifying incipient field-specific entity records required by a user. The method includes receiving user-input of a specific field, wherein the specific field corresponds to a specific field segment, obtaining field-specific entity records associated with the specific field segment, analyzing each of the field-specific entity records to determine an importance score, identifying at least one pair of field-specific entity records having at least one similar entity attribute and designating relations between them, determining weightage score of each relation between the at least one pair of field-specific entity records, determining change in the importance score and change in weightage scores of each relation associated with each field-specific entity record over a predefined duration of time, determining net score of each of the field-specific entity records and identifying incipient field-specific entity records based on the net score of each of the field-specific entity records.