Mixed Effect Predictive Model for Performance Estimation
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Solution Overview
Problem
Manually analyzing large batches of electronic records to predict future performance estimation values is a time-consuming and error-prone process, especially when dealing with thousands of records and multiple influencing factors.
Innovation Solution
A system and method that uses a data analytics mixed effect predictive model, accessed by a server, to automatically designate record characteristic values as fixed and random effect variables, generating faster and more accurate future performance estimation values by transmitting indications to an interactive user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of electronic records is performed, then accuracy of performance estimation can be maintained through human judgment, but time consumption increases significantly when dealing with thousands of records
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses data analytics and machine learning models to perform performance estimation. The system automatically accesses electronic records, applies predictive models, and generates performance values without human intervention, thereby eliminating time consumption while maintaining or improving accuracy through consistent algorithmic processing.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between raw electronic records and performance estimation results. This intermediary system processes records through structured data analytics pipelines, applying predefined models and algorithms to transform unprocessed data into actionable performance estimates, thereby resolving the contradiction between speed and accuracy.
2Adaptability or versatility
If manual analysis of electronic records is performed, then flexibility in adjusting analysis parameters can be maintained, but error rate increases with substantial numbers of records
Solution Approach 1:
The patent implements a dynamic automated system where analysis parameters and models can be adjusted and reconfigured without manual reprogramming. The system allows for dynamic modification of predictive models, data sources, and analysis parameters while maintaining consistent processing, thereby preserving flexibility while eliminating human errors through automated execution of adjusted parameters.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated system continuously monitors and validates its own performance, comparing results against expected outcomes and adjusting parameters accordingly. This self-correcting feedback loop maintains reliability by detecting and correcting errors automatically, while preserving flexibility through adaptive parameter adjustment based on performance metrics.
3Productivity
If automated predictive models are used, then processing speed increases for large batches of records, but system complexity increases
Solution Approach 1:
The patent segments the automated analysis system into distinct modular components: data access modules, model application modules, and result generation modules. Each component performs a specific function independently, allowing for streamlined processing that increases speed while managing complexity through clear separation of concerns. The segmented architecture enables parallel processing of multiple records through different model variants.
Solution Approach 2:
The patent implements universal predictive models that can process multiple types of electronic records and apply to various performance estimation scenarios through a single unified system. This multi-functional approach increases processing speed by eliminating the need for separate specialized systems for different record types, while managing complexity through standardized processing frameworks that handle diverse inputs uniformly.
Data Source
AI summary
According to some embodiments, a server may access a data store containing electronic records, each electronic record representing a risk association for an entity in connection with a plurality of relationships, and each electronic record may contain a set of record characteristic values. The server may automatically designate a first sub-set of the set of record characteristic values as fixed effect variables and a second sub-set as random effect variables. A data analytics mixed effect predictive model may then generate, based on the fixed and random effect variables, a future performance estimation value for the risk association of each entity in connection with its plurality of relationships. An indication associated with the future performance estimation value for the risk association of at least one entity in connection with its plurality of relationships may then be transmitted to generate an interactive user interface display.