Entity Data Growth Factor Modeling for Complex-Scale Accuracy
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Solution Overview
Problem
Existing automated models fail to account for the complexity of systems at larger scales, leading to inadequate performance.
Innovation Solution
An apparatus and method for determining a growth factor using a processor and memory to receive entity data, generate an entity process, determine a growth factor, and optimize characteristics affecting growth ability, generating an interface element to depict progression and transmit to a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing automated models are used to determine growth factors, then the process can be automated, but the models fail to account for system complexity at larger scales
Solution Approach 1:
The patent segments the growth factor determination process into multiple components: receiving entity data, generating an entity process, determining growth factors, optimizing characteristics, and generating interface elements. This segmentation allows the system to handle complexity by breaking down the overall problem into manageable sub-problems that can be addressed systematically.
Solution Approach 2:
The system dynamically adapts to different entity types and data structures by generating entity processes that are specific to each entity. The growth factor determination is not static but adjusts based on the complexity and characteristics of the input data, allowing the automated model to maintain accuracy across varying system complexities.
2Productivity
If the system analyzes entity data to determine growth factors, then growth optimization can be achieved, but the process complexity increases
Solution Approach 1:
The system uses a universal approach where a single framework can analyze different entity types (businesses, organizations, systems) by generating appropriate entity processes based on the input data. This multi-functionality reduces the need for separate specialized models for each entity type, thereby managing complexity while maintaining broad optimization capability.
Solution Approach 2:
The system automatically generates entity processes and determines growth factors without requiring manual intervention or pre-defined models for each specific case. The entity process generation step creates tailored analysis procedures based on the input data structure, allowing the system to serve itself by adapting to new entity types without external reconfiguration.
3Loss of information
If the system generates interface elements to depict progression, then user understanding of growth is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary processing by generating the entity process and determining growth factors before creating interface elements. This sequencing allows the complex analysis to be completed in advance, and the interface generation can then focus only on visualizing the pre-computed results, reducing the time required for the entire process.
Solution Approach 2:
The interface elements are generated as representations or copies of the underlying entity process and growth factor data. Rather than processing the raw data repeatedly for display, the system creates simplified visual representations that convey the same information, reducing processing time while maintaining information integrity for the user.
Data Source
AI summary
An apparatus and method for determining a growth factor. The method may include receiving, by a processor, entity data and determining, by the processor, a growth factor. Further, the method may include generating, by the processor, at least an interface element as a function of the growth factor and transmitting, by the processor, the at least an interface element to a graphical user interface (GUI).