Tiered Condition Evaluation Using Human and Neural Network Allocation
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Solution Overview
Problem
Existing technologies face challenges in efficiently evaluating the existence of conditions due to difficulties in adapting to changing circumstances, balancing human and automated evaluations, ensuring accuracy, and scaling evaluation processes, which can lead to inconsistency and loss of quality control.
Innovation Solution
A system and method for efficiently scaling condition existence evaluation through human and artificial neural network allocation, utilizing a tiered evaluation process that dynamically adjusts resource allocation based on load and resource constraints, including human users and artificial neural networks to balance evaluation loads and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human users perform evaluations manually, then evaluation accuracy and adaptability improve, but evaluation time and resource consumption increase
Solution Approach 1:
The evaluation system is segmented into multiple tiers: automated neural network evaluation tier and manual human evaluation tier. The system divides evaluation tasks based on complexity and requirements, routing simple evaluations to automated systems and complex evaluations to human experts, thereby resolving the contradiction between accuracy and time consumption
Solution Approach 2:
An intermediary evaluation engine is introduced that manages the coordination between automated neural networks and human evaluators. This intermediary layer dynamically assigns evaluations to appropriate tiers, filters out simple cases for automated processing, and directs complex cases to human experts, optimizing the balance between speed and accuracy
2Productivity
If automated neural networks perform evaluations, then evaluation speed and scalability improve, but adaptability to changing circumstances deteriorates
Solution Approach 1:
The evaluation system dynamically adjusts its composition based on incoming evaluation requests. The intermediary engine analyzes each request and dynamically routes it to either automated or human evaluation tiers, allowing the system to adapt to changing circumstances while maintaining high throughput for routine evaluations
Solution Approach 2:
The system creates a universal evaluation platform that handles multiple types of evaluations through both automated and manual tiers. The same infrastructure can process simple pattern-matching evaluations via neural networks and complex contextual evaluations via humans, providing versatility across different evaluation scenarios
3Reliability
If more evaluation resources are allocated, then evaluation capacity and consistency improve, but system complexity and resource constraints worsen
Solution Approach 1:
The evaluation capacity is segmented across multiple independent tiers rather than requiring a single monolithic system. The automated tier handles high-volume routine evaluations while the human tier handles complex cases, distributing the workload and maintaining consistency without requiring all resources to be constantly available
Solution Approach 2:
The intermediary evaluation engine automatically manages resource allocation without external intervention. It monitors system state, selects appropriate evaluation tiers, and coordinates resources dynamically, reducing the need for complex manual resource management while maintaining reliable evaluation consistency
Data Source
AI summary
Disclosed are a method, a device, and/or a system of efficient scaling of condition existence evaluation through human and/or artificial neural network allocation. In one embodiment, a system for efficiently allocating requests for electronic evaluation of existence conditions includes one or more processors and one or more computer readable non-transitory media including instructions that when executed: receive a first evaluation request to determine existence of a first condition received from a device of a first user alleging existence of the first condition; select a first evaluation tier to evaluate the first condition data including one or more human users and/or an artificial neural network; receive a second evaluation request to determine existence of a second condition; determine an evaluation load of the first evaluation tier exceeds evaluation capacity; and select a second evaluation tier for evaluation to reduce use of human evaluation and/or conserve computing resources.