Urgency-Based Object Prioritization Using Biological Data
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Solution Overview
Problem
Existing systems for physically transferring objects to users often result in a poor user experience, particularly when prioritization is based on purchasing power or habits, failing to effectively address user-specific needs and urgency of object-addressable maladies.
Innovation Solution
A system and method that utilize a computing device to receive biological extraction data from users, train an urgency machine-learning model to determine urgency metrics, and generate an object prioritization list based on these metrics, ordering candidate objects to optimize physical transfer according to urgency, utility, and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prioritization is based on purchasing power or habits, then service delivery is simplified, but user experience deteriorates because individual needs and urgency are not addressed
Solution Approach 1:
The system changes the prioritization parameter from purchasing power to urgency metrics derived from biological extraction data. Machine learning models analyze biological markers to determine health urgency levels, dynamically adjusting prioritization parameters based on individual user physiological states rather than static purchasing power metrics.
Solution Approach 2:
The patent replaces the mechanical/commercial prioritization system (based on purchasing power) with a bio-information-based system. Biological extraction data and machine learning algorithms substitute for traditional purchase-based prioritization mechanisms, enabling urgency-based ordering without direct user input.
2Device complexity
If prioritization is based on purchasing power, then system complexity is reduced, but individual user needs and urgency of maladies are not effectively addressed
Solution Approach 1:
The system enables self-service prioritization by automatically analyzing biological extraction data to determine user urgency levels. The machine learning models process biological markers and autonomously generate prioritization orders without requiring users to manually assess or communicate their needs, making the system both simple to operate and highly adaptive to individual conditions.
Solution Approach 2:
The system performs preliminary analysis of biological extraction data to pre-determine urgency levels before physical transfer decisions are made. By预先 analyzing biological markers and predicting malady urgency, the system prepares prioritization information in advance, enabling rapid response to individual user needs without adding operational complexity.
3Measurement precision
If biological extraction data is used to determine urgency metrics, then prioritization accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between biological extraction data and urgency metrics. These models process complex biological data patterns and translate them into interpretable urgency scores, acting as a mediator that handles data processing complexity internally while presenting simplified urgency assessments to the prioritization system.
Data Source
AI summary
A system for generating an object prioritization list for physical transfer, the system comprising a computing device configured to receive a biological extraction of a user, determine, using the biological extraction, a plurality of urgency metrics, wherein determining the plurality of urgency metrics including training an urgency machine-learning model with training data that includes a plurality of entries wherein each entry correlates biological extraction data to metrics of urgency of object-addressable maladies, and determining the plurality of urgency metrics as a function of the urgency machine-learning model, order, using a first ranking machine-learning process, a plurality of candidate objects as a function of the plurality of urgency metrics, and generate an object prioritization list as a function of the ordered plurality of candidate objects.