Proxy Nodes in Graph Models for Entity Eligibility

The patent segments the entity evaluation process into multiple independent criteria groups (e.g., demographic criteria, clinical criteria, laboratory criteria) that can be evaluated separately. Each criteria group is assigned a weight, and entities are evaluated incrementally through these segments rather than requiring complete assessment of all attributes simultaneously, thus improving processing speed while maintaining selection accuracy.

The patent implements partial action by allowing entity eligibility determination to proceed with incomplete attribute data. Entities can be partially evaluated based on available criteria groups, and the system continues processing without waiting for complete data collection, thereby significantly improving productivity while using confidence scores to maintain measurement precision for final eligibility decisions.

The patent performs preliminary actions by pre-defining and weighting multiple criteria groups before entity assessment begins. The system prepares the evaluation framework in advance, organizing criteria into hierarchical groups with assigned weights, so that when entities are assessed, the evaluation can proceed efficiently through predetermined pathways rather than requiring ad hoc analysis, thus reducing time loss while maintaining precision.

The patent introduces dynamics by making the evaluation process adaptive and incremental. Entities are assessed dynamically as attributes become available, with the system adjusting processing based on confidence scores and data completeness. This dynamic approach allows early termination for clearly eligible or ineligible entities and continues detailed assessment only where needed, reducing overall time loss while preserving measurement precision.

The patent applies local quality by assigning different weights to different criteria groups based on their reliability and importance. Not all criteria are treated equally; instead, each criteria group receives a weight reflecting its contribution to reliable conclusion formation. This allows the system to adapt to varying data quality across different attribute types while maintaining overall conclusion reliability through weighted aggregation.

The patent uses parameter changes by introducing confidence scores and weight parameters that adjust the influence of different data sources. When data reliability varies across entities or attribute types, the system modifies parameters such as criterion weights and confidence thresholds to compensate, thereby maintaining conclusion reliability while preserving adaptability to diverse data sources.

The patent segments the graph model into distinct node types (criteria nodes, entity nodes, proxy nodes) and connection types, making the complexity manageable through structured organization. Proxy nodes are selectively introduced only where data reliability metrics indicate their usefulness, rather than throughout the entire model, thus improving productivity while controlling device complexity through targeted application.

The patent introduces proxy nodes as intermediary elements that mediate between entity attributes and criteria evaluation when direct data is unreliable or unavailable. These proxy nodes serve as intermediate computational steps that can infer or substitute missing information, improving productivity by enabling continued processing while the added complexity is localized to specific intermediary connections rather than the entire system.