Graph Data Representation for Automated Entity Eligibility
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Solution Overview
Problem
Existing systems struggle to effectively map events and entities into a meaningful data representation that conveys attributes and attribute properties for eligibility evaluation, necessitating a more efficient and automated method for determining entity eligibility for events.
Innovation Solution
A graph data representation system that includes entity nodes, event nodes, attribute nodes, inclusion and exclusion edges, and machine learning models to generate inbound edges based on attribute associations, enabling automated eligibility determination and event scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual methods are used to map events and entities into data representation for eligibility evaluation, then the system can handle eligibility determination, but the process requires significant manual effort and operational costs
Solution Approach 1:
The patent segments the eligibility determination process into distinct components: event nodes representing events, entity nodes representing entities, attribute nodes representing specific attributes, and edges representing relationships. This segmentation allows automated processing of complex eligibility criteria by breaking down the evaluation into manageable graph traversal operations, resolving the contradiction between automation and complexity.
Solution Approach 2:
The patent introduces a graph data structure as an intermediary representation layer between raw event/entity data and eligibility determination logic. The graph representation with nodes and edges serves as a mediator that transforms unstructured data into a format suitable for automated evaluation, enabling the system to achieve high automation without requiring complex procedural logic.
2Measurement precision
If comprehensive attribute mapping is implemented to accurately represent entity properties for eligibility evaluation, then the accuracy of eligibility determination improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by creating attribute nodes with specific properties tailored to individual eligibility criteria requirements. Each attribute node represents a specific property (e.g., age, location, qualifications) with localized data structures and validation rules appropriate to that attribute type. This allows the system to maintain high measurement precision for each attribute without requiring a uniformly complex data structure across all attributes.
Solution Approach 2:
The patent transforms the complexity of attribute representation by moving from traditional flat data structures to a multi-dimensional graph structure. Attributes are represented as nodes in the graph with relationships to entities and events, adding a dimensional layer that organizes complexity spatially rather than procedurally. This dimensional transformation enables accurate eligibility evaluation while managing complexity through visual and structural organization.
3Loss of information
If traditional data structures are used to store event and entity information, then the system is simpler to implement, but it cannot efficiently convey attribute properties and relationships for eligibility determination
Solution Approach 1:
The patent merges multiple data representation concepts into a unified graph structure that simultaneously represents entities, events, attributes, and relationships. Instead of using separate data structures for each type of information, the graph model combines them into a single interconnected representation where nodes can represent any entity type and edges represent various relationship types. This merging preserves all attribute properties while managing complexity through a unified model.
Solution Approach 2:
The patent creates a universal graph data structure that can represent multiple types of information (entities, events, attributes, relationships) using the same fundamental building blocks. The node and edge structures serve multiple functions: nodes can represent entities or events or attributes, and edges can represent various relationship types. This multi-functionality allows the system to preserve comprehensive attribute information without requiring separate complex data structures for each information type.
4Productivity
If automated eligibility determination is implemented using graph representations, then processing speed and efficiency improve, but the system requires sophisticated machine learning models and algorithms
Solution Approach 1:
The patent applies preliminary action by pre-processing event and entity data into graph representations before eligibility determination. The graph structure is constructed in advance with all attributes, relationships, and connections established beforehand. This preliminary structuring enables efficient automated querying and evaluation during the actual eligibility determination process, achieving high productivity without requiring complex machine learning models during the evaluation phase itself.
Data Source
AI summary
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.