Dynamic Entity Activity Graphs for Incomplete Behavior Data

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Solution Overview

Problem

Existing systems fail to provide holistic and dynamic representations of entity behaviors and activities, relying on static and out-of-date single-entity evaluations that are difficult to update and often incomplete due to inconsistent data formats and missing records.

Innovation Solution

A computer-based method generates a dynamic entity activity graph by aggregating entity-specific and activity-related data records, using machine learning and data resolution engines to merge and normalize data across multiple sources, forming a dynamic network graph that updates in real-time to reflect current relationships and activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If single-entity oriented evaluation is used, then data processing is simple, but the representation of entity behaviors is incomplete and static

Engineering Contradiction:

Improvedata processing complexityVSAvoidcompleteness of entity behavior representation

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent merges multiple single-entity data records into a unified group-level representation by identifying common entities across different business records. This combining approach aggregates entity-specific data records and entity-related activity records to create a comprehensive group-level view that captures holistic entity behaviors while maintaining manageable processing complexity through systematic data integration.

Inventive Principle:

Principle #5Merging (Combining)

2Device complexity

If static single-source data evaluation is used, then data storage is simple, but the data is out-of-date and incomplete

Engineering Contradiction:

Improvedata storage complexityVSAvoidcurrency and completeness of entity data

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent implements dynamic data structures including dynamic entity graphs and activity data structures that automatically update as new entity-specific data records and activity records are received. The system transitions from static single-source storage to dynamic multi-source aggregation, where the entity activity graph continuously evolves to reflect current entity relationships and behaviors, ensuring data currency and completeness without overwhelming storage complexity.

Inventive Principle:

Principle #15Dynamics

3Loss of information

If holistic and dynamic entity representation is implemented, then entity behavior analysis is comprehensive, but system complexity increases

Engineering Contradiction:

Improvecompleteness of entity behavior representationVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the complex task of holistic entity representation into distinct modular components: entity resolution engines that process individual entity records, activity resolution engines that handle activity data, dynamic entity graphs that store entity relationships, and activity data structures that capture behavioral patterns. This segmentation allows comprehensive entity behavior analysis while managing system complexity through organized, reusable modules with well-defined interfaces.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary data structures including the entity activity graph and activity data structure that mediate between raw entity-specific data records and final analytical outputs. These intermediaries transform complex multi-source data into structured representations that facilitate comprehensive entity behavior analysis while simplifying downstream processing and reducing overall system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If real-time dynamic updating of entity relationships is implemented, then entity activity analysis is current, but processing requirements increase

Engineering Contradiction:

Improvecurrency of entity activity dataVSAvoiddata processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent performs preliminary actions by pre-processing entity-specific data records and activity records upon receipt, resolving entities and activities in advance, and organizing data into the entity activity graph structure before queries are executed. This preliminary organization of data into normalized, relationship-based structures enables real-time dynamic updating and efficient querying, as the heavy lifting of data integration and relationship establishment is completed proactively rather than reactively during query processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250321970A1Computer-based systems for dynamic data discovery and methods thereof

Publication Date: 2025.10.16 CAPITAL ONE SERVICES LLC

AI summary

In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.