Conditional attributes

The computing system addresses inefficiencies in data classification and integration by inferring dynamic data structures and automating data management, enhancing data modeling and integration capabilities for improved scalability and insights.

WO2026112414A1PCT designated stage Publication Date: 2026-05-28RELTIO INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Filing Date
2025-11-21
Publication Date
2026-05-28

AI Technical Summary

Technical Problem

Existing data management systems struggle with inefficient and inaccurate manual data classification, lack of seamless integration across multiple data sources, and inadequate modeling of entities, relationships, and interactions, which hinders effective business insights and scalability.

Method used

A computing system that utilizes machine learning models to infer dynamic data structures, automate data classification, and integrate data across a multi-tenant platform, enabling efficient modeling of entities, relationships, and interactions, and providing a graphical user interface for data management and interaction.

Benefits of technology

Facilitates accurate and efficient data classification and integration, allowing businesses to model and understand complex data structures, enhance scalability, and provide real-time insights through a unified data platform.

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Abstract

Ingesting data from one or more data sources, wherein the data is associated with a tenant of a multi-tenant platform. Generating a machine learning model input based on the ingested data. Providing the generated machine learning model input to a machine learning model. Inferring, using the machine learning model, an inferred dynamic data structure, wherein the inferred dynamic data structure includes a subset of entity attributes inferred by the machine learning model from a set of the entity attributes, wherein at least a portion of the entity attributes include conditional entity attributes, wherein the conditional entity attributes depend on the values of one or more of the other entity attributes of the set of the entity attributes. Presenting, via a graphical user interface (GUI), a visual representation of the inferred dynamic data structure. Tracking user interactions received through the GUI associated with the visual representation of the inferred dynamic data structure. Dynamically adjusting, using one or more other machine learning models, the inferred dynamic data structure based on the tracked user interactions. Presenting, via the GUI, the dynamically adjusted inferred dynamic data structure.
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