Contextual Entity Tagging for Adaptive Automation Tasks

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems struggle to automate entity characterization and sensitivity discovery in dynamic data environments, particularly when encountering new scenarios or unconventional entity formats, due to their reliance on prior knowledge of entity structures and characteristics.

Innovation Solution

A system that identifies context parameters such as hierarchical, parallel, and self-context parameters to infer entity characteristics, assigns entity tags based on these parameters, and triggers automation tasks accordingly, without requiring prior knowledge of entity structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If pattern matching or machine learning-based techniques are used for automated tag identification, then automation of entity characterization is achieved, but the system fails when encountering new domains or unconventional entity formats due to reliance on prior knowledge

Engineering Contradiction:

Improveautomation of entity characterizationVSAvoideffectiveness in new domains

Core Design Contradiction:

Extent of automationVSAdaptability or versatility

Solution Approach 1:

The system performs self-learning by automatically discovering entity characteristics and relationships from data without requiring pre-defined patterns or training data. The contextual analysis module autonomously identifies hierarchical, parallel, and self-context parameters, enabling the system to adapt to new domains and unconventional entity formats independently

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes its analysis parameters based on the data it encounters. By identifying different types of context parameters (hierarchical, parallel, self-context) and adjusting its inference mechanisms accordingly, the system can handle diverse entity formats and domains without requiring retraining or reconfiguration

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional manual methods for identifying and classifying entities are used, then accuracy in entity characterization is maintained, but the process becomes impractical and error-prone as data volume and complexity grow

Engineering Contradiction:

Improveaccuracy in entity characterizationVSAvoidprocessing speed and efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical analysis with automated computational systems. The contextual analysis module uses algorithmic inference to identify entity characteristics, while the tag assignment module automatically applies tags based on inferred attributes, eliminating manual intervention and significantly improving processing speed while maintaining accuracy through systematic analysis

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If pattern matching techniques are used for entity tagging, then known entity types can be identified accurately, but the system cannot handle new scenarios or unconventional entity formats

Engineering Contradiction:

Improveidentification accuracy of known entitiesVSAvoidhandling of new scenarios

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system transitions from static pattern matching to dynamic contextual analysis. The contextual analysis module continuously adapts its inference mechanisms based on the data structure it encounters, automatically adjusting to handle new scenarios and unconventional entity formats while maintaining high accuracy for known entities through learned patterns

Inventive Principle:

Principle #15Dynamics

4Measurement precision

If machine learning models are trained on annotated datasets, then entity characteristics can be recognized with high accuracy, but the system requires prior knowledge and cannot generalize to new domains

Engineering Contradiction:

Improverecognition accuracy of entity characteristicsVSAvoidgeneralization to new domains

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary contextual analysis to understand data structures and relationships before making inferences about entity characteristics. By first identifying hierarchical, parallel, and self-context parameters, the system builds a foundational understanding that enables accurate characterization without requiring pre-trained models or annotated datasets

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260003671A1System and method for automated identification and inference of characteristics of entities

Publication Date: 2026.01.01 ABLUVA PTE LTD

AI summary

A method for managing automation tasks for a subject entity is disclosed. The method includes identifying context parameters associated with the subject entity among a set of entities. The context parameters comprise a hierarchical context parameter, a parallel context parameter, or a self-context parameter. Further, the method includes inferring characteristics of the subject entity based on the identified context parameters and relationships between the subject entity and one or more other entities within the set of entities. The characteristics indicate an operational and contextual attributes of the subject entity. Furthermore, the method includes assigning entity tags to the subject entity based on the characteristics. The entity tags indicate a representation of the subject entity's contextual and operational attributes. Furthermore, the method includes triggering an automation task associated with the subject entity based on the assigned entity tags.