AI Entity Profile Analysis for Accurate Query Aggregation

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Solution Overview

Problem

Existing entity analysis systems rely heavily on manual data extraction and processing, leading to inefficiencies, errors, and limited scalability, especially when handling high-dimensional data, and lack continuous learning capabilities.

Innovation Solution

An AI-driven data analysis system that integrates structured and unstructured data using a machine learning model to generate comprehensive entity profiles, enabling automated data operations, aggregation, and real-time insights.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual data extraction and processing is used, then flexibility in handling diverse data sources is maintained, but processing efficiency and accuracy deteriorate due to substantial human effort and errors

Engineering Contradiction:

Improvedata processing efficiencyVSAvoiddata processing accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces manual mechanical data extraction and processing with an AI-driven system that uses machine learning models to automatically extract, validate, and process data from multiple sources. This substitution eliminates human error while maintaining the ability to handle diverse data formats through adaptive learning algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements self-service through automated data validation, quality assessment, and continuous learning mechanisms. The AI model automatically improves its processing capabilities by learning from new data patterns, reducing the need for manual intervention while enhancing both efficiency and accuracy over time.

Inventive Principle:

Principle #25Self-service

2Productivity

If rule-based systems or algorithmic models are introduced for automation, then processing efficiency improves, but the ability to manage high-dimensional data complexity deteriorates

Engineering Contradiction:

Improveautomation efficiencyVSAvoiddata complexity management capability

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent transforms the approach to handling high-dimensional data by changing the parameters of the processing system itself. Instead of using fixed rule-based algorithms, the system employs machine learning models that can dynamically adjust their processing parameters based on the complexity and dimensionality of the input data, enabling effective management of complex datasets.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system introduces dynamics by using adaptive machine learning models that can evolve and adjust their processing strategies in real-time based on data characteristics. This dynamic capability allows the system to efficiently manage varying levels of data complexity without requiring manual reconfiguration of processing rules.

Inventive Principle:

Principle #15Dynamics

3Ease of manufacture

If traditional entity analysis approaches are used, then simplicity of implementation is maintained, but scalability deteriorates when handling vast amounts of data from varied sources

Engineering Contradiction:

Improvesystem implementation simplicityVSAvoiddata scalability

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements a universal AI-driven platform that can handle multiple data types, sources, and analysis requirements through a single system architecture. The machine learning models are designed to be multi-functional, capable of processing structured and unstructured data from diverse sources while maintaining consistent performance and enabling easy scaling.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Device complexity

If static data processing systems are used, then system simplicity is maintained, but the capacity for continuous learning and adaptation deteriorates

Engineering Contradiction:

Improvesystem structure simplicityVSAvoidcontinuous learning capability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent incorporates feedback mechanisms where the AI system continuously learns from processing results and new data inputs. The machine learning models receive feedback from data quality assessments and analysis outcomes, automatically adjusting their parameters and approaches to improve future processing, thereby enabling continuous adaptation without significantly increasing system structural complexity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12572551B1User interaction within a data analysis system

Publication Date: 2026.03.10 ALPHA DEAL LLC

AI summary

Methods, apparatuses, system, devices, and computer program products for user interaction within a data analysis system are disclosed that include a controller receiving user queries via a user interface; in response to receiving the user queries, performing data operations on the entity profiles in the database, wherein the data operations include at least one of: sorting the entity profiles based on composite scores associated with the entity profiles; searching the entity profiles based on the specified one or more parameters in the user queries; or combining the entity profiles by correlating data from multiple profiles based on the composite scores of the entity profiles, to generate an aggregated result set; generating a response to the user queries; and outputting, by the controller, the response to the user interface for display to the user.