Composite AI Data Access for Scalable Entity Analysis

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Solution Overview

Problem

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scalability, especially when handling high-dimensional data, and lack continuous learning capabilities, resulting in outdated data outputs.

Innovation Solution

An AI-driven integrated entity analysis system that aggregates structured and unstructured data using a machine learning model to generate comprehensive entity profiles, providing real-time insights and reducing processing complexity and energy consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual extraction and processing methods are used for entity analysis, then human effort and time are required, but efficiency and scalability are limited

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidmanual processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processing with an automated AI-based system that uses machine learning models to extract, process, and analyze entity data from multiple sources, thereby eliminating human effort and significantly improving processing efficiency and scalability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service entity analysis by automatically gathering data from diverse sources, processing it through AI models, and generating insights without requiring human intervention, allowing the system to serve itself and continuously learn from new data

Inventive Principle:

Principle #25Self-service

2Extent of automation

If rule-based systems or algorithmic models are used for automation, then some automation is achieved, but the ability to manage high-dimensional data complexity is limited

Engineering Contradiction:

Improvedata processing automationVSAvoiddata complexity management capability

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent transforms the approach to handling complex high-dimensional data by changing the parameters of the processing system itself - using AI models with adjustable parameters that can adapt to different data types, dimensions, and relationships, enabling the system to manage complexity that traditional algorithms cannot handle

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system combines multiple data sources, various AI models, and different processing techniques into a composite analytical framework, where each component contributes specific capabilities that together enable comprehensive management of high-dimensional data complexity

Inventive Principle:

Principle #40Composite materials

3Reliability

If traditional entity analysis systems are used, then data processing can be performed, but continuous learning and adaptation capabilities are lacking

Engineering Contradiction:

Improvedata output accuracyVSAvoidcontinuous learning capability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements feedback mechanisms where the AI models continuously learn from new data and analysis results, adjusting their parameters and improving their accuracy over time. The system incorporates feedback loops that allow it to adapt to changing data patterns and improve reliability through continuous learning

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static rule-based approaches to dynamic AI models that can adapt their behavior based on incoming data. The models are designed to be dynamic, continuously updating their understanding of entities and relationships, thereby improving both reliability and adaptability simultaneously

Inventive Principle:

Principle #15Dynamics

4Productivity

If manual extraction methods are used, then data can be processed, but errors and inconsistencies increase due to subjective processing

Engineering Contradiction:

Improvedata processing capacityVSAvoiddata extraction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces subjective manual extraction with objective AI-based extraction that applies consistent rules and patterns across all data, eliminating human bias and errors while maintaining high processing capacity. The AI models provide measurable and reproducible results that improve both productivity and precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12406084B1Providing access to composite AI-generated data

Publication Date: 2025.09.02 ALPHA DEAL LLC

AI summary

Methods, apparatuses, system, devices, and computer program products for providing access to composite AI-generated data is disclosed that includes a controller of an API server, receiving from a user system, an API call that specifies a request to perform a data operation on an entity database; interpreting the API call to identify the data operation and parameters related to the scoring data; executing the data operation on the entity database according to the specified parameters; generating a response to the API call; and transmitting the response to the user system.