Integrated Predictive Decision System for Deep Web Data Simulation

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Solution Overview

Problem

Current systems fail to integrate the retrieval of relevant information from diverse sources, analyze high-volume data, and use it for predictive decision-making and simulation, as they typically handle only single aspects of data management and analysis.

Innovation Solution

A fully integrated system incorporating a high-volume deep web scraper, data retrieval engine, directed computational graph module, and decision/action path simulation engine to optimize decision-making and simulate outcomes using machine-mediated prediction algorithms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple separate software products are used to handle different aspects of data management and analysis, then each product can specialize in its specific function, but the system complexity increases and integration between functions becomes difficult

Engineering Contradiction:

Improvefunctional specializationVSAvoidsystem integration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent combines multiple previously separate software functions into a single integrated platform. The system merges data collection, deep web scraping, data retrieval, computational graph construction, and decision simulation capabilities into one unified architecture, eliminating the need for multiple separate software products while maintaining all specialized functions.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The integrated system performs multiple functions through a single platform. The system can simultaneously scrape deep web data, retrieve operational data, construct computational graphs, analyze high-volume data, and simulate decision outcomes - all within one universal system that serves multiple purposes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If a fully integrated system is created to handle data retrieval, analysis, and predictive decision-making, then functional versatility improves, but device complexity increases

Engineering Contradiction:

Improveintegrated functionalityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The integrated system is organized into distinct modular components including a deep web scraper module, data retrieval engine, computational graph construction module, data analysis engine, and decision simulation module. Each module handles specific tasks independently while communicating through standardized interfaces, making the complex system manageable and maintainable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs intermediary components such as the computational graph module that acts as a mediator between raw data retrieval and complex analytical processing. The graph structure serves as an intermediate representation layer that organizes data relationships before feeding into the decision simulation engine, simplifying the overall information flow.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Quantity of substance

If high-volume data is retrieved from multiple sources including deep web scraping, then data quantity and diversity increase, but data processing time and computational resources increase

Engineering Contradiction:

Improvedata volumeVSAvoiddata processing time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and organizing data as it is collected from multiple sources. The computational graph is constructed in advance to establish data relationships and dependencies before full analysis begins, enabling more efficient processing of high-volume data sets.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces traditional sequential data processing with a computational graph-based parallel processing architecture. The graph structure enables simultaneous evaluation of multiple data paths and relationships, significantly reducing processing time compared to traditional linear analysis methods.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11295262B2System for fully integrated predictive decision-making and simulation

Publication Date: 2022.04.05 QOMPLX INC

AI summary

A system for fully integrated predictive decision-making and simulation having a high-volume deep web scraper system, a data retrieval engine, a directed computational graph module, and a decision and action path simulation engine. The system receives an analysis campaign configuration comprising analysis parameters for optimizing a decision; retrieves operations data from devices related to the analysis campaign configuration; retrieves supplemental data from deep web extraction related to the analysis campaign configuration; constructs a directed computational graph from the analysis campaign configuration; determines a set of possible prospective actions; simulates the outcome of each prospective action using the data processing pipelines of the directed computing graph as a simulation model; determines an optimal outcome from the parametric analysis by matching the outcome of each prospective action against the analysis parameters; and recommends the prospective action with the optimal outcome as the decision.