Module Selection Engine for Automated Completion Tracking

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Solution Overview

Problem

Existing systems struggle to accurately and efficiently generate and track module interactions in a network environment, particularly for subordinate users, leading to inefficiencies in resource distribution and module selection.

Innovation Solution

A module selection and tracking system that uses a module selection engine to determine and present modules to subordinate users, track interactions, and distribute resources based on module completion, reducing manual input and conserving computing resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual tracking of module interactions is implemented, then measurement precision of module completion is improved, but loss of time and productivity deteriorate due to manual input requirements

Engineering Contradiction:

Improvemodule completion tracking accuracyVSAvoidtime for manual input

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically tracks module interactions by detecting user actions on module interface components without requiring manual input. The tracking module monitors completion statuses autonomously, allowing the system to serve itself in data collection and reducing dependency on manual tracking operations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical tracking operations with an automated electronic tracking system. The tracking module uses electronic detection of user interactions with module interface components, substituting manual data entry with automated electronic monitoring to improve efficiency and reduce time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated module selection engine is deployed, then productivity is improved through automation, but device complexity increases due to training and selection mechanisms

Engineering Contradiction:

Improvemodule selection efficiencyVSAvoidengine training and selection system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The module selection engine is trained in advance using training sets containing supervisor-account-specific acceptable modules and general acceptable modules. This preliminary training action prepares the engine to automatically select appropriate modules without requiring complex real-time decision-making, thereby improving productivity while managing complexity through pre-computation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The training process is divided into stages with different training sets - first using supervisor-specific modules, then general acceptable modules. This segmentation of the training process allows the complex task of engine development to be broken into manageable stages, reducing overall system complexity while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If real-time tracking of module interactions is implemented, then measurement precision is improved, but use of energy and computing resources worsens due to continuous monitoring

Engineering Contradiction:

Improveinteraction tracking accuracyVSAvoidcomputing resources for tracking

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The tracking module monitors module interface components and automatically determines completion statuses by detecting specific user actions. Rather than continuous monitoring, the system skips through tracking by event-driven detection of completion-critical actions, maintaining measurement precision while reducing overall computing resource consumption by only processing relevant events.

Inventive Principle:

Principle #21Skipping (Rushing through)

Data Source

PatentUS12444009B2Systems and methods for generating and training a module selection engine for distribution allocation in a network environment

Publication Date: 2025.10.14 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for generating and training a module selection engine for distribution allocation in a network environment. The present invention is configured to receive a subordinate user account identifier associated with a subordinate user account; determine a supervisor user account identifier associated with the subordinate user account identifier; apply a module selection engine to the subordinate user account identifier, wherein the module selection engine determines a plurality of modules to present to the subordinate user based on the subordinate user account identifier; generate, by the module selection engine, a plurality of module interface components; transmit the plurality of module interface components to a user device associated with the subordinate user account; and determine a plurality of module completion statuses for each module of the plurality of module interface components.