Resource Distribution Command Auto-Fill from Historical Data

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Solution Overview

Problem

Users frequently make errors when completing input fields for resource distribution commands, leading to erroneous information processing, unnecessary consumption of computing, network, and financial resources, and additional costs for reversing these errors.

Innovation Solution

A system that displays a graphical user interface with auto-populated input fields based on historical data, using machine learning to identify correct inputs and requiring user confirmation before resource distribution, thereby preventing erroneous transfers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users manually complete input fields for resource distribution commands, then the system can process resource distribution requests, but users frequently make errors leading to erroneous information processing and unnecessary consumption of computing, network, and financial resources

Engineering Contradiction:

Improveaccuracy of resource distribution commandsVSAvoidconsumption of computing, network, and financial resources

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by automatically populating input fields with historical resource distribution data before the user submits the command. This pre-filling of accurate information based on past patterns prevents user errors and eliminates the need for error correction, thereby reducing unnecessary consumption of computing, network, and financial resources.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by presenting the auto-populated command details to the user for review and confirmation. This feedback loop allows users to verify the accuracy of automatically generated commands before execution, preventing erroneous resource transfers and reducing waste of computational and financial resources.

Inventive Principle:

Principle #23Feedback

2Productivity

If users manually complete multiple input fields for resource distribution commands, then the system can process distribution requests, but the process is time-consuming and error-prone

Engineering Contradiction:

Improvespeed of resource distribution command completionVSAvoidaccuracy of resource distribution commands

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides self-service by automatically generating and completing resource distribution commands using historical data and machine learning models. This automation reduces manual user input requirements while maintaining high accuracy through intelligent data retrieval and prediction, thereby improving both productivity and reliability.

Inventive Principle:

Principle #25Self-service

3Reliability

If the system uses auto-populated input fields based on historical data and machine learning, then erroneous resource transfers are prevented, but the system complexity increases

Engineering Contradiction:

Improveaccuracy of resource distribution commandsVSAvoidsystem complexity for command processing

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system applies universality by using a single machine learning model to handle multiple functions: retrieving historical data, predicting user intent, auto-populating input fields, and validating commands. This multi-functional approach improves reliability while managing system complexity through consolidated processing logic rather than separate specialized components.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces an intermediary layer between user input and command execution that automatically retrieves historical data, processes it through machine learning models, and generates suggested commands. This intermediary mediation layer enhances accuracy by filtering and validating information before it reaches the execution stage, while the modular structure helps manage overall system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12430175B2Systems and methods for electronically augmenting resource distribution commands and facilitating transfer of resources

Publication Date: 2025.09.30 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for electronically augmenting resource distribution commands and facilitating transfer of resources. The present invention may be configured to display, via a user device and to a user, a graphical user interface including input fields for providing commands to perform a resource distribution and receive, from the user and via the graphical user interface displayed via the user device, a single user input at the graphical user interface. The present invention may be configured to, in response to receiving the single user input, automatically identify, based on historical resource distribution data associated with the user, inputs corresponding to each of the input fields and display, via the graphical user interface displayed via the user device and in each input field of the input fields, an input, of the inputs, corresponding to the input field to complete the commands.