ML Resource Distribution System for SMS Instruction Parsing
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Solution Overview
Problem
Current systems require manual database searches and user input to generate resource distributions, consuming significant computing and financial resources and increasing the likelihood of errors when handling high volumes of text-based instructions.
Innovation Solution
An electronic system that uses machine learning to parse text-based instructions, determine sender and recipient aliases, and generate resource distributions by predicting and actualizing distribution elements, thereby automating the resource distribution process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual database searches and user input are used to generate resource distributions, then the system can process text-based instructions, but computing and financial resources are consumed significantly and error likelihood increases when handling high volumes of instructions
Solution Approach 1:
The system uses machine learning models to automatically parse text-based instructions and extract distribution elements without requiring manual user input. The model self-services by learning from historical data and autonomously determining sender/recipient aliases, distribution amounts, and other parameters, eliminating the need for manual database searches and reducing both computing overhead and financial costs associated with human operator intervention
Solution Approach 2:
The system performs preliminary action by pre-processing and storing structured information from historical text-based instructions in training datasets. The machine learning model is trained in advance on this structured data, enabling it to quickly and accurately parse new instructions without requiring real-time manual intervention or extensive database searches during actual resource distribution operations
2Productivity
If manual database searches and user input are used to generate resource distributions, then the system can process text-based instructions, but the likelihood of errors increases when handling high volumes of instructions
Solution Approach 1:
The machine learning model autonomously parses text-based instructions and extracts distribution elements with consistent accuracy, eliminating human error. The model self-corrects by learning from training data and applying learned patterns to new instructions, maintaining high reliability even when processing large volumes of instructions without manual intervention
Solution Approach 2:
The system uses feedback from training on historical text-based instructions to continuously improve parsing accuracy. The machine learning model learns from correct and incorrect examples in the training dataset, adjusting its parameters to minimize errors. This feedback mechanism ensures high reliability when processing production instructions, as the model has been optimized through iterative learning on diverse examples
3Loss of energy
If machine learning is used to automatically generate resource distributions from text-based instructions, then computing resources are conserved by reducing manual input, but the system complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary layer between text-based instructions and the resource distribution system. Instead of directly parsing unstructured text or requiring manual input, the model transforms natural language instructions into structured distribution elements that the system can process efficiently. This intermediary approach reduces overall system complexity by centralizing the parsing logic in a trained model rather than requiring complex rule-based systems or manual intervention across multiple components
4Productivity
If machine learning models are used to parse text-based instructions, then the speed and accuracy of processing increases, but the initial setup and training requirements increase
Solution Approach 1:
The system performs preliminary action by collecting and structuring historical text-based instructions into training datasets before deploying the machine learning model. This upfront preparation work, including data cleaning, annotation, and organization, is completed in advance so that the model can be trained once and then rapidly process production instructions without requiring repeated setup or training, amortizing the initial time investment over many subsequent operations
Data Source
AI summary
Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent. The present invention may be configured to determine, based on user data in a user information data structure, a user associated with the sender alias, determine, based on user data associated with the user in the user information data structure and based on predicted distribution elements from a machine learning model, actual distribution elements, and generate, based on the actual distribution elements, a resource distribution.