18149348. SYSTEMS AND METHODS FOR TRAINING AND DEPLOYING A NEURAL NETWORK simplified abstract (Truist Bank)
Contents
SYSTEMS AND METHODS FOR TRAINING AND DEPLOYING A NEURAL NETWORK
Organization Name
Inventor(s)
Barath Jayaraman of Fort Mill SC (US)
SYSTEMS AND METHODS FOR TRAINING AND DEPLOYING A NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18149348 titled 'SYSTEMS AND METHODS FOR TRAINING AND DEPLOYING A NEURAL NETWORK
Simplified Explanation: The patent application describes systems and methods for training machine learning models of a neural network to process unstructured data related to resource record utilization. The neural network is then deployed to process this data, providing recommendations for improving resource utilization based on predicted outcomes.
- Neural network trained to process unstructured data related to resource record utilization
- Recommends user actions based on predicted resource utilization improvements
- User interface on a user device provides recommended actions for resource optimization
Key Features and Innovation:
- Training machine learning models of a neural network to process unstructured data
- Predicting resource utilization improvements based on user data
- Providing recommendations for optimizing resource utilization
Potential Applications:
- Resource management in various industries
- Personalized recommendations for users based on data analysis
- Optimization of resource allocation in businesses
Problems Solved:
- Lack of efficient resource utilization strategies
- Difficulty in analyzing unstructured user data
- Ineffective decision-making based on resource records
Benefits:
- Improved resource utilization efficiency
- Personalized recommendations for users
- Enhanced decision-making based on predicted outcomes
Commercial Applications: Optimizing resource allocation in businesses for improved efficiency and decision-making processes.
Prior Art: Research on machine learning models for resource optimization and data analysis in various industries.
Frequently Updated Research: Ongoing studies on neural network applications in resource management and data analysis.
Questions about Neural Network Resource Optimization: 1. How does the neural network process unstructured data to predict resource utilization improvements? 2. What are the potential challenges in deploying the neural network for resource optimization tasks?
Question 1: How does the neural network process unstructured data to predict resource utilization improvements?
The neural network processes unstructured data by building layers to analyze patterns and relationships within the data. By training the model on historical resource record utilization data, it can predict future outcomes and recommend actions for improving resource utilization efficiency.
Question 2: What are the potential challenges in deploying the neural network for resource optimization tasks?
Some potential challenges in deploying the neural network for resource optimization tasks include data privacy concerns, the need for continuous training to adapt to changing data patterns, and ensuring the accuracy and reliability of the predictions made by the model. Additionally, integrating the recommendations provided by the neural network into existing systems and workflows may require careful planning and implementation.
Original Abstract Submitted
Systems and methods train machine learning model(s) of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization, and the neural network is deployed to process the unstructured data. Unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record is received, and the neural network is applied to the received unstructured user data, the applying including determining that at least a portion of the user resource should be transmitted from the first user record to the second user record based on predicted resource utilization improvement(s). A recommended user action is provided, via a user interface of a user device, the recommended user action including an indication of the resource utilization improvement(s).
