Systems and methods of optimizing resource allocation using machine learning and predictive control
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Solution Overview
Problem
Computer systems face challenges in optimizing resource allocation due to overwhelming data processing and storage demands, with existing technologies failing to efficiently identify data sets of significant interest to end users and adapt to rapidly changing environments.
Innovation Solution
A computer system utilizing predictive and control machine learning models to prioritize and allocate resources based on selection scores, monitor performance, and adjust models to improve accuracy and responsiveness to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer systems process all possible data sets for every data category, then completeness of data processing is improved, but resource consumption (processing capacity, memory storage, communications bandwidth) worsens
Solution Approach 1:
The patent extracts and processes only the most relevant data sets from the complete data universe by using predictive machine learning models to identify data sets with high probability of user interest. This selective extraction approach maintains processing completeness for important data while eliminating waste on low-value data sets.
Solution Approach 2:
The system changes the parameter of data selection from exhaustive (all possible data sets) to selective (high-probability data sets) by introducing predictive scoring mechanisms. This parameter change transforms the processing approach from brute-force completeness to intelligent completeness, reducing resource consumption while maintaining reliability for important data.
2Productivity
If computer systems process a smaller subset of data sets to reduce resource consumption, then resource efficiency is improved, but accuracy in identifying user-interested data sets worsens
Solution Approach 1:
The patent implements feedback loops where the system monitors actual user interactions with processed data sets and uses this information to retrain and improve the predictive machine learning models. This continuous feedback mechanism ensures that the system's ability to identify user-interested data sets improves over time, maintaining high accuracy even when processing only a subset of data.
Solution Approach 2:
The system performs preliminary actions by using predictive machine learning models to pre-identify and score data sets before full processing occurs. This preliminary filtering based on predicted user interest allows the system to focus resources on high-probability data sets while maintaining accurate identification of user-relevant information.
3Device complexity
If computer systems use traditional data processing methods, then system complexity is reduced, but adaptability to rapidly changing environments worsens
Solution Approach 1:
The patent introduces dynamic elements by implementing machine learning models that continuously learn and adapt to changing data patterns and user preferences. The system transitions from static processing rules to dynamic adaptive models that automatically adjust to environmental changes, improving versatility while managing complexity through modular model architecture.
Solution Approach 2:
The system enables self-service by allowing the machine learning models to automatically adapt and improve without manual reconfiguration. The predictive models autonomously learn from new data patterns and environmental changes, providing adaptability while keeping the control mechanism relatively simple through automated learning rather than complex manual tuning.
4Ease of operation
If computer systems allocate resources to process all data sets equally, then fairness in resource allocation is improved, but overall system performance worsens
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on the specific characteristics and predicted value of each data set. Instead of uniform allocation, the system assigns processing resources proportionally to predicted user interest, ensuring that high-value data sets receive adequate attention while low-value data sets consume minimal resources, thereby improving overall performance.
Solution Approach 2:
The system changes the allocation parameter from equal distribution to differentiated distribution based on predictive scoring. This parameter transformation allows the system to maintain fairness in the sense that each data set receives allocation proportional to its expected value, rather than uniform allocation that treats all data sets identically regardless of their potential importance.
Data Source
AI summary
A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period. Predictive machine learning model(s) and decision-making machine learning model(s) are adjusted based on the performance metrics to improve respective performance(s).