Machine Learning Resource Allocation via Event Vector Segmentation
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Solution Overview
Problem
Machine learning algorithms face difficulties in extracting meaningful insights from large historical data sets, especially from transactional websites primarily structured for human interaction, due to unstructured data and authentication challenges.
Innovation Solution
A resource management system utilizing a data ingest server, vector processing server, and prediction processing server to generate event vectors and predict future resource allocations by comparing current event vectors with historical data, creating a resource allocation curve showing past and future resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are applied to large historical data sets from transactional websites, then resource allocation predictions can be improved, but the unstructured nature of the data and authentication challenges make it difficult to gather meaningful insights
Solution Approach 1:
The patent segments the unstructured data into structured event vectors with defined schemas. Each event vector captures specific transactional events in a standardized format, transforming raw unstructured data into machine-learning-friendly structured representations that can be efficiently processed while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary layer that handles authentication and data extraction from protected sources. This intermediary component retrieves historical data from authenticated sources and transforms it into accessible event vectors, solving both the authentication challenge and the unstructured data problem simultaneously.
2Reliability
If authentication protection is applied to historical data sets, then data security is improved, but the data becomes less accessible to machine learning algorithms
Solution Approach 1:
The patent introduces an intermediary layer that handles authentication and data extraction from protected sources. This intermediary component retrieves historical data from authenticated sources and transforms it into accessible event vectors, solving both the authentication challenge and the unstructured data problem simultaneously.
3Loss of information
If event vectors are mined from large data sets and processed through machine learning, then resource allocation insights are improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring historical data into event vectors before machine learning analysis. By organizing data into standardized schemas in advance, the system reduces the computational burden during prediction phases, thereby decreasing processing time while maintaining comprehensive insight extraction.
Data Source
AI summary
A method and system for allocation of resources is disclosed. A data source is mined to determine event vectors from a large number of cases that follow a branched processing model. Current event vectors are compared to the mined event vectors with machine learning to predict future nodes for the current event vectors. Historical resource allocations for the mined event vectors are used to determine resource allocation for the current event vector over time. Current event vectors are combined to produce a resource allocation curve showing past and future resources allocated.