ML User Clustering for Resource Prediction Drift
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Solution Overview
Problem
Enterprises face challenges in predicting proper resource levels and performance due to the complexity of various variables involved.
Innovation Solution
A system utilizing machine learning models that receive user data, transform it into user datasets, generate vectors, and input them into a machine learning model to predict actions for users, thereby clustering users based on predicted actions and executing corresponding action sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict resource levels and performance, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct components: data collection from multiple sources, data transformation into standardized datasets, vector generation for numerical representation, machine learning model processing, and action clustering. This segmentation allows each component to be optimized independently while maintaining overall prediction accuracy.
Solution Approach 2:
The patent introduces intermediate data structures (user datasets, vectors) that mediate between raw input data and machine learning model processing. These intermediaries standardize data formats and prepare features appropriately, reducing the complexity burden on the core prediction model while preserving prediction accuracy.
2Reliability
If multiple data sources are integrated for comprehensive user data, then prediction reliability is improved, but data processing complexity increases
Solution Approach 1:
The system merges data from multiple sources (user profile data, user activity data, and other relevant data) into a unified user dataset. This consolidation process integrates diverse data types while applying standardized transformation rules, thereby improving prediction reliability through comprehensive data coverage while managing processing complexity through systematic integration.
Solution Approach 2:
The patent creates a universal data transformation framework that handles multiple data types and sources through a common processing pipeline. The system applies consistent transformation rules across different data sources, enabling multi-functional data integration that improves reliability without proportionally increasing processing complexity.
3Productivity
If user data is transformed and vectorized for machine learning input, then model performance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary data transformation and vectorization operations before machine learning model processing. By pre-processing data into appropriate formats and generating vectors in advance, the system optimizes model input quality and performance while managing processing time through efficient pre-computation and batch processing strategies.
Data Source
AI summary
Methods and systems are disclosed herein for using machine learning models to predict proper resource levels and performance levels of those resources. One mechanism for predicting proper resource levels and performance levels of those resources may involve a use of a machine learning model that is enabled to receive user data as input and output a predication for one or more actions for each user. The users may be clustered based on the user action predicted by the machine learning model and one or more action sequences may be executed against each action cluster.