Dynamic Peer Group Benchmarking With Hierarchical Time Series Analysis
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Solution Overview
Problem
Existing machine learning systems struggle to efficiently generate accurate benchmark metrics for dynamic peer groups in near-real time, often requiring complex models that increase computational costs without significant accuracy improvements.
Innovation Solution
A system that performs structured analysis on time series data to generate benchmark metrics, using hierarchical peer groups and feedback loops to improve accuracy, while reducing computational complexity and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used to generate accurate benchmark metrics for dynamic peer groups, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent segments the peer group benchmarking process into distinct phases: historical data analysis phase (offline) and real-time benchmark generation phase (online). The complex machine learning model is trained offline on historical data to learn peer group relationships, while the online phase uses the pre-trained model with minimal computation. This segmentation allows high accuracy without requiring complex real-time computation.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing peer group assignments and historical performance data before real-time benchmarking is needed. The machine learning model is trained in advance on historical data, and peer groups are pre-established based on device characteristics. When real-time benchmarks are requested, the system simply retrieves and displays pre-computed results with minimal additional processing.
2Measurement precision
If complex machine learning models are used to generate accurate benchmark metrics, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent divides the computationally intensive model training and data processing into offline batch operations and minimal online operations. The energy-consuming activities (model training, peer group formation, historical analysis) are performed offline when energy availability is not constrained. The online benchmark generation requires minimal energy as it only needs to query pre-computed results and generate visualizations.
Solution Approach 2:
All energy-intensive computations are performed in advance: the machine learning model is trained offline, peer groups are pre-established, and historical performance metrics are pre-aggregated. This preliminary computation stores results in databases for rapid retrieval, eliminating the need for repeated energy-consuming calculations during real-time benchmarking operations.
3Productivity
If real-time benchmark data is generated for dynamic peer groups, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic peer groups that automatically adjust based on device characteristics and performance patterns. The system continuously monitors device metrics (traffic volume, application type, category) and dynamically reassigns devices to appropriate peer groups as they evolve. This dynamic adaptation enables real-time benchmarking without requiring manual intervention or complex static configuration.
Solution Approach 2:
The system incorporates feedback loops where benchmark results and device performance data are continuously collected and used to refine peer group assignments. The machine learning model learns from historical benchmark data and device characteristics, automatically improving peer group formation algorithms. This feedback mechanism enables the system to adapt to changing conditions while maintaining streamlined operations through automated learning rather than complex manual management.
Data Source
AI summary
Systems and methods include receiving a request for presentation of a benchmark line chart diagram associated with a device identifier. The system can access device identifier data including category data, application data, or traffic volume data. The system can determine a branch of related hierarchical groups for the device identifier based on the device identifier data. The system can access data including cohort groups including a minimum number of device identifiers such that aggregate metric data associated with the cohort does not reveal any information about any single device identifier. The system can select a benchmark group for the device identifier. The system can access data including aggregate metrics associated with the selected benchmark group. The system can transmit data including instructions cause one or more processors to provide for display a benchmark line chart diagram and benchmark metric data indicative of aggregate metrics associated with the benchmark group.