Feature Deployment Optimization via Usage Pattern Analysis
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Solution Overview
Problem
Current application management services face inefficiencies in deploying feature updates based on usage patterns, leading to unnecessary resource consumption due to inadequate demand inference schemes.
Innovation Solution
A hosted service optimizes feature deployment by identifying a target audience through processing usage pattern signals and organizational rules, allowing for precise deployment of feature updates to client devices that match specific usage metrics and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional application management services deploy feature updates without usage pattern analysis, then feature updates can be deployed broadly, but personnel resources are unnecessarily consumed and demand prediction becomes inefficient
Solution Approach 1:
The system enables self-service by automatically analyzing usage patterns and identifying target audiences for feature updates without requiring manual personnel intervention. The hosted service autonomously processes usage data, applies organizational rules, and determines deployment targets, allowing the system to serve itself in the demand prediction process.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. Instead of personnel manually analyzing usage patterns and predicting demand, the system uses automated processing of usage pattern signals against organizational rules to identify target audiences, substituting human mechanical work with algorithmic processing.
2Measurement precision
If comprehensive usage pattern analysis is performed to identify target audience, then deployment precision is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing usage pattern data in an organized manner before deployment decisions are needed. Usage patterns are continuously collected and structured in advance, so when a feature update requires target audience identification, the analysis can proceed quickly using pre-organized data rather than starting from raw data.
Solution Approach 2:
The patent applies segmentation by dividing the usage pattern analysis into distinct components: collecting usage signals, processing them against organizational rules, and generating target audience identifiers. This segmented approach allows each component to be optimized independently and processed efficiently in a pipeline manner, reducing overall processing time while maintaining precision.
3Productivity
If usage pattern signals are processed based on organizational rules, then resource allocation is optimized, but system complexity increases
Solution Approach 1:
The hosted service implements universality by serving multiple functions within a single system: it collects usage pattern data, processes the data against organizational rules, identifies target audiences, and manages feature update deployments. This multi-functional approach consolidates what could be separate complex systems into one unified service, managing complexity through integration rather than proliferation of components.
Data Source
AI summary
Variety of approaches to optimize a feature deployment based on an usage pattern are described. A hosted service initiates operations to optimize the feature deployment upon detecting a feature update associated with an application. Next, a target audience for the feature update is identified by processing an usage pattern signal of the application based on information update and an organizational rule associated with the feature update. The feature update is deployed to the target audience.