Weighted Application Pairing for Cross-Platform Installation
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Solution Overview
Problem
Users face challenges in discovering alternative application installations due to the proliferation of connected devices and architectures, with existing solutions failing to consider user preferences or previous installations.
Innovation Solution
A platform-independent system using a server with a data structure that associates application pairings with weightings, allowing client devices to select target installations based on random number generation within weighted ranges, considering user characteristics and key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a platform-independent scheme is implemented to offer installations across multiple devices and architectures, then the adaptability and versatility of the installation offering system is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary between users and installation offerings. The server hosts the data structure containing application pairings and weightings, and manages the target selection algorithm. This intermediary approach allows platform-independent installation offerings without requiring complex logic in each client device, as the server handles the cross-platform compatibility and selection calculations.
Solution Approach 2:
The patent creates a universal installation offering system that works across multiple platforms and device architectures. The data structure and target selection algorithm are designed to be platform-agnostic, allowing the same system to serve various devices (mobile phones, tablets, computers) with different operating systems and architectures through a common interface and methodology.
2Ease of operation
If user preferences and previous installations are considered in installation offerings, then the relevance and user satisfaction is improved, but the data processing and algorithm complexity increase
Solution Approach 1:
The patent uses weightings as parameters to represent user preferences and installation relevance. Instead of complex rule-based systems, the patent transforms qualitative preferences into quantitative weightings that can be processed by the target selection algorithm. These weightings are adjusted based on user characteristics, previous installations, and key performance indicators, allowing dynamic adaptation without increasing algorithmic complexity significantly.
Solution Approach 2:
The patent implements feedback mechanisms through key performance indicators that track user interactions with installation offerings. This feedback is used to adjust weightings and improve future recommendations. The system learns from user behavior patterns and refines its offerings over time, making the system smarter without requiring users to manually configure complex preferences.
3Reliability
If random selection with weighted ranges is used to notify target installations, then the fairness and objectivity of installation recommendations is improved, but the computational overhead increases
Solution Approach 1:
The patent uses random number generation within weighted ranges to select target installations. This approach provides sufficient objectivity and fairness for recommendation purposes without requiring exhaustive analysis or overly complex selection processes. The random selection with weighting strikes a balance between thoroughness and efficiency, providing reliable recommendations with acceptable computational overhead.
Data Source
AI summary
A system comprising a server in communication with a user device configured to run a plurality of applications. A data structure is held in the system, said data structure comprising multiple entries, wherein each entry associates an application pairing with a weighting, each application pairing comprising a source installation and a target installation. The user device running an application corresponding to one of the source installations selects one of the target installations to download from the server on the basis of the weightings, wherein each of the weightings is for the source installation is proportional to probability of the respective target installation being selected and installed.