Digital Assistant OS Upgrade Peripheral Reliability
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Solution Overview
Problem
Users are reluctant to adopt operating system (OS) upgrades due to concerns about peripheral device failures, which are often caused by outdated or incompatible drivers, leading to inaccurate notifications and increased support costs for both users and suppliers.
Innovation Solution
A digital assistant is configured to interact with the OS upgrade system, using machine learning and real-world data to predict the success of peripheral device operations post-upgrade, providing personalized and context-aware notifications and recommendations, and proactively addressing potential issues by locating mitigations or workarounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OS upgrade notifications are provided to users, then users can be informed about available upgrades, but users may still be reluctant to upgrade due to concerns about peripheral device failures
Solution Approach 1:
The system implements feedback by collecting real-world data from crowd-sourced devices about peripheral device operation after OS upgrades. This feedback loop enables the confidence model to learn from actual outcomes and improve prediction accuracy over time, providing users with reliable information about upgrade compatibility based on empirical evidence rather than theoretical assumptions.
Solution Approach 2:
The patent replaces traditional mechanical testing methods (physical testing of each device combination) with a machine learning-based confidence model. This substitution uses algorithms to predict peripheral device compatibility by analyzing patterns in crowd-sourced data, eliminating the need for extensive manual testing while providing accurate compatibility information to users.
2Measurement precision
If traditional notification systems are used without machine learning predictions, then the system is simpler to implement, but notification accuracy and user trust are reduced
Solution Approach 1:
The confidence model serves multiple functions: it predicts peripheral device compatibility, generates confidence scores for notifications, and learns from crowd-sourced data over time. This multi-functional approach consolidates what would otherwise require separate testing, prediction, and update systems into a single machine learning model, improving measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The system implements self-service through automated data collection from crowd-sourced devices and automatic model training. The confidence model continuously improves itself by learning from real-world outcomes without requiring manual intervention for each new device combination, reducing the operational complexity while maintaining high prediction accuracy.
3Reliability
If comprehensive testing of all device combinations is performed, then compatibility information is more accurate, but time and resources required increase significantly
Solution Approach 1:
The system performs preliminary action by collecting compatibility data from crowd-sourced devices in the background before users need upgrade information. The confidence model is pre-trained on accumulated data and can provide instant predictions without requiring time-consuming testing at the point of upgrade decision, significantly reducing the time loss while maintaining reliable compatibility information.
Solution Approach 2:
The patent changes the parameter of data collection from controlled laboratory testing to real-world crowd-sourced data. This parameter change allows the system to gather compatibility information from diverse actual usage scenarios simultaneously, achieving comprehensive coverage without the time constraints of sequential testing, thereby improving reliability without increasing time loss.
Data Source
AI summary
A digital assistant supported across computing devices is configured to interact with an operating system (OS) upgrade system so that various user experiences, services, content, or features associated with support for peripheral devices during an OS upgrade of a computing device can be provided by the digital assistant and rendered as a native digital assistant user experience. The digital assistant is configured to surface a notification through a user interface (UI) when an OS upgrade is available for a user's computing device and recommended for installation. The OS upgrade system executes a confidence model in a machine learning system using real world crowd-sourced data to make predictions of successful post-upgrade operations of peripheral devices with an associated level of confidence. The digital assistant personalizes the OS upgrade notification to the user based on the configuration of computing and peripheral devices, applicable context, and the confidence level.