Conditional Software Update Recommendations Based on Local System State
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Solution Overview
Problem
Conventional software update recommendation systems struggle to tailor recommendations to individual clients due to their unique attributes, leading to uninformed risks for clients and difficulties in creating unconditional update recommendations that fit all deployed clients.
Innovation Solution
A conditional recommendation system that processes logic to obtain the current software version and available updates for a client, along with associated risk conditions based on client attributes. The system sends these updates and conditions to the client, allowing it to determine the best course of action based on its own attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software update recommendation systems provide unconditional update recommendations to all clients, then the system can maintain simplicity and broad applicability, but clients face uninformed risks due to lack of customization to individual client attributes
Solution Approach 1:
The patent segments the update recommendation process into two distinct parts: (1) the recommendation service provides conditional update recommendations with associated conditions, and (2) the client locally evaluates whether those conditions are satisfied. This segmentation allows the system to be adaptive to individual clients without requiring the recommendation service itself to be complex or have access to detailed client attributes.
Solution Approach 2:
The client performs self-service by locally evaluating the conditions associated with update recommendations against its own attributes. This eliminates the need for the recommendation service to know or process detailed client-specific information, maintaining system simplicity while enabling personalized recommendations. The client independently determines whether to apply the update based on its own state.
2Adaptability or versatility
If the recommendation service accesses detailed client attributes to provide personalized recommendations, then update recommendations can be tailored to individual clients, but sensitive client information must be shared with the remote service
Solution Approach 1:
The patent extracts the condition evaluation logic from the recommendation service and places it locally on the client. The recommendation service only provides conditional recommendations without accessing detailed client attributes. The client extracts and evaluates only the necessary conditions locally, keeping sensitive attribute information private while still enabling personalized update decisions.
Solution Approach 2:
Conditions act as an intermediary between the recommendation service and client attributes. The recommendation service communicates conditions that represent update requirements, and the client evaluates these conditions against its attributes without exposing the attributes themselves. This intermediary mechanism enables personalization while preserving privacy.
3Reliability
If the system provides broad unconditional update recommendations, then it can be applied to all clients without customization, but clients cannot make informed decisions about update risks specific to their system state
Solution Approach 1:
The patent introduces dynamic condition evaluation where the client assesses update recommendations based on its current system state. Conditions are dynamically evaluated against client attributes such as software versions, hardware configurations, and system states. This enables reliable informed decision-making while maintaining operational simplicity through automated local evaluation.
Data Source
AI summary
Processing logic may obtain a version of software that is running on a client. Processing logic may obtain a second version of the software which is an update to the version of software that is running on the client. Processing logic may send the second version to the client with one or more conditions that are associated with the second version, for the client to determine whether or not to update to the second version.