Snap Assist Recommendation Model for Workspace Setup
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Solution Overview
Problem
Existing snap assist solutions in software platforms for personal computing devices only present items that are currently open, leading to irrelevant recommendations and increased user effort in finding and opening desired items, resulting in a degraded user experience and reduced productivity.
Innovation Solution
A machine learning model analyzes various factors, including user behavior and item relationships, to provide intelligent snap assist recommendations, recommending items that are not currently open but relevant to the user's task, and adapting over time to individual user habits and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing snap assist solutions only present currently open items, then the system complexity is reduced, but the relevance of recommendations to user tasks decreases and user productivity is reduced
Solution Approach 1:
The system performs preliminary actions by proactively recommending items that the user will likely want to snap next, based on analysis of user behavior patterns, item relationships, and current task context. This allows the system to anticipate user needs before the user manually searches for items, thereby improving recommendation relevance without requiring complex real-time processing during the snap operation itself.
Solution Approach 2:
The patent replaces manual mechanical searching and selection processes with an intelligent recommendation system that uses machine learning models to automatically analyze user behavior, item relationships, and task context. This substitution of manual operations with automated intelligent analysis improves the relevance of snap recommendations while managing system complexity through efficient data processing.
2Ease of operation
If existing snap assist solutions present all available items without organization, then complete item coverage is provided, but user effort to find relevant items increases and user experience degrades
Solution Approach 1:
The system applies local quality by providing different levels of recommendation specificity based on local context - analyzing user behavior patterns, item relationships, and current task requirements to tailor recommendations to the specific local situation rather than applying a uniform approach. This allows the system to prioritize and organize items dynamically, making relevant items easier to find while reducing the time users spend searching.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with snap recommendations, analyzing which items users select and which they ignore, and using this feedback to refine future recommendations. This feedback loop improves the ease of finding relevant items over time while reducing the time users spend searching, as the system learns from user behavior patterns.
3Productivity
If users must manually find and open desired items not currently open, then system resources are conserved, but user productivity decreases and user experience is degraded
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns, item relationships, and task context to proactively recommend items users will likely want to snap. This preliminary action enables users to quickly access relevant items without manual searching, thereby improving productivity. The system manages computing resource usage by performing this analysis efficiently and only processing data necessary for generating relevant recommendations.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on user behavior patterns, item relationships, and current task context. By changing parameters such as recommendation priority, item selection criteria, and presentation format based on analyzed data, the system improves productivity by providing context-aware recommendations while managing computing resources through selective and efficient data processing.
Data Source
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AI summary
The techniques disclosed herein enable systems to provide intelligent snap assist recommendations using a diverse set of factors and factor weights. To generate recommendations, a system receives a user input placing a first item in a region of a snapped configuration in a display environment. In response, the system assigns a confidence score for a plurality of items including items open in the display environment as well as items that are not open. The system then ranks the items based on confidence score and selects a list of recommended items from the ranked list. The recommended items are then presented in a second region of the snapped configuration for selection. The system is further configured to receive and analyze user selections of snapped items to learn over time and adjust confidence scoring.