Workspace Reservation Recommendations From Learned User Preferences
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Solution Overview
Problem
Conventional workspace reservation systems rely on manual user selection without learning user preferences, making it challenging and time-consuming to find optimal workspaces based on relationships with other people or workspace types, and fail to update preferences dynamically.
Innovation Solution
Implement a system that learns personnel and workspace preferences using machine learning models based on real-time communication records to recommend the most suitable workspace for a user, incorporating graphical user interfaces for workspace selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user selection is used for workspace reservation, then system complexity is low, but workspace selection efficiency and user experience deteriorate
Solution Approach 1:
The system automatically learns user preferences from communication records and makes workspace recommendations without requiring manual input from users. The machine learning model processes communication data to infer preferences and generates recommendations autonomously, reducing the burden on users while improving selection efficiency.
Solution Approach 2:
The patent replaces manual mechanical selection processes with an automated machine learning system. Instead of users manually browsing and selecting workspaces, the system uses AI algorithms to analyze communication records and automatically determine optimal workspace recommendations, substituting human effort with intelligent automation.
2Adaptability or versatility
If manual workspace selection without learning is used, then data processing requirements are low, but the ability to personalize and adapt to user needs deteriorates
Solution Approach 1:
The system continuously learns from user behavior patterns and communication records to refine and update preference models. This feedback loop allows the machine learning model to adapt to changing user needs over time, improving personalization accuracy while managing data processing through intelligent filtering and prioritization of relevant communication data.
3Loss of time
If conventional workspace reservation systems are used, then implementation is simple, but time consumption for finding optimal workspaces increases
Solution Approach 1:
The system performs preliminary learning of user preferences by analyzing communication records before the actual workspace selection occurs. By pre-processing and storing preference models, the system can quickly generate recommendations during workspace selection without requiring users to manually evaluate options, significantly reducing time consumption.
Data Source
AI summary
A recommended workspace at a premises is determined for a user of a software platform requesting a workspace reservation based on learned preferences specific to the user. The workspace reservation request is initiated via an interaction by the user with one or more graphical user interfaces displaying information associated with workspaces at the premises. Scores for each of multiple candidate workspaces identified for the user from amongst available ones of the workspaces are determined based on weights defined according to learned personnel preferences of the user and learned workspace preferences of the user. A recommended workspace is determined for the user as a candidate workspace of the multiple candidate workspaces corresponding to a highest one of the scores. An indication of the recommended workspace is then output for display within the one or more graphical user interfaces in response to the workspace reservation request.