Workspace Reservation Recommendations from Learned User Preferences
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Solution Overview
Problem
Conventional workspace reservation systems lack the ability to learn user preferences and provide recommendations based on relationships with other people and workspace types, leading to inefficient and time-consuming manual selection processes.
Innovation Solution
A system that learns personnel and workspace preferences from real-time communications and reservation data to recommend optimal workspaces based on scores determined by user relationships and workspace characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual workspace selection is used, then system complexity is low, but workspace selection time and effort increase
Solution Approach 1:
The system automatically learns user preferences from communication records and reservation data, then autonomously recommends optimal workspaces without requiring manual input from users. The system serves itself by extracting preferences from data and generating recommendations automatically.
Solution Approach 2:
The system uses feedback from communication records and reservation data to continuously learn and update user preferences. This feedback loop enables the system to improve its recommendations over time by adapting to changing user behavior patterns.
2Adaptability or versatility
If conventional workspace reservation systems are used, then implementation is simple, but they cannot learn user preferences or provide personalized recommendations
Solution Approach 1:
The system automatically extracts user preferences from communication records and reservation data without requiring explicit user input or configuration. It self-adapts to user behavior patterns and generates personalized recommendations autonomously.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated data extraction from communication records. Instead of requiring users to manually specify preferences, the system uses computational analysis of communication patterns to infer preferences automatically.
3Productivity
If users manually review workspace information, then they have full control over selection, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of communication records and reservation data to pre-determine user preferences and generate workspace recommendations before the user needs to make a selection. This advance preparation eliminates the need for manual review of multiple workspaces.
Solution Approach 2:
The system continuously learns from user behavior feedback to improve recommendation accuracy. By analyzing how users interact with workspaces and communicate with colleagues, the system refines its preference models and provides increasingly accurate recommendations.
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.