AI Personalization for Meeting Invitee and Content Selection
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Solution Overview
Problem
Conventional meeting organization technologies fail to adequately assist meeting organizers in identifying relevant invitees and computer-readable content for electronic invitations, leading to extensive manual input, especially in scenarios with a large number of attendees or when content is not recently or frequently accessed.
Innovation Solution
An AI personalization application utilizes user and tenancy graphs to identify potential invitees and relevant content by traversing nodes and edges based on keywords, ranking them for inclusion in electronic invitations, and reducing the need for manual input through automated suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional technologies present lists of recently or frequently contacted individuals for invitee selection, then the ease of operation is improved for common scenarios, but the adaptability deteriorates when individuals exist that should be invited but do not meet the criteria for being included in either list
Solution Approach 1:
The system changes the parameters of invitee identification from static criteria (recent contact, frequent contact) to dynamic AI-driven analysis that considers meeting topic, attendee roles, organizational hierarchy, and contextual relevance. This allows the system to adapt to diverse meeting scenarios while maintaining ease of operation through automated suggestions.
Solution Approach 2:
The AI personalization application performs self-service by automatically analyzing meeting requirements, searching for relevant invitees across the organization, and presenting ranked suggestions to the organizer. This eliminates the need for manual searching through contact lists while adapting to specific meeting contexts.
2Measurement precision
If extensive manual input is required to identify invitees and content, then the measurement precision of invitee and content selection is improved, but the productivity deteriorates due to the time-consuming nature of the process
Solution Approach 1:
The system replaces the mechanical process of manual invitee and content identification with an AI-driven automated system that analyzes meeting topics, organizational data, and content repositories. This substitution maintains high accuracy through intelligent analysis while dramatically improving productivity by eliminating manual searching and selection processes.
Solution Approach 2:
The AI personalization application performs preliminary action by proactively identifying and ranking potential invitees and relevant content before the organizer needs to make selections. The system pre-analyzes meeting requirements and prepares personalized suggestions, allowing the organizer to quickly review and confirm rather than search from scratch.
3Ease of operation
If conventional technologies present content based on recent or frequent access, then the ease of operation is improved for simple cases, but the adaptability deteriorates when content exists that is relevant to the meeting but does not meet the criteria for being included in either list
Solution Approach 1:
The system changes the parameters of content identification from simple access patterns (recent, frequent) to sophisticated AI-driven relevance analysis that considers meeting topic, content type, attendee roles, and contextual factors. This enables the system to adapt to diverse content requirements while maintaining ease of operation through automated recommendations.
4Adaptability or versatility
If the system supports a relatively large number of attendees, then the versatility of the meeting application is improved, but the device complexity increases due to the computational burden of identifying and managing numerous invitees
Solution Approach 1:
The system replaces complex manual invitee management processes with an AI-driven automated system that efficiently handles large numbers of attendees. The AI personalization application analyzes organizational data, meeting requirements, and attendee relationships to automatically generate and manage invitee lists, reducing the computational and operational complexity despite supporting large meeting capacities.
Data Source
AI summary
A computing system obtains a keyword and an identifier for a user, where a meeting is to be scheduled between the user and at least one other individual. Based upon the keyword and the identifier for the user, the computing system obtains an identifier for an invitee to the meeting and/or an identifier for content that is to be included in an electronic invitation for the meeting via a computer-implemented user graph for the user. The computing system causes the identifier for the invitee and/or the content to be included in the electronic invitation. The electronic invitation for the meeting is transmitted to electronic accounts of invitees that are identified in the electronic invitation. Data is generated during the meeting by a meeting application that hosts the meeting, and the user graph is modified based upon the data. Subsequently, the computing system obtains a task via the modified user graph.