Event Theme Consensus via Digital Wardrobe Analysis
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Solution Overview
Problem
Determining an appropriate event theme that is well-received by attendees can be challenging, often relying on guesswork and requiring paid event coordinators, as existing methods do not effectively utilize participant preferences to ensure theme alignment with the audience's interests.
Innovation Solution
A method and system that generate an event theme by analyzing digital wardrobes of participants, identifying consensus preferences through machine learning and real-time communications, to create a theme that aligns with the majority's preferences, using structured and unstructured data from various computing nodes and IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional event coordination methods are used with paid event coordinators, then theme determination can be handled professionally, but cost increases and the process becomes less efficient
Solution Approach 1:
The system enables automatic theme determination through self-service mechanisms where the computer system autonomously analyzes participant preferences, generates theme options, and determines consensus without requiring human event coordinators. The digital wardrobe analysis and automated consensus determination replace manual professional coordination services.
Solution Approach 2:
The patent replaces the mechanical human coordination process with an automated computer-based system that uses machine learning algorithms, data analysis, and automated communication to determine event themes. The system substitutes human judgment and coordination efforts with computational analysis of digital wardrobe data and preference aggregation.
2Device complexity
If manual guesswork is used to determine event themes, then no complex analysis systems are needed, but theme alignment with attendee preferences deteriorates
Solution Approach 1:
The system performs preliminary analysis of participant preferences by analyzing digital wardrobes and preference data before the event theme is finalized. This advance analysis allows the system to generate informed theme recommendations based on pre-collected data about participant interests and styles.
Solution Approach 2:
The system implements feedback mechanisms where participants can review generated theme options and provide input. The consensus determination process incorporates participant responses and adjusts theme selections based on aggregated feedback, ensuring the final theme aligns with group preferences.
3Productivity
If automated consensus determination is implemented, then coordination efficiency improves, but data processing complexity increases
Solution Approach 1:
The system segments the complex theme determination process into distinct modules: digital wardrobe analysis, preference extraction, theme generation, consensus determination, and communication. Each module handles a specific aspect of the process, making the overall complex task manageable and automatable through specialized sub-functions.
Solution Approach 2:
The system uses an intermediary computational layer that processes raw digital wardrobe data and transforms it into meaningful preference insights. This intermediary processing layer handles the complexity of data analysis while presenting simplified results to users, mediating between raw data and actionable theme recommendations.
Data Source
AI summary
A method, computer program product, and a system where a processor(s) generates a digital wardrobe for each user of a set of users. The processor(s) obtains a prospective theme(s) for a given event and a list of participants comprising a portion of the set of users. The processor(s) identify preferences, in the digital wardrobes of the portion of the set of users, relevant to each of the one or more prospective themes for the given event. The processor(s) determine, based on analyzing the relevant preferences in the digital wardrobes of the portion of the set of users, if a consensus exists in the relevant preferences of the portion of the set of users, where the consensus represents a given prospective theme, where the respective relevant preferences for the given prospective theme are aligned across the portion of the set of users. Based on determining that the consensus exists, the processor(s) generates an event theme for the given event, wherein the event theme is the consensus.