Personalized Experience Journey System Using Dynamic Activity Routing
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Solution Overview
Problem
Current experiential commerce systems lack the ability to provide personalized and dynamic mobility services that incorporate real-time user preferences and activity status, failing to offer optimized experience journeys that align with user interests and journey parameters.
Innovation Solution
A system utilizing a processor and memory to generate and transmit personalized experience journeys by processing user preferences, activity statuses, and smart city data, including computer vision techniques for activity monitoring and preference learning, to recommend and route activities within a user's vicinity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experiential commerce systems provide personalized recommendations, then user preference accuracy improves, but data collection complexity increases
Solution Approach 1:
The patent combines multiple data collection methods (surveys, observation, transaction data, social media data) into a unified preference learning system. This merging allows the system to gather comprehensive user preference data through various channels simultaneously, improving measurement precision while managing complexity through integration rather than separate systems.
Solution Approach 2:
The system introduces preference learning algorithms as intermediaries that process raw data from multiple sources and transform it into actionable user preference profiles. These algorithms act as mediators between complex data collection mechanisms and the recommendation engine, simplifying the overall system architecture while maintaining high measurement accuracy.
2Adaptability or versatility
If the system inserts surprising elements into journeys, then experiential value increases, but journey reliability decreases
Solution Approach 1:
The recommendation system dynamically adjusts journey itineraries by incorporating surprising elements based on real-time user preferences and context. The system maintains reliability by using dynamic optimization algorithms that ensure surprising activities are still logistically feasible and aligned with user constraints, balancing adaptability with reliability through continuous adjustment rather than static planning.
Solution Approach 2:
The system changes journey parameters (such as activity selection, timing, and location) to introduce surprising elements while maintaining core journey objectives. By modifying specific parameters within acceptable ranges and constraints, the system enhances experiential value without fundamentally compromising journey reliability and feasibility.
3Measurement precision
If the system monitors real-time activity status, then recommendation accuracy improves, but processing load increases
Solution Approach 1:
The system implements periodic monitoring and updating of activity status rather than continuous real-time tracking. By refreshing recommendation data at optimized intervals and triggering updates based on significant changes rather than constant monitoring, the system maintains high recommendation accuracy while significantly reducing processing load and energy consumption.
Solution Approach 2:
The system performs preliminary processing and filtering of activity status data before full recommendation generation. By pre-processing incoming data streams, identifying relevant changes, and preparing candidate recommendations in advance, the system reduces the computational burden during actual recommendation delivery while maintaining accuracy.
Data Source
AI summary
A system, method and computer-readable medium for generating and transmitting experience journeys. The system includes a processor and a memory including instructions that, when executed by the processor, cause the processor to perform operations including: receiving a user's request for an experience journey; retrieving a listing of activities within a prescribed vicinity of the user; retrieving a listing of the user's preferences; eliminating activities from the listing of activities that are contrary to the preferences in the listing of user preferences, thereby forming a listing of preferred available activities; devising routes from the user's location to the activities in the listing of preferred available activities; generating experience journeys from the listing of preferred available activities and the devised routes to the activities in the listing of preferred available activities; and transmitting the generated experience journeys to the user.