Parking Location Recommendation Based on Store Visit Intent
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Solution Overview
Problem
Existing technologies for delivering commodities at a store do not effectively consider a user's visit or non-visit to the store when determining a convenient parking location.
Innovation Solution
An information processing method that acquires entry information including a user's ID, entry time, and planned receipt time, and determines a parking location based on the difference between the entry time and the planned receipt time, using parking information stored in memory to identify a location within a predetermined distance from the store.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed parking location is assigned for commodity delivery, then the delivery process is simplified, but user convenience is reduced when users plan to visit the store
Solution Approach 1:
The parking location recommendation system dynamically adjusts recommendations based on user behavior patterns and store visit history, transforming static parking assignments into adaptive, context-aware suggestions that optimize for both delivery efficiency and user convenience
Solution Approach 2:
The system incorporates feedback loops that analyze user entry/exit patterns, commodity pickup behavior, and store visit frequency to continuously refine parking location recommendations, ensuring the system adapts to individual user preferences and changing conditions
2Device complexity
If parking location is determined without considering store visit intent, then the determination process is simplified, but user convenience is reduced
Solution Approach 1:
The system performs preliminary analysis of user entry patterns, store visit history, and commodity pickup behavior before making parking location recommendations, preparing user profiles and preference data in advance to enable accurate, context-aware suggestions without adding complexity to the actual determination process
Solution Approach 2:
The system automatically analyzes user behavior data and generates personalized parking recommendations without requiring manual input from users, with the system self-adjusting based on observed patterns in store visits, commodity pickups, and parking preferences
3Measurement precision
If the system monitors user entry and behavior patterns, then parking location accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features from user behavior data (entry patterns, visit frequency, commodity pickup timing) while filtering out unnecessary information, focusing processing on key indicators that most strongly correlate with parking location preferences to maintain accuracy while reducing complexity
Data Source
AI summary
An information processing device performs: acquiring entry information indicating an entry of a user into an administrative region of a store and including a user ID, an entry time when the user entered, and a planned receipt time associated with the user ID for receiving a commodity; determining, when a difference between the entry time and the planned receipt time is larger than a given number, location information about a parking location which is a distance equal to or shorter than a predetermined distance away from the store as parking location information on the basis of parking information which is held in a memory and includes location information about parking locations in a parking place in the administrative region; and outputting the parking location information.