Unified Path Recommendation for Multi-Item Acquisition
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Solution Overview
Problem
Existing mobile device applications face challenges in providing users with unified strategies for obtaining multiple items within a geographical area, leading to resource wastage and overwhelming users with unconnected results due to the need for multiple inquiries and inadequate visualization of personal and circumstantial factors.
Innovation Solution
A system that determines user preference and urgency factors, analyzes path finding factors using machine learning, and communicates a unified path for obtaining multiple items, integrating data from scheduling, historical, and social media sources to provide coherent and resource-efficient guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple separate inquiries are made to obtain information about different items, then more comprehensive information can be gathered, but the user is overwhelmed with unconnected results and resource consumption increases
Solution Approach 1:
The patent combines multiple separate inquiries about different items into a single unified path recommendation. The system integrates information from multiple data sources (scheduling, historical, social media) and presents a cohesive strategy that connects all items to be obtained, rather than providing separate unconnected results for each item inquiry.
Solution Approach 2:
The system serves multiple functions simultaneously: it determines user preferences, analyzes urgency factors, processes multiple data sources, and generates a unified path recommendation. This multi-functional approach consolidates what would otherwise require multiple separate system operations into a single comprehensive service.
2Ease of operation
If detailed analysis of multiple factors is performed to provide personalized paths, then user experience improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of user preferences, urgency factors, and path finding factors before generating the final recommendation. By pre-processing and analyzing these factors in advance, the system can efficiently generate personalized path recommendations without requiring extensive real-time computation, thus improving user experience while controlling processing time.
3Reliability
If comprehensive data from multiple sources is integrated, then the quality of path recommendation improves, but data processing complexity and resource consumption increase
Solution Approach 1:
The patent merges data from multiple sources (scheduling information, historical data, social media content) into a single integrated analysis. By combining these data sources and processing them together through a unified machine learning model, the system achieves high-quality recommendations while reducing the total computational energy required compared to processing each data source separately.
Data Source
AI summary
A device may determine one or more preference factors associated with a user moving within a geographical area to obtain a first item and a second item. The device may determine one or more urgency factors associated with obtaining the first item and the second item. The device may select first candidate locations for the first item, and second candidate locations for the second item. The device may analyze path finding factors that include a predicted location of the user at a target date and time set for obtaining the first item and the second item, the one or more urgency factors, the one or more preference factors, and the one or more first candidate locations and the one or more second candidate locations. The device may determine, based on analyzing the path finding factors, a path for the user to obtain the first item then the second item.