Server Computing Device for Proactive Service Recommendations
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Solution Overview
Problem
Existing navigation systems require users to anticipate their needs and actively input requests for services, leading to missed opportunities for utilizing nearby services, as they often rely on user awareness and proactive inputting.
Innovation Solution
A server computing device and method that monitors user and friend activities, including location and calendar data, to recommend services at intersecting locations within a predetermined time frame, facilitating spontaneous meetings and service utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users actively input service requests into navigation devices, then service information can be obtained, but many missed opportunities occur because users fail to anticipate needs in time
Solution Approach 1:
The system performs preliminary actions by automatically detecting user location, determining travel direction, and identifying nearby services without waiting for user input. The server computing device proactively queries service information based on predicted user needs, resolving the contradiction by eliminating the need for timely user anticipation while preventing loss of service opportunities
Solution Approach 2:
The navigation system performs self-service by automatically monitoring user position, calculating intersecting locations with friends, determining availability windows, and requesting service information without user intervention. This resolves the contradiction by making the system autonomous in obtaining service information while maintaining ease of use
2Loss of information
If wireless navigation devices provide service location lists, then users can view available services, but users must be cognizant of their needs ahead of time and input requests manually
Solution Approach 1:
The system performs preliminary actions by continuously monitoring user location and automatically determining nearby services before the user needs them. The server proactively retrieves service information based on predicted intersecting locations, eliminating both the time loss for manual input and preventing information loss about nearby services
Solution Approach 2:
The system implements feedback by continuously monitoring user position, travel direction, and friend location data, then using this feedback to dynamically determine nearby services and automatically request information. This closed-loop approach prevents information loss while eliminating the need for user input time
3Loss of time
If the system monitors user and friend activities continuously, then timely recommendations can be provided, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating intersecting locations between user and friend trajectories and determining availability windows in advance. This allows timely recommendations without continuous complex monitoring, as the heavy computation is performed proactively based on periodic location updates
4Loss of information
If the system proactively determines service recommendations, then users do not miss service opportunities, but the system must process multiple data sources including location and calendar information
Solution Approach 1:
The server computing device performs multiple functions using a unified approach: it monitors location data, processes calendar information, determines friend availability, calculates intersecting locations, and retrieves service information all through a single integrated system. This multi-functional design prevents information loss about service opportunities while managing data processing complexity through consolidation
Data Source
AI summary
A server computing device and related method for providing recommendations to a user computing device are disclosed. In one example, user activity of the user of a user computing device and friend activity of a friend using a friend computing device are received. A request for a recommendation is received from the user device. Based on the user and friend activities, it is estimated that the user and friend will approach an intersecting location within a window of time, and the user and friend are available to meet in that window. A recommendation of a service offered within a distance of the location is sent, with the recommendation displayed on the user computing device.