Geolocation-Based Advisor Matching System
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Solution Overview
Problem
Conventional methods for scheduling meetings between clients and advisors are inefficient, requiring extensive back-and-forth communication and failing to account for real-time availability and proximity, and existing software solutions struggle with managing data across different databases and platforms.
Innovation Solution
A computer system and method that utilizes a server to monitor client datasets for triggering threshold values, activating geolocation modules to identify nearby advisors and querying their availability, allowing for automated notifications to clients about available advisors in their geographical proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional scheduling methods are used, then clients can meet with advisors, but extensive back-and-forth communication is required and scheduling efficiency is low
Solution Approach 1:
The system performs preliminary actions by proactively monitoring client datasets for triggering events and pre-identifying suitable advisors before the client initiates a meeting request. This includes pre-filtering advisors based on expertise matching, geographical proximity, and real-time availability, so that when a triggering event occurs, the meeting can be scheduled immediately without back-and-forth communication.
Solution Approach 2:
The system enables self-service by automatically detecting when a client needs advisor assistance through dataset monitoring, and autonomously matching them with appropriate advisors based on predefined criteria. The system handles the entire scheduling process without requiring manual intervention from either the client or advisor, eliminating communication overhead while maintaining service quality.
2Device complexity
If data is stored in separate databases for clients and advisors, then data organization is maintained, but data management complexity increases
Solution Approach 1:
The system introduces an intermediary analytical engine that acts as a mediator between the client database and advisor database. This engine receives data from both databases, performs unified analysis to identify triggering events and match clients with advisors, and coordinates data flow between the separate databases. This intermediary layer simplifies data management by providing a centralized coordination point while maintaining the benefits of separate database storage.
3Adaptability or versatility
If real-time geolocation monitoring is implemented, then advisor proximity can be determined, but additional data processing requirements increase
Solution Approach 1:
The system merges geolocation data processing with the existing dataset monitoring framework. Instead of treating location monitoring as a separate complex function, it is integrated into the unified analytical engine that already monitors client datasets for triggering events. The analytical engine combines dataset analysis, geolocation monitoring, and advisor matching into a single coordinated process, reducing overall system complexity while enabling real-time location-based matching.
Data Source
AI summary
The system and methods described herein provide for managing user datasets by facilitating interactions between users and their advisors following location-based notification of certain triggering events in the user dataset. The geolocation of the user is used to identify nearby advisors who can provide consultation as required by the user. Some embodiments facilitate introductions to a potential user of a set of advisors matched to the user's profile and in response to certain triggering events in the user's dataset.