Trip Planning System with Machine Learning Recommendations
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Solution Overview
Problem
Traditional systems for generating trip plans for relationship managers are time-consuming and inefficient, requiring manual planning that diverts resources away from cultivating client relationships and often results in missed opportunities to meet clients due to lack of awareness about nearby client locations.
Innovation Solution
A system utilizing machine learning models and graphical user interfaces to generate trip plans with recommendations, automating the process of identifying potential leads, optimizing routes, and allowing users to input preferences and timing data, thereby streamlining the trip planning process and enhancing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trip planning is used, then relationship managers can customize their trips, but the planning process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service trip planning by automatically generating trip plans based on client data and relationship manager preferences. The machine learning model processes client information, determines optimal routes, and creates schedules without requiring manual intervention, allowing relationship managers to simply input their preferences and receive automated trip plans.
Solution Approach 2:
The patent replaces the mechanical manual planning process with an automated machine learning system. The machine learning model substitutes for the relationship manager's manual research and planning activities, using algorithms to process client data, determine visit priorities, and generate optimized trip itineraries automatically.
2Productivity
If manual trip planning is used, then relationship managers can control their schedules, but productivity decreases due to time spent on planning
Solution Approach 1:
The system performs preliminary actions by pre-processing client data, identifying potential leads, and pre-determining optimal trip routes before the relationship manager needs to execute the trip. The machine learning model analyzes historical data and client information in advance to generate ready-to-execute trip plans, eliminating the need for last-minute manual planning.
Solution Approach 2:
The trip planning system serves itself by automatically generating, optimizing, and managing trip schedules without requiring relationship manager intervention. The system monitors client data changes and automatically updates trip plans, allowing relationship managers to focus entirely on client interactions rather than planning logistics.
3Reliability
If relationship managers focus on manual trip planning, then they can ensure trip details are accurate, but they miss opportunities to meet clients
Solution Approach 1:
The machine learning model continuously receives feedback from client interactions and data updates, using this information to refine and improve trip plan accuracy. The system learns from past trip outcomes and client responses to optimize future trip recommendations, ensuring increasing accuracy over time without requiring manual verification of each detail.
Solution Approach 2:
The patent replaces manual verification of trip details with automated machine learning algorithms that continuously optimize trip plans. The system automatically validates trip logistics, adjusts schedules based on real-time data, and ensures accuracy through computational processing rather than manual checking, freeing relationship managers to focus on client engagement.
4Adaptability or versatility
If relationship managers manually research target clients, then they can identify suitable prospects, but the process is time-consuming and organizational difficulty increases
Solution Approach 1:
The system performs self-service client identification by automatically analyzing client data, identifying potential leads, and ranking prospects based on machine learning algorithms. The system independently researches and evaluates client suitability without requiring relationship managers to manually search through databases or conduct preliminary research.
Solution Approach 2:
The patent replaces manual client research with automated machine learning processes that systematically analyze client information, identify patterns, and generate prospect recommendations. The machine learning model processes large datasets to identify suitable clients, substituting the relationship manager's manual research efforts with computational analysis.
Data Source
AI summary
Disclosed embodiments may include a method for generating a trip plan with trip recommendations. The method may include generating and transmitting a graphical user interface to a user device and receiving, from the user device, criteria, which is then used to retrieve candidate data. The criteria and candidate data may then be used to generate potential leads using a machine learning model. The method may include generating a graphical user interface to display the potential leads and receive timing and preference data from the user. The method may further include generating, using a machine learning model, a plan based on the potential leads and other data. The plan may be presented to a user via graphical user interface, which may be used to modify and update the plan. The plan may be stored and accessed by the user at a later time through a user device.