Machine Learning Roadside Assistance Dispatch System
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Solution Overview
Problem
Conventional roadside assistance systems are inefficient and prone to errors due to reliance on scripted information gathering and manual identification of service providers, leading to increased wait times for users.
Innovation Solution
A data processing system utilizing machine learning to generate roadside assistance instructions, which includes an interactive user interface tailored to user profiles, allowing information input in any order and leveraging natural language recognition to improve data capture efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional roadside assistance systems use scripted information gathering procedures, then the process structure is simple and controlled, but the information capture efficiency is low and time-consuming
Solution Approach 1:
The patent replaces the mechanical/scripted information gathering process with an automated machine learning-based system. The ML model automatically processes user inputs, identifies service needs, and dispatches providers without requiring structured scripted interactions, thereby improving productivity while managing system complexity through automation.
Solution Approach 2:
The system enables self-service by allowing users to provide information in their own words through natural language input. The machine learning model automatically processes this unstructured input to extract necessary information and determine service requirements, eliminating the need for users to follow predetermined scripts and improving information capture efficiency.
2Reliability
If roadside assistance associates manually identify and dispatch service providers, then the process is flexible and adaptable, but the accuracy and reliability of service provider selection is reduced due to human error
Solution Approach 1:
The patent replaces manual service provider identification with an automated machine learning system. The ML model processes user information, evaluates service needs, and selects appropriate providers based on learned patterns and criteria, thereby improving reliability and accuracy while reducing human error in the automation process.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from service outcomes and adjusts its provider selection algorithms. This feedback loop improves the accuracy of service provider identification over time while maintaining high levels of automation in the dispatch process.
3Ease of operation
If the system requires information to be input in a particular order, then the data collection process is structured and controlled, but the user experience is inefficient and time-consuming
Solution Approach 1:
The patent implements a dynamic information collection process where the system adapts to user input in any sequence rather than requiring fixed order. The machine learning model processes information as it arrives and dynamically adjusts its requests for additional information, allowing users to provide data in their own time and order while reducing overall wait time.
Solution Approach 2:
The system performs preliminary actions by proactively requesting and processing necessary information based on the current state of the interaction. The machine learning model anticipates what information is needed and can pre-process it, reducing the time required for information collection and accelerating the overall assistance process.
Data Source
AI summary
Systems, methods, computer-readable media, and apparatuses for receiving requests for roadside assistance, generating user interfaces and using machine learning to generate roadside assistance instructions are provided. In some examples, a request for roadside assistance may be received. A user and one or more partners may be identified based on the request. In some examples, a profile associated with the user, partner or the like may be identified. A user interface may be generated based on the profile and may include features unique to the profile, partner, or the like. In some arrangements, the interface may include a first portion and a second portion. Selection of an option from the first portion may cause the system to identify data for display in the second portion and cause the data to be displayed in the second portion. Machine learning may be used to determine or identify one or more roadside assistance instructions and a roadside assistance instruction may be generated and executed.