Context-Aware Destination Accelerators for Faster Transport Requests
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Solution Overview
Problem
User-centric network services face inefficiencies due to the need for manual selection of on-demand services, which can drain resources and occupy interface devices, especially with the increasing number of available services.
Innovation Solution
A network computer system that utilizes historical user data to generate service accelerators, allowing users to automatically request services with minimal input by leveraging contextual information and preconfigured settings, reducing the need for manual selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection interfaces are provided for users to specify service information, then users can select desired services with preferences, but the number of sequential screens increases and interface devices are occupied for longer time
Solution Approach 1:
The system performs preliminary actions by analyzing user historical data, contextual information, and service patterns before the user makes a selection. Service accelerators are pre-configured with predicted service parameters based on user behavior patterns, so when a user selects an accelerator, the service request is already partially prepared with likely desired parameters, significantly reducing selection time.
Solution Approach 2:
The system enables self-service by automatically generating service requests based on user selections of service accelerators. The computing system autonomously fills in service parameters, selects appropriate service providers, and processes requests without requiring users to manually navigate through multiple screens or specify detailed preferences, thus reducing interface occupation time.
2Adaptability or versatility
If multiple on-demand services are made available to users, then service variety increases, but resource consumption and interface device occupation increase
Solution Approach 1:
The system segments the service selection process into two parts: (1) user selects from a simplified set of service accelerator categories, and (2) the computing system automatically expands this into detailed service parameters and provider selection. This segmentation allows the system to support diverse service varieties while keeping the user interface simple and resource-efficient.
Solution Approach 2:
Service accelerators serve multiple functions: they represent service categories, encode user preferences, predict service parameters, and trigger automated request processing. This multi-functionality allows the system to handle diverse service types through a unified, resource-efficient interface mechanism rather than requiring separate interfaces for each service type.
3Loss of information
If sequential selection screens are used for service requests, then users can specify detailed service information, but interface devices are occupied and processing time increases
Solution Approach 1:
The system extracts detailed service specification tasks from the user interface and transfers them to the backend computing system. Users only need to select service accelerators at a high level, while the computing system extracts and fills in detailed service parameters, provider selections, and request specifications automatically based on historical data and contextual analysis.
Solution Approach 2:
Service accelerators act as intermediaries between user intent and detailed service specifications. Instead of users directly interacting with complex service parameter forms, they select accelerators that mediate and translate into detailed service requests, reducing interface complexity while preserving specification detail through the intermediary layer.
Data Source
AI summary
A computing system can detect the launch of a rider application on computing devices of users of a transport service. The computing system can receive location data indicating the current location of each user, and determine a usage pattern for each user based on historical data corresponding to historical utilization of the transport service by the user. Based on the current location and the usage pattern of the user, the computing system can determine one or more suggested destination locations for the user, and transmit, over the one or more networks, display data to cause the rider application to display a destination accelerator for each of the one or more suggested destination locations. The destination accelerator can be selectable by the user to automatically input a destination location into a transport request for the transport service.