Destination Accelerators Using Rider Usage Patterns
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Solution Overview
Problem
User-centric network services face inefficiencies due to the need for manual selection of multiple interfaces for on-demand services, leading to performance issues and resource drain on operative devices.
Innovation Solution
A network computer system that utilizes historical user data to generate service accelerators, allowing users to automatically request services through a single selection, reducing the need for manual input and optimizing the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select services through multiple selection interfaces, then service customization and user control are improved, but device resources are drained and performance issues occur
Solution Approach 1:
The system performs preliminary actions by automatically analyzing user data, identifying usage patterns, and pre-configuring service accelerators before users need to make selections. This eliminates the need for users to manually navigate multiple selection interfaces while still providing personalized service recommendations based on their historical behavior.
2Measurement precision
If multiple selection interfaces are provided for service customization, then service selection precision is improved, but time consumption and interface complexity increase
Solution Approach 1:
The system enables self-service by automatically analyzing user data and generating service accelerator recommendations without requiring users to manually navigate through multiple selection interfaces. The system serves itself by using its own collected user data to make intelligent service recommendations, thereby reducing selection time while maintaining precision.
3Adaptability or versatility
If multiple selection interfaces are used for on-demand services, then service customization is improved, but device resource consumption increases
Solution Approach 1:
The system extracts and utilizes existing user data that is already collected by the service provider for other purposes. By analyzing this existing data to identify usage patterns and generate service accelerators, the system provides customization without requiring additional data collection or processing that would consume extra device energy.
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.