Dynamic Queue Prioritization via Historical Behavior Profiles
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Solution Overview
Problem
Service providers with limited capacity, such as restaurants, face challenges in efficiently managing customer reservations and allocating resources due to unpredictable customer arrival times and behaviors, leading to potential delays or denials for parties without reservations.
Innovation Solution
A central orchestration engine dynamically generates predicted queuing profiles based on historical behavior profiles of both service providers and service seeking entities, assigning confidence levels to prioritize service requests and allocate resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service providers use traditional reservation systems with fixed capacity allocation, then resource allocation is simplified, but customer satisfaction deteriorates due to unpredictable arrivals and denial of service for parties without reservations
Solution Approach 1:
The patent implements dynamic queue prioritization where the queue structure automatically adjusts based on real-time factors such as historical behavior profiles, party size, and predicted arrival times. Service providers can dynamically modify service allocation without predetermined rigid rules, allowing the system to adapt to unpredictable customer arrivals while maintaining operational reliability
Solution Approach 2:
The system changes multiple parameters simultaneously including confidence levels for different queue positions, predicted arrival time windows, and historical behavior metrics. These parameter changes enable the orchestration engine to optimize service allocation dynamically, improving reliability while managing complexity through automated parameter adjustment
2Productivity
If service providers allocate resources based on first-come-first-served or fixed reservations, then operational complexity is reduced, but resource utilization efficiency deteriorates due to capacity constraints and idle time
Solution Approach 1:
The patent introduces a central orchestration engine as an intermediary between service providers and customers. This intermediary automatically manages complex queue prioritization, resource allocation, and coordination tasks, thereby improving resource utilization efficiency while shielding service providers from direct complexity. The orchestration engine handles the computational burden of dynamic optimization
Solution Approach 2:
The system performs preliminary actions by pre-calculating queue priorities, predicted arrival times, and resource allocation strategies before service delivery. Historical behavior profiles are analyzed in advance to establish confidence levels and prioritize queues, enabling efficient resource utilization without real-time complexity during actual service delivery
3Reliability
If service providers deny service to parties without reservations, then resource allocation simplicity is maintained, but customer satisfaction and service reliability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the orchestration engine continuously monitors queue positions, predicted arrival times, and historical behavior data. This feedback loop allows the system to dynamically adjust service guarantees and queue priorities, ensuring reliable service delivery while managing complexity through automated feedback-driven optimization rather than manual intervention
Data Source
AI summary
Apparatus and associated methods relate to automatically prioritizing predicted events in a dynamic predicted queueing profile (PQP) for a service provider (SP) for a finite future time period. In an illustrative example, a central orchestration engine (COE) may generate, in response to a request for service from a service seeking entity (SSE), a dynamic queueing event profile (DQEP) associating the SSE with the PQP for the SP at the future time period. The COE may, for example, generate a confidence level of execution (CLE) for each DQEP in the PQP based on a historical behavior profile (HBP) of the SP and of each corresponding SSE. The COE may, for example, apply the confidence level of execution to each corresponding DQEP to assign a priority in the PQP. Various embodiments may, for example, advantageously dynamically prioritize a queue based on historical behavior of an SP and SSEs in the queue.