Cognitive Queue Management via Non-Newtonian Fluid Modeling

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Solution Overview

Problem

Conventional queue management systems rely solely on a 'first-in-time' protocol, failing to consider contextual and cognitive user properties to optimize queue efficiency and flow.

Innovation Solution

A queue management system that utilizes device recognition, user data retrieval, and queue management circuits to analyze cognitive and contextual data, modeling users as a fluid to dynamically reorder the queue, employing Dissipative Particle Dynamics for efficient crowd management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a first-in-time queue protocol is used, then the queue management system is simple to operate, but the queue efficiency and user flow optimization deteriorate

Engineering Contradiction:

Improvequeue management simplicityVSAvoidqueue efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces the mechanical first-in-time queue protocol with a cognitive computing system that uses machine learning models to analyze user properties, contextual data, and environmental factors. This substitution enables dynamic queue reordering based on multiple parameters including user urgency, preferences, and real-time conditions, thereby improving queue efficiency while maintaining ease of operation through automated decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically changes queue ordering parameters by considering multiple factors beyond arrival time, including user cognitive data, contextual information, and real-time environmental parameters. The queue management circuit reorders users based on weighted combinations of these parameters, allowing the system to adapt queue efficiency to varying conditions while keeping the user interface simple

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If conventional first-in-time queue management is used, then the system complexity is low, but the analytics capability and revenue optimization deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidanalytics capability

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent introduces cognitive computing circuits and machine learning models as intermediary layers between the queue management system and the users. These intermediaries process and analyze multiple data sources including user properties, contextual information, and behavioral patterns, transforming raw data into actionable queue ordering decisions while maintaining a simple user-facing interface

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary analysis of user properties and contextual data before queue ordering decisions are made. By pre-processing and storing user profiles, preferences, and historical behavior data, the system enables rapid analytics-driven queue reordering without adding complexity to the real-time user experience

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11810159B2Cognitive and contextual queue management

Publication Date: 2023.11.07 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A queue management system including a user data retrieving circuit configured to retrieve a user property of the user and a queue managing circuit configured to model the plurality of users as a non-Newtonian fluid, to analyze the user properties to assign each user an anisometric polarity parameter, and to solve an objective function for a queue property to cause the plurality of users to collectively act as the non-Newtonian fluid in the queue.