Docking Station Context-Aware Peripheral Configuration

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Solution Overview

Problem

Existing docking stations lack context-aware and user-centric configuration management, requiring manual user interaction for connecting and initializing peripherals, which is inefficient and time-consuming.

Innovation Solution

A method involving a docking station and an external computing device that identifies contextual data, trains a configuration management model to generate rules for automatic configuration, and applies these rules to manage docking station and peripherals without user interaction, using machine learning and neural networks to optimize connections and power distribution based on user needs and peripheral capabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual user interaction is used for connecting and initializing peripherals, then the docking station can be configured with user control, but the process becomes inefficient and time-consuming

Engineering Contradiction:

ImproveUser control over peripheral configurationVSAvoidTime required for manual connection and initialization

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs preliminary actions by training a machine learning model with contextual data during calibration phases. The model learns optimal configuration patterns in advance, enabling automatic configuration when peripherals are connected without requiring manual user interaction during actual use.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The docking station implements self-service through the trained machine learning model that automatically configures peripherals based on contextual data. The system serves itself by making intelligent configuration decisions without human intervention, reducing both time loss and maintaining ease of operation.

Inventive Principle:

Principle #25Self-service

2Productivity

If automatic configuration is implemented without user interaction, then productivity is enhanced, but the system requires complex machine learning models and contextual data processing

Engineering Contradiction:

ImproveSpeed of peripheral configurationVSAvoidComplexity of configuration management model

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary between the docking station and peripherals. It processes contextual data and generates configuration decisions, simplifying the overall system architecture by centralizing intelligence in a dedicated component rather than distributing complexity throughout the entire system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system manages complexity by focusing on changing key parameters such as contextual data collection and model training configurations. By adjusting these parameters during calibration, the system adapts to different environments without requiring fundamental architectural changes, thus maintaining productivity while controlling complexity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If contextual data is collected and processed, then context-aware configuration is achieved, but the system requires additional data processing and model training resources

Engineering Contradiction:

ImproveContext-aware configuration capabilityVSAvoidEnergy consumption for data processing and model training

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system uses periodic action by collecting contextual data continuously but processing it periodically through the trained model. The model is trained during calibration phases and then applied repeatedly for configuration decisions, reducing continuous processing energy consumption while maintaining adaptability to contextual changes.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The machine learning model is trained in advance during calibration phases using collected contextual data. This preliminary action stores learned patterns that can be quickly applied during operation, reducing the energy required for real-time data processing while maintaining context-aware configuration capabilities.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12608324B2Managing a configuration of a docking station and computing peripherals

Publication Date: 2026.04.21 DELL PROD LP

AI summary

Managing a configuration of a docking station and computing peripherals, including performing, at a first time, a calibration and configuration of a configuration management model, including identifying contextual data associated with the docking station and the computing peripherals; training, based on the contextual data, the configuration management model, including generating a configuration policy including configuration rules, the configuration rules for performing computer-implemented actions to automatically configure the docking station and the computing peripherals; performing, at a second time, a steady-state management of the docking station and the computing peripherals, including monitoring the contextual data of the docking station and the computing peripherals; and accessing the configuration management model including the configuration policy, identifying the configuration rules based on the monitored contextual data, applying the configuration rules to perform the computer-implemented actions to configure the docking station and the computing peripherals.