Control-Variable Neural Networks for Adaptive Multi-Condition Inference

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Solution Overview

Problem

Machine learning models, such as deep neural networks, produce deterministic outputs that cannot be adjusted in a controllable manner, leading to inefficiencies in training and performance across diverse real-world conditions.

Innovation Solution

Incorporating control variables during training to vary their values over a range, allowing a single machine learning model to adapt to different conditions, enabling controlled inferencing through specified variable values.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple machine learning models are trained to handle different conditions, then performance across diverse real-world conditions is improved, but device complexity and memory requirements increase

Engineering Contradiction:

Improveperformance across diverse conditionsVSAvoidmemory requirements

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent combines multiple specialized models into a single unified model that processes input data along with control variable specifications. Instead of maintaining separate models for different conditions, the unified model receives both the input data and control variables indicating the desired condition, then generates appropriate output based on this combined information. This merging approach reduces memory requirements while maintaining adaptability across diverse conditions.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal machine learning model that can perform multiple functions by accepting control variables as additional inputs. This single model is designed to handle various conditions and operations that previously required separate specialized models. The model's architecture allows it to adapt its behavior based on the control variables provided, making it multi-functional and reducing the need for multiple separate models.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If multiple machine learning models are trained for different conditions, then adaptability to varied scenarios is improved, but training time and complexity increase

Engineering Contradiction:

Improveadaptability to varied scenariosVSAvoidtraining time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent merges the training processes of multiple specialized models into a single unified training process. Instead of training separate models for different conditions sequentially, the unified model is trained on a combined dataset that includes input data, control variables representing different conditions, and corresponding target outputs. This consolidation reduces the overall training time while achieving the same adaptability to varied scenarios.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent develops a universal training framework that teaches a single model to handle multiple conditions simultaneously. The training process involves providing the model with diverse input data paired with control variables indicating different scenarios, allowing the model to learn condition-specific behaviors within a single training run. This approach eliminates the need for separate training processes for each condition, significantly reducing training time and complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Stability of the object's composition

If deterministic outputs are produced by machine learning models, then consistency is improved, but controllable adjustment of outputs is lost

Engineering Contradiction:

Improveoutput consistencyVSAvoidcontrollable output adjustment

Core Design Contradiction:

Stability of the object's compositionVSEase of operation

Solution Approach 1:

The patent introduces dynamic control variables that allow the output of the machine learning model to be adjusted based on specified conditions. Instead of producing fixed deterministic outputs, the model now accepts control variables as inputs that dynamically influence the output generation process. This enables controllable adjustment of outputs while maintaining consistency through the structured relationship between control variables and results, allowing users to specify desired outcomes by providing appropriate control variable values.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12462545B2Controllable neural networks or other controllable machine learning models

Publication Date: 2025.11.04 SAMSUNG ELECTRONICS CO LTD

AI summary

A method includes obtaining (such as accessing, receiving, acquiring, etc.), using at least one processor of an electronic device, a machine learning model trained to process input data and generate output data over at least one range of values associated with one or more control variables. The method also includes providing, using the at least one processor, specified input data to the machine learning model and providing, using the at least one processor, one or more specified values of the one or more control variables to the machine learning model. The one or more specified values of the one or more control variables are within the at least one range of values. The method further includes performing inferencing using the machine learning model to process the specified input data and generate specified output data. The inferencing is controlled based on the one or more specified values of the control variable(s).