Camera-Based Stress Detection for Diverse Industries

Publication ID: 24-11857323_0007_PTD

Published: November 07, 2025

Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Camera-Based Stress Detection for Diverse Industries,” Published Technical Disclosure No. 24-11857323_0007_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857323_0007_PTD

This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,323.

Summary of the Inventive Concept

The inventive concept applies the core technology of camera-based stress determination to new applications and use cases, including livestock monitoring, autonomous vehicles, athlete performance, retail customer experience, and remote worker well-being.

Background and Problem Solved

The original patent addressed the issue of human stress detection using camera-based systems. However, the problem of stress detection extends beyond human individuals to various industries and fields. The new inventive concept tackles these unaddressed areas, leveraging the core technology to improve animal welfare, road safety, athletic performance, customer satisfaction, and remote worker productivity.

Detailed Description of the Inventive Concept

The new inventive concept comprises a camera-based sensor and a trained processing unit, which analyzes images of behavior, facial expressions, or physiological responses to detect signs of stress. In the livestock monitoring application, the system detects stress in animals, enabling farmers to take preventative measures. In autonomous vehicles, the system alerts the control system to potential safety risks. For athletes, the system provides real-time feedback on stress levels during competition. In retail, the system offers personalized recommendations for stress relief. For remote workers, the system provides alerts and recommendations for stress management.

Novelty and Inventive Step

The new inventive concept's novelty lies in its application of camera-based stress detection to diverse industries, which was not anticipated by the original patent. The inventive step resides in the adaptation of the core technology to address specific challenges in each industry, such as animal behavior analysis, driver behavior monitoring, or facial expression recognition in athletes.

Alternative Embodiments and Variations

Alternative embodiments may include using different types of cameras, such as thermal or hyperspectral cameras, or integrating the system with other sensors, like heart rate or skin conductance monitors. Variations may involve applying the technology to other industries, such as healthcare, education, or finance, or developing new algorithms for stress detection.

Potential Commercial Applications and Market

The inventive concept has significant commercial potential across various industries, including agriculture, automotive, sports, retail, and human resources. The market demand for stress detection and management solutions is growing, driven by the need for improved productivity, safety, and well-being.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Computer Vision, Machine Learning, and Biometric Signal Processing with expertise in neural network-based stress detection systems, requiring advanced signal processing skills, machine learning algorithm design, and interdisciplinary understanding of physiological response analysis

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with graduate-level training in computer science or biomedical engineering, proficient in neural network architectures, image processing techniques, and signal feature extraction, with working knowledge of applying machine learning to physiological monitoring systems

Obviousness Rationale

A PHOSITA would recognize the source patent's camera-based stress detection methodology as a generalizable technology that could be readily adapted to multiple domain-specific applications by applying standard machine learning transfer learning techniques. The core neural network architecture and bitplane analysis method disclosed in the source patent provides a flexible framework that can be trivially modified to analyze different subject populations or behavioral contexts. The PTD's variations represent predictable extensions of the original stress detection technology using standard machine learning adaptation strategies.

Obvious Combinations & Variations

Source Patent Element

Camera-based image sequence processing using LSTM neural network for stress detection

PTD Variation

Applying identical neural network architecture to analyze livestock, autonomous vehicle drivers, athletes, retail customers, and remote workers

Obviousness Reasoning

Transferring a machine learning model across domains is a standard technique when the underlying signal processing methodology remains consistent, representing a predictable design variation

Source Patent Element

Bitplane analysis in red, green, and blue color channels for physiological signal extraction

PTD Variation

Extending color channel analysis to different subject populations and capturing additional behavioral signals beyond human facial responses

Obviousness Reasoning

A PHOSITA would recognize color channel feature extraction as a generalizable technique that can be applied to diverse imaging scenarios with minimal algorithmic modification

Source Patent Element

Trained processing unit using hemodynamic change training datasets

PTD Variation

Retraining the neural network on domain-specific behavioral datasets while maintaining core LSTM architecture

Obviousness Reasoning

Model transfer and retraining on specialized datasets is a standard machine learning technique for adapting generalized algorithms to specific use cases

Source Patent Element

Camera-based physiological monitoring system

PTD Variation

Integrating additional sensor modalities like thermal or hyperspectral cameras to enhance stress detection

Obviousness Reasoning

Sensor fusion and multi-modal signal processing are well-established techniques for improving machine learning model performance across various detection domains

Source Patent Element

Neural network for extracting stress-related physiological signals

PTD Variation

Expanding stress detection to different industries with unique behavioral monitoring requirements

Obviousness Reasoning

Applying a generalized machine learning approach to solve domain-specific monitoring challenges represents a predictable technological extension

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857323, a person having ordinary skill in the art would find the disclosed camera-based stress detection variations obvious and predictable extensions of the source patent's core neural network methodology. The PTD demonstrates that applying the original stress detection framework to diverse domains represents a straightforward technological adaptation using standard machine learning transfer learning techniques, thereby rendering potential patent claims obvious as a matter of law.

Original Patent Information

Patent Number	US 11,857,323
Title	System and method for camera-based stress determination
Assignee(s)	NURALOGIX CORPORATION