% ============================================================================ % ssm_RAJ_v1_patent_application.tex % % UNITED STATES PATENT APPLICATION % SOLID STATE MACHINE FOR SELF-REGULATING COMPUTATIONAL SYSTEMS % % Inventor: Robert A. James % Filing Date: December 2025 % % This document uses the original non-versioned section content which is % language-agnostic and concept-focused, protecting the SSM concept itself % ============================================================================ \documentclass[12pt,letterpaper]{article} % ============================================================================ % PACKAGES % ============================================================================ \usepackage[utf8]{inputenc} \usepackage[T1]{fontenc} \usepackage{times} \usepackage[margin=1in]{geometry} \usepackage{graphicx} \usepackage{float} \usepackage{placeins} \usepackage{amsmath} \usepackage{amssymb} \usepackage{booktabs} \usepackage{enumitem} \usepackage{setspace} \usepackage{hyperref} \usepackage{fancyhdr} \usepackage{lastpage} \graphicspath{{figures/}} % ============================================================================ % PAGE SETUP % ============================================================================ \setlength{\parindent}{0.5in} \setlength{\parskip}{0.5em} \setlength{\headheight}{14.5pt} \onehalfspacing \pagestyle{fancy} \fancyhf{} \rhead{Application No.: [To Be Assigned]} \lhead{Docket No.: SSM-001} \rfoot{Page \thepage\ of \pageref{LastPage}} \renewcommand{\headrulewidth}{0pt} \newcommand{\figref}[1]{FIG.~#1} % ============================================================================ % BEGIN DOCUMENT % ============================================================================ \begin{document} % ============================================================================ % TITLE PAGE % ============================================================================ \begin{center} \Large\textbf{UNITED STATES PATENT APPLICATION} \vspace{2em} \LARGE\textbf{STEADY STATE MACHINE FOR}\\[0.3em] \LARGE\textbf{SELF-REGULATING COMPUTATIONAL SYSTEMS} \vspace{1em} \large\textit{An Adaptive Runtime System Exhibiting}\\ \large\textit{Autonomous Workload Optimization and Deterministic Convergence} \vspace{2em} \normalsize \begin{tabular}{ll} \textbf{Inventor:} & Robert A. James \\ \textbf{Assignee:} & [To Be Determined] \\ \textbf{Filing Date:} & December 2025 \\ \textbf{Application Type:} & Utility Patent Application \\ \end{tabular} \end{center} \vspace{2em} \noindent\textbf{Technical Field:} Adaptive Virtual Machines, Self-Regulating Computational Systems, Feedback-Loop Control Architectures, Runtime Optimization \vspace{1em} \noindent\textbf{Related Systems:} Virtual Machine Runtimes, Adaptive Execution Environments, Embedded Systems, Microkernel Subsystems \clearpage % ============================================================================ % TABLE OF CONTENTS % ============================================================================ \tableofcontents \clearpage % ============================================================================ % INCLUDE ORIGINAL NON-VERSIONED SECTIONS % These sections are language-agnostic and concept-focused % ============================================================================ \input{sections/02_field.tex} \input{sections/03_background.tex} \input{sections/04_summary.tex} % ============================================================================ % DRAWINGS AND FIGURES % ============================================================================ \FloatBarrier \section{Brief Description of the Drawings} The following figures illustrate representative embodiments and experimental validation of the Solid State Machine architecture. The drawings depict configuration distributions, mode selection behavior, feedback loop effects, workload-shape invariance, and performance comparisons between adaptive and static configurations. \subsection{Configuration Space Analysis} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch1_config_distribution.png} \caption{\textbf{FIG. 1 -- Configuration Space Distribution.} Performance distribution across the static configuration space, illustrating the wide variance in execution behavior when feedback loops are configured manually. The distribution demonstrates that static configurations produce highly variable performance outcomes, motivating the need for autonomous mode selection.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch1_config_ranking.png} \caption{\textbf{FIG. 2 -- Configuration Ranking.} Ranking of static configurations by mean performance and stability metrics. This analysis identifies candidate high-performance configurations that form the basis for validated execution modes in the Solid State Machine.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch1_main_effects.png} \caption{\textbf{FIG. 3 -- Main Effects Analysis.} Main effects plot showing the influence of individual feedback loops (L1--L7) on overall system performance. This factorial analysis reveals which loops contribute most significantly to performance improvement and stability.} \end{figure} \subsection{Mode Selection and Optimization} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch2_runoff_boxplot.png} \caption{\textbf{FIG. 4 -- Configuration Runoff Comparison.} Box plot comparison of candidate top-performing configurations under identical workload conditions. This runoff analysis validates that the selected execution modes represent genuine performance optima rather than statistical artifacts.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch2_optimality_scores.png} \caption{\textbf{FIG. 5 -- Optimality Analysis.} Multi-objective optimality scores across configuration candidates, balancing throughput, variance reduction, and convergence speed. The Solid State Machine's mode selector uses similar multi-criteria evaluation to select appropriate execution modes.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{phase_space_portrait.png} \caption{\textbf{FIG. 6 -- Optimality Analysis.} Execution heat versus performance parameter traced through configuration space during adaptive convergence. The non-retracing path demonstrates state-dependent conductance characteristic of memristive dynamics, with approximately 180-degree reversals at architectural cache boundaries. Horizontal spreads indicate bimodal distributions over dual attractor states at resonance windows.} \end{figure} \subsection{Workload Shape Invariance} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch3_shape1_performance.png} \caption{\textbf{FIG. 7 -- Workload Shape Performance (Set I).} Performance measurements across the first family of workload shapes, including sinusoidal, triangular, and burst-like patterns. The Solid State Machine maintains consistent performance regardless of input waveform characteristics.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch5_shape2_performance.png} \caption{\textbf{FIG. 8 -- Workload Shape Performance (Set II).} Performance validation across additional workload waveforms demonstrating shape-invariant behavior. The system achieves stable throughput across square-wave, sawtooth, and compound mixed-pattern workloads.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch5_cv_comparison.png} \caption{\textbf{FIG. 9 -- Coefficient of Variation Analysis.} Comparison of coefficient of variation (CV) across workload families, demonstrating that the adaptive system maintains low variance regardless of workload shape. Low CV indicates predictable, stable execution behavior.} \end{figure} \subsection{Adaptive Mode Behavior} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch4_mode_usage_stacked.png} \caption{\textbf{FIG. 10 -- Mode Usage Distribution.} Stacked distribution showing how the supervisory mode selector (L8) allocates time across different execution modes for various workload families. The Solid State Machine autonomously selects appropriate modes without manual intervention.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=\textwidth]{ch4_l8_vs_static.png} \caption{\textbf{FIG. 11 -- Adaptive vs. Static Performance.} Direct comparison between the Solid State Machine's adaptive behavior and equivalent static configurations. The adaptive system matches or exceeds static performance while providing automatic workload adaptation.} \end{figure} \subsection{State Vector Dynamics} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig1_snake_trajectory.pdf} \caption{\textbf{FIG. 12 -- State Vector Trajectory.} Representative trajectory of the runtime state vector through the execution heat--entropy phase space. The hysteresis-like behavior demonstrates that the system exhibits memory effects: the current state depends not only on instantaneous workload but also on recent execution history. This memristive characteristic enables stable convergence to appropriate operating points.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig2_K_vs_window.pdf} \caption{\textbf{FIG. 13 -- Performance Scaling Relationship.} Relationship between the stability constant K and the observation window size W, showing the scaling law that governs system behavior. The periodic structure reveals fundamental resonances in the adaptive architecture.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig3_FFT_spectrum.pdf} \caption{\textbf{FIG. 14 -- Spectral Analysis.} Fast Fourier Transform spectrum of state vector dynamics, revealing the characteristic frequencies and periodic structures inherent in the Solid State Machine's feedback architecture. Dominant spectral peaks correspond to fundamental control loop frequencies.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig4_golden_ratio_penalties.pdf} \caption{\textbf{FIG. 15 -- Golden Ratio Interference Pattern.} Performance variations showing interference effects at window sizes related to the golden ratio $\varphi \approx 1.618$. These patterns demonstrate that the system exhibits cache-like resonance behavior governed by mathematical constants.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig5_bimodal_distributions.pdf} \caption{\textbf{FIG. 16 -- Bimodal State Distributions.} Distribution of state vector measurements showing quantum-analog bimodal behavior. Under certain conditions, the system exhibits discrete stable states rather than continuous distributions, analogous to quantized energy levels.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig6_performance_vs_k.pdf} \caption{\textbf{FIG. 17 -- Performance Correlation with K.} Correlation between the stability constant K and measured performance metrics. This relationship enables the mode selector to predict performance outcomes based on state vector measurements.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{fig7_performance_by_regime.pdf} \caption{\textbf{FIG. 18 -- Performance by Operating Regime.} Performance breakdown across different operating regimes identified by the workload characterization subsystem. Each regime corresponds to distinct feedback loop configurations and mode selections.} \end{figure} \begin{figure}[H] \centering \includegraphics[width=0.9\textwidth]{architecture.png} \caption{\textbf{FIG. 19 -- System Architecture.} } \end{figure} \FloatBarrier \newpage % ============================================================================ % DETAILED DESCRIPTION % ============================================================================ \input{sections/06_detailed_description.tex} % ============================================================================ % CLAIMS % ============================================================================ \input{sections/08_claims.tex} % ============================================================================ % ABSTRACT OF THE DISCLOSURE % ============================================================================ \clearpage \section*{Abstract of the Disclosure} \addcontentsline{toc}{section}{Abstract of the Disclosure} \begin{center} \textbf{A STEADY STATE MACHINE FOR SELF-REGULATING COMPUTATIONAL SYSTEMS} \end{center} A Steady State Machine (SSM) is disclosed that provides autonomous, self-regulating control of computational workloads through coordinated feedback loops operating within a virtual or physical execution environment. The SSM continuously measures internal runtime metrics and external workload characteristics, derives stability-oriented control signals, and dynamically adjusts system parameters to maintain an optimized operational state. Unlike conventional virtual machines or runtime systems that rely on static configuration, manual tuning, or heuristic rules, the SSM employs a structured state vector, multi-loop feedback controllers, and a supervisory mode-selection mechanism to converge toward a stable operating point aligned with the workload's intrinsic behavior. The architecture produces deterministic, repeatable steady-state behavior while accommodating workload variability, pipeline turbulence, cache effects, and temporal fluctuations. Experimental validation across 38,400 runs demonstrates zero variance across replicates within each configuration, and an exact equilibrium relationship (K = 1.0) governing resource allocation. The disclosed system can be implemented in software, firmware, or hardware, and applies to virtual machines, microkernels, embedded runtimes, or adaptive control subsystems. \vspace{1em} \end{document}