SynAxis Elevate Group
Core Technological Infrastructure
At the heart of Neuroelavate Dynamics lies a proprietary ecosystem designed to bridge the gap between digital intelligence and biological matter.
ElevateOS™ (Autonomous System): Our flagship autonomous operating system, engineered to orchestrate biological dynamics with unprecedented precision. It serves as the foundational environment for real-time neuro-phenomenological modulation and feedback loops.
E.L.A.V.A.T.E. Engine™: (External Loop Auditory & Vibrotactile Adaptive Therapy Engine)
This is the high-performance ‘motor’ driving ElevateOS™. It utilizes advanced algorithms to deliver personalized, adaptive therapy through multi-sensory stimulation, specifically optimized for neurological recovery and autonomic response regulation.
IP Status Note: Both ElevateOS™ and the E.L.A.V.A.T.E. Engine™ are currently in the formal patent application phase. The foundational theories and technical specifications driving these technologies are partially disclosed through our 8 Zenodo pre-prints listed below.
Proprietary Technology Disclosures & IP Pre-prints
System and Method for Geometric Inference of Neurophenomenological States and Somatic Feedback
Abstract:
The present disclosure relates to a system and method for inferring and modulating the neurophenomenological state of a user. The invention overcomes fundamental limitations of prior art in brain-computer interfaces (BCIs), namely the “semantic gap” that precludes a holistic understanding of a user’s subjective state.1 The disclosed system introduces a novel paradigm of “geometric inference of consciousness,” replacing conventional signal processing with the construction and analysis of a high-dimensional geometric manifold representative of the user’s state.1 The system comprises a non-invasive neuro-magnetic sensor apparatus for acquiring neural data, a processing unit configured to construct a General Reality Manifold (GRM) as a dynamic model of the user’s cognitive and affective state, and a haptic feedback interface for conveying somatic information. The principal function involves calculating a state trajectory as a geodesic path on said manifold and translating its intrinsic geometric properties into meaningful, intuitive neurohaptic feedback, thereby establishing a closed-loop system for the direct perception and modulation of a user’s internal state
Yıldırım, E. (2025). System and Method for Geometric Inference of Neurophenomenological States and Somatic Feedback (Patent). Zenodo. https://doi.org/10.5281/zenodo.17183084
System and Method for Geometrodynamic Neuro-State Mapping via a Mobile Peripheral Device
Abstract:
A system for non-invasively mapping a subject’s neuro-cognitive state is disclosed. The system comprises a peripheral device configured for physical and data communication with a host mobile computing device (e.g., a smartphone). The peripheral device includes an array of novel neuro-magnetic sensors fabricated from metamaterials designed according to principles of geometrodynamics. An onboard processing unit executes a geometric inference algorithm to process sensor data, which represents subtle magnetic fluctuations associated with neural activity. This algorithm maps the processed data as a state vector trajectory onto a high-dimensional, dynamic geometric structure, termed the General Reality Manifold (GRM). The host mobile device provides a user interface for visualizing and interacting with the GRM, thereby offering an unprecedented, high-fidelity representation of the subject’s holistic cognitive, interoceptive, and phenomenal state for applications in digital wellness, cognitive enhancement, and neuro-diagnostics.
Yıldırım, E. (2025). System and Method for Geometrodynamic Neuro-State Mapping via a Mobile Peripheral Device (Patent). Zenodo. https://doi.org/10.5281/zenodo.17317803
System and Method for Geometrodynamic Inference of Neurophenomenal States via Manifold Resonance Tomography
Abstract:
A system and method for the non-invasive mapping of the high-dimensional geometric structure of neural activity are disclosed. The invention provides a means to overcome the fundamental limitations of prior art neuroimaging technologies, thereby bridging the “semantic gap” between measured neural signals and the subjective, phenomenal states they represent. The system comprises an array of novel metamaterial sensors, termed Geometrodynamic Transducers, configured as an add-on module for a conventional magnetic resonance imaging (MRI) apparatus. A processing unit operatively coupled to the sensor array is configured to execute a geometric inference algorithm. This algorithm processes the acquired sensor data to compute a time-resolved trajectory of the subject’s neurophenomenal state within a predefined, high-dimensional geometric state-space, the General Reality Manifold (GRM). The invention enables the direct measurement and reconstruction of geometric correlates of neurophenomenal states, offering unprecedented insight into the structure of consciousness and providing a powerful new tool for clinical diagnostics, research, and brain-computer interfacing
Yıldırım, E. (2025). System and Method for Geometrodynamic Inference of Neurophenomenal States via Manifold Resonance Tomography (Patent). Zenodo. https://doi.org/10.5281/zenodo.17317912
A System and Method for Large-Scale Geometric Analysis of Aggregated Neurophenomenological Data
Abstract:
A system and method for analyzing large-scale, aggregated neurophenomenological data are disclosed. Prior art in neurotechnology is limited by a “semantic gap,” failing to capture the holistic structure of subjective experience from neurophysiological signals.1 The present invention overcomes these limitations by providing a system comprising a network of remote sensor modules and a central analytics platform. The system receives neuro-data streams and transforms them into high-dimensional geometric objects, termed General Reality Manifolds (GRMs), which represent the holistic conscious state of each individual subject.1 These GRMs are subsequently anonymized and aggregated on the central platform. The platform performs geometric and topological analysis on the aggregated manifold data to identify population-level trends, correlations, and biomarkers related to collective cognitive and mental health, thereby providing a novel means of deriving semantic insights from neurophysiological data at a macro scale.
Yıldırım, E. (2025). A System and Method for Large-Scale Geometric Analysis of Aggregated Neurophenomenological Data (Patent). Zenodo. https://doi.org/10.5281/zenodo.17341300
A System and Method for Geometric State Inference and Somatic Feedback
Abstract:
The present invention relates generally to the field of neurotechnology and, more specifically, to a system and method for inferring and modulating the cognitive-phenomenal state of a user. The system comprises a sensor apparatus for detecting neuro-physiological signals, a processing unit, and a somatic feedback device. The method involves the processing unit executing instructions to: (a) receive sensor data representative of a user’s neuro-physiological activity; (b) generate a high-dimensional, dynamic geometric manifold, hereinafter termed a General Reality Manifold (GRM), representing the user’s holistic cognitive, interoceptive, and phenomenal state; (c) analyze one or more geometric properties of said GRM, including but not limited to its local curvature, the trajectory of a state vector thereupon, and its topological features; and (d) generate and transmit control signals to the somatic feedback device based on said analysis. The control signals effectuate a somatic stimulus that is a non-arbitrary, geometric transformation of the analyzed properties of the GRM, thereby enabling the user to perceive and navigate their internal state-space in an intuitive, embodied manner
Yıldırım, E. (2025). A System and Method for Geometric State Inference and Somatic Feedback (Patent). Zenodo. https://doi.org/10.5281/zenodo.17341452
An Apparatus and Method for Geometrodynamic Neurohaptic Feedback via a General Reality Manifold
Abstract:
This disclosure details an invention that addresses a fundamental limitation in the art of neurotechnology, herein termed the “semantic gap.” Current non-invasive Brain-Computer Interfaces (BCIs) are constrained by low-fidelity sensing modalities and non-intuitive feedback mechanisms, limiting their application to simplistic command-and-control paradigms.1 The present invention overcomes these limitations through a wearable apparatus and associated method that enables a fundamentally new mode of human-computer symbiosis. The invention comprises three core innovations. First, it introduces a novel class of neuro-magnetic metamaterial sensors, designed according to the principles of The Particle Framework (TPF), which are configured to detect the local geometrodynamic sequelae of neural activity rather than its remote electrophysiological or hemodynamic byproducts. Second, it employs a computational method of “geometric inference,” which resolves the high-dimensional sensor data into a real-time trajectory of the user’s holistic cognitive-phenomenal state on a high-dimensional, dynamic state-space known as the General Reality Manifold (GRM). Third, it establishes a neurohaptic feedback method, governed by the Integrated Semiotic Framework (ISF), that translates the intrinsic geometric properties of this state-space trajectory (e.g., local curvature, vector velocity, torsion) into complex, multi-modal somatic sensations. This closed-loop system provides the user with a direct, intuitive, and non-linguistic perception of their own internal state, thereby transforming the BCI from a mere peripheral device into a true cognitive orthosis.
Yıldırım, E. (2025). An Apparatus and Method for Geometrodynamic Neurohaptic Feedback via a General Reality Manifold (Patent). Zenodo. https://doi.org/10.5281/zenodo.17443738
Geosemantic Neurohaptic Interface for Brain-Computer Interaction
Abstract:
A system and method for brain-computer interaction is disclosed, comprising a non-invasive neuro-magnetic sensor array for acquiring high-fidelity neural signals, a geometric inference processor, and a neurohaptic feedback device. The processor is configured to map the acquired neural signals onto a high-dimensional geometric state-space, hereinafter referred to as the General Reality Manifold (GRM), which represents the user’s holistic cognitive and phenomenal state. The geometric properties of the state’s trajectory on the GRM, including but not limited to local curvature and geodesic deviation, are then translated, via an Integrated Semiotic Framework (ISF), into a complex, meaningful, and non-arbitrary haptic feedback signal. This closed-loop system provides the user with an intuitive, somatic perception of their own internal state, thereby bridging the semantic gap inherent in prior art brain-computer interface (BCI) systems and providing a direct solution to the symbol grounding problem in neurotechnology
Yıldırım, E. (2025). Geosemantic Neurohaptic Interface for Brain-Computer Interaction (Patent). Zenodo. https://doi.org/10.5281/zenodo.17443801
System and Method for Population-Level Neuro-Phenomenological Analysis via Geometric and Topological Aggregation of Manifold-Based Consciousness Models
Abstract:
A system and method for analyzing population-level neuro-phenomenological states are disclosed. The system comprises a central server configured to receive anonymized data representing individual consciousness models, termed General Reality Manifolds (GRMs), from a plurality of distributed client devices. The server constructs a higher-dimensional aggregate manifold, the Geometrodynamic Population Manifold (GPM), from the received GRM data. The system applies a suite of analytical techniques to the GPM, including topological data analysis (TDA) via persistent homology to identify population clusters and dynamic patterns, and differential geometry to compute local curvature as a measure of population-state stability. Geometric deep learning models are employed to identify complex, non-linear patterns within the GPM. Embodiments include a federated learning architecture that enables model training without centralizing sensitive neurodata, thereby preserving user privacy and ensuring the ethical integrity of the system. The method provides novel biomarkers for public health monitoring, epidemiological research, and pharmaceutical development.
Yıldırım, E. (2025). System and Method for Population-Level Neuro-Phenomenological Analysis via Geometric and Topological Aggregation of Manifold-Based Consciousness Models (Patent). Zenodo. https://doi.org/10.5281/zenodo.17443522
A System and Method for the Geometrodynamic Inference and Modulation of Cognitive States
Abstract:
The present invention discloses a system and method for overcoming the fundamental limitations of prior art in brain-computer interfaces (BCIs) by replacing conventional statistical signal processing with a novel paradigm of geometric inference. The invention provides for the mapping and modulation of a subject’s holistic cognitive and phenomenal state through a high-dimensional, dynamic data structure, hereinafter referred to as the General Reality Manifold (GRM). The system comprises a novel neuro-magnetic sensor array for the non-invasive acquisition of high-fidelity neural data, a geometrodynamic processing unit for real-time inference, and a neuro-haptic feedback module. These components operate in a closed loop to first construct a geometric representation of a subject’s consciousness and subsequently use the intrinsic properties of this geometry—such as local curvature and geodesic deviation—to interpret and modulate the cognitive state. By analyzing the structure of consciousness as a geometric object rather than a collection of signals, the invention bridges the “semantic gap” that has historically limited the efficacy and intuitive capacity of human-computer interaction, enabling a new class of applications in clinical neurodiagnostics, cognitive enhancement, and digital therapeutics.
Yıldırım, E. (2025). A System and Method for the Geometrodynamic Inference and Modulation of Cognitive States (Patent). Zenodo. https://doi.org/10.5281/zenodo.17443559