Continuous Ambient Ionizing Radiation Field Mapping Using Smartphone CMOS Dark Current Analysis with Crowdsourced Spatial Aggregation and Anomaly Detection
Turns every smartphone into a radiation sensor. CMOS image sensors detect ionizing radiation via dark frame pixel events; a neural network classifier distinguishes gamma-ray absorption, Compton scattering, and cosmic ray muon tracks from thermal noise. Crowdsources measurements across millions of phones using privacy-preserving geohash bucketing and Gaussian process interpolation to create block-level radiation maps 1,000x denser than EPA RadNet. Statistical anomaly detection against a learned natural background model triggers real-time public health alerts for radiological incidents within 15 minutes.
Passive Vehicle Occupant Count Estimation Using Roadside Millimeter-Wave Radar Micro-Doppler Analysis with CNN Classification for Automated HOV Lane Enforcement
Counts vehicle occupants from outside the vehicle using 77 GHz FMCW radar micro-Doppler analysis of involuntary physiological motion: respiration (0.15-0.4 Hz thoracic displacement), cardiac chest wall vibration (1-1.7 Hz), and postural micro-sway (0.05-0.3 Hz). A roadside radar unit illuminates passing vehicles, extracts micro-Doppler spectrograms after bulk motion compensation and clutter cancellation, and classifies occupant count (1/2/3/4+) via a CNN with vehicle-type-specific batch normalization. Works through tinted windows, at night, in rain/fog. Captures no images, no license plates, no PII. Output: integer count + confidence score. Privacy-preserving HOV enforcement at 92% binary accuracy, $800-1,200 per unit.
Non-Intrusive Cognitive Decline Screening via Smart Home Appliance Interaction Pattern Analysis with Behavioral Drift Detection
Screens for early cognitive decline by analyzing existing smart home device interaction logs β smart speakers, thermostats, lighting, locks, kitchen appliances β with no additional hardware or wearable compliance. An edge-deployed temporal transformer learns each occupant's baseline interaction grammar across 47 behavioral features mapped to 7 cognitive domains (executive function, episodic memory, temporal orientation, attention, semantic memory, visuospatial navigation, processing speed). CUSUM/EWMA drift detection on per-domain Behavioral Regularity Index scores identifies sustained multi-domain decline, filtering transient confounds (illness, visitors, device changes). All processing on-device; no data leaves the home.
Continuous Residential Foundation Drainage Monitoring via Sump Pump Current Signature Analysis with Precipitation-Correlated Infiltration Estimation
Monitors sump pump health and foundation drainage performance using a $15 smart plug's current waveform analysis. Extracts 28-element motor signature features (inrush profile, harmonic spectrum, check valve reverse-flow detection) per pump cycle, classifies degradation via a temporal convolutional network, and correlates activation patterns with real-time precipitation data to estimate drainage conductance coefficients. Detects drain tile collapse, root intrusion, check valve leakage, discharge pipe freezing, and predicts pump failure months in advance via Weibull survival modeling. No sensors in the sump pit, no plumbing modification, no contact with water.
Residential Cooking Fire Risk Prediction Using Multi-Sensor Fusion of Stove Current Signature, Range Hood Particulate Density, and Ambient Acoustic Anomaly Detection
Predicts cooking fires 2β5 minutes before ignition by fusing three non-visual sensing modalities: smart plug current analysis (cooking method classification, surface temperature estimation via heating element impedance drift, unattended cooking detection), range hood particulate/VOC sensing (PM2.5 exponential ramp detection, humidity-to-particulate ratio for steam/smoke disambiguation), and MEMS microphone acoustic classification (sizzle-to-carbonization transition, oil decomposition popping). A temporal convolutional network on an ESP32-S3 outputs a 0β100 risk score with graduated alerts from notification through automatic stove shutoff. Retrofit BOM under $90, no camera, no cloud dependency.
OpenStenoMask: Dual-MEMS Microphone Stenomask with Exponential Horn Acoustics, Spectral Subtraction Noise Cancellation, and Solar-Assisted Power
A fully open-source stenomask design using dual Knowles MEMS microphones (voice + noise reference), RP2040 DSP with spectral subtraction noise cancellation, exponential horn acoustic coupling for 6β12 dB gain, BLE 5.0 wireless audio, and optional thin-film solar charging. CERN-OHL-S v2 hardware + GPL-3.0 firmware. Complete BOM under $45.
Per-Animal Enteric Methane Emission Attribution Using Proximal Open-Path Laser Absorption Spectroscopy at Livestock Watering Stations with RFID-Gated Gaussian Plume Inversion
Deploys tunable diode laser absorption spectroscopy sensors at livestock watering stations to measure path-integrated methane concentrations in the breath plumes of identified animals. UHF RFID ear tags attribute each measurement to individual cattle, while Gaussian plume inversion with concurrent wind data deconvolves multi-animal scenarios. Bayesian sequential updating across 5β7 watering visits converges per-animal emission factors within Β±15%, enabling precision feed optimization, genetic selection for low-emitting phenotypes, auditable carbon credit verification under Verra VM0042, and early digestive health screening.
Passive Residential Foundation Deformation Monitoring Using Differential GNSS Carrier-Phase Measurements from Rooftop Solar Microinverter Telemetry Arrays
Embeds low-cost multi-constellation GNSS receivers into rooftop solar microinverters and computes carrier-phase differential positioning between receivers on the same roof to detect sub-millimeter foundation settlement, differential tilt, lateral translation, and seasonal heave cycles. Exploits the existing installed base of 5.2 million U.S. solar homes as a distributed geodetic monitoring network. A neighborhood deformation graph with inter-roof baselines distinguishes per-building anomalies from regional subsidence. Alert severity classification delivers actionable structural engineering recommendations through existing solar monitoring apps.
Predictive Vehicle Occupant Injury Mitigation Using Pre-Crash Wearable Biometric Data Fusion with Vehicle Dynamics for Individualized Adaptive Restraint System Optimization
Fuses real-time wearable biometric data (heart rate, HRV, muscle tension via bioelectrical impedance, body composition, upper-body posture from wrist IMU) with vehicle pre-crash severity prediction through a sub-40 ms neural network inference pipeline to compute individualized restraint deployment parameters β airbag pressure, vent timing, pretensioner force profiles, headrest displacement β optimized for the specific occupant's physiology, age-related fragility, pregnancy status, and bracing state. Includes UWB-based in-cabin occupant localization, BLE 5.3 connection subrating for latency-adaptive data link, and federated cross-manufacturer model training with differential privacy.
Real-Time Vehicle Trajectory Anomaly Detection and Behavioral Threat Scoring Using Distributed License Plate Reader Networks with Spatiotemporal Graph Neural Network Analysis
Reconstructs multi-hop vehicle trajectories from sparse LPR camera reads on a residential road graph and applies a spatiotemporal graph neural network β combining a Graph Attention Network spatial encoder, Transformer temporal encoder, and VAE anomaly scorer β to detect casing-associated behavioral patterns: repeated circuits, dead-end probing, dwell-time anomalies, temporal outliers, and cross-session escalation. A federated learning protocol with differential privacy enables multi-neighborhood collaborative training without sharing raw plate data.
Predictive Urban Tree Root Infrastructure Conflict Detection Using Fleet-Mounted Ground-Penetrating Radar and Physics-Informed Neural Network Root Growth Modeling
Mounts compact dual-frequency GPR antenna arrays on municipal fleet vehicles (refuse trucks, street sweepers) that repeatedly traverse street corridors on fixed schedules. An automated root detection pipeline identifies woody roots exceeding 15 mm diameter from hyperbolic radar reflections, and a temporal differencing module measures root tip advance rates across sequential scans separated by weeks to months. A physics-informed neural network trained on validated root water uptake, mechanical deflection, and allometric growth models projects each root system's 3D growth trajectory 2 to 10 years forward. Predicted root envelopes are intersected with GIS-registered utility maps and hardscape layers to generate conflict probability scores for each tree-infrastructure pair, enabling proactive root pruning, barrier installation, or species replacement ranked by expected damage cost and ROI.
Continuous Detection of Subterranean Termite Activity in Residential Structures Using Distributed Piezoelectric Contact Microphones with Edge-Deployed Acoustic Emission Classification
A permanently installed network of low-cost piezoelectric contact microphones bonded to structural wood members captures structure-borne acoustic emissions at 192 kHz. An on-device 1D-CNN classifier distinguishes termite feeding (mandible scraping, 1-8 kHz), soldier alarm signals (head-banging impulses at 5-25 Hz), and gallery construction (substrate rasping, 3-15 kHz) from rodent activity, thermal expansion, and household noise. Multi-sensor signal attenuation analysis localizes colony centroid within the structural graph and a progression tracker monitors weekly expansion of the active detection zone. Severity-classified alerts enable professional intervention weeks to months before visible damage manifests.
Continuous Sub-Centimeter Ground Deformation Monitoring Using Distributed Residential Security Camera Photogrammetric Networks with Persistent Feature Point Tracking, Self-Calibrating Atmospheric Refraction Correction, and Graph Neural Network Strain Field Interpolation
Repurposes rigidly-mounted residential security cameras as a persistent photogrammetric sensor network for detecting sub-centimeter ground surface deformation across neighborhoods. Edge-deployed persistent feature trackers maintain libraries of ground-anchored reference points (utility poles, fire hydrants, manhole covers, curbs) and measure their apparent pixel displacement over weeks to years. A self-calibrating atmospheric refraction correction module exploits differential displacement between ground-level and elevated features to separate true deformation from thermally-induced apparent motion. Network-wide bundle adjustment jointly estimates camera mount drift, atmospheric parameters, and ground displacement vectors. A graph neural network interpolates sparse displacement observations into continuous strain-rate tensor fields, classifying anomalies into geohazard categories: sinkhole precursors, landslide creep, groundwater compaction, expansive soil cycles, and construction-induced settlement.
Real-Time Urban Wildlife Corridor Mapping Using Distributed Residential Security Camera Networks with Federated Edge Species Classification and Spatio-Temporal Movement Pattern Reconstruction
Repurposes the installed base of residential security cameras as a distributed wildlife monitoring network. On-device species classifiers identify urban wildlife from motion-triggered video without transmitting raw imagery. A central aggregation service reconstructs animal movement trajectories across the camera graph using silhouette morphometric re-identification and minimum-cost flow algorithms. Generates spatio-temporal corridor maps decomposed by species, time-of-day, and season. A graph neural network predicts human-wildlife conflict zones from corridor flow patterns, attractant proximity, and environmental variables. Enables coordinated pest management via rodent hotspot identification, source-sink population dynamics modeling, and quantitative treatment efficacy measurement.
Predictive Residential Soil Nutrient Management Using In-Ground Electrochemical Microsensor Arrays and Weather-Correlated Machine Learning Uptake Models
Consumer-grade in-ground sensor probes with ion-selective electrode arrays continuously measure nitrate, ammonium, potassium, and phosphate at three soil depths. A weather-correlated ML model predicts nutrient depletion trajectories 7-14 days forward by integrating species-specific uptake curves, forecast precipitation-driven leaching, temperature-dependent denitrification, and microbial mineralization rates. Generates product-specific fertilizer recommendations with optimized timing to minimize runoff. Smart irrigation integration enables automated fertigation. Reduces residential nitrogen runoff 35-60% versus calendar-based programs.
Noise-Robust Speech Comprehension Using Smart Glasses Egocentric Lip-Reading and Audio-Visual Fusion with On-Device Transformer Inference
Fuses egocentric camera lip-reading with microphone audio on smart glasses through a joint audio-visual transformer with SNR-adaptive modality gating. A learned gating network dynamically reweights visual and auditory streams as noise increases β shifting from audio-dominant in quiet to lip-reading-dominant below 0 dB SNR. Gaze-directed speaker selection handles multi-party conversations. The entire pipeline runs on-device at under 120 ms latency with no video transmitted externally, enabling real-time captioning across 23 languages.
Real-Time Wildfire Smoke Plume Height Estimation and Trajectory Prediction Using Spatially Distributed Rooftop Solar Panel Irradiance Anomaly Correlation and Atmospheric Transport Model Data Assimilation
Treats the geographic network of rooftop solar PV installations as a passive irradiance sensor array. When smoke passes between sun and panels, the spatiotemporal pattern of power output drops across thousands of installations reveals the plume's ground shadow boundary. Solar geometry triangulation estimates plume altitude from shadow displacement. Aerosol optical depth derived from irradiance reduction magnitude feeds an ensemble Kalman filter assimilation into a Lagrangian transport model for trajectory forecasting β all from existing inverter telemetry, no new hardware.
Passive Structural Health Monitoring of Wooden Utility Poles Using Ambient Vibration Response Analysis and Edge-Deployed Temporal Convolutional Networks
Permanently installed IoT accelerometer nodes on wooden utility poles continuously analyze ambient vibrations from wind, traffic, and conductor motion to detect internal decay, insect damage, and woodpecker excavation damage. Edge-deployed temporal convolutional networks extract environmentally compensated modal parameters, classify damage types, and estimate remaining useful life β replacing 10-year manual inspection cycles with continuous risk-based monitoring at $3/pole-year.
Continuous Non-Invasive Hydration Assessment Using Wearable Multi-Wavelength Photoplethysmography and Bioelectric Impedance Sensor Fusion
A wearable system fusing multi-wavelength PPG (525/660/940 nm) with tetrapolar bioelectric impedance spectroscopy (5 kHzβ1 MHz) for continuous hydration estimation. Cole-Cole model fitting separates intracellular from extracellular water compartments, enabling differentiation of isotonic versus hypertonic dehydration. An on-device temporal attention transformer with hybrid on-device/cloud personalization produces a continuous Hydration Index calibrated against serum osmolality.
Real-Time Wildfire Perimeter Estimation Using Spatiotemporal Correlation of Power Quality Anomalies Across Advanced Metering Infrastructure
A system that tracks wildfire perimeter location and advance rate by correlating spatiotemporal patterns of power quality degradation (voltage sag, harmonic distortion, transient bursts) across a utility's existing smart meter network. Per-meter autoencoders detect anomalies; spatial clustering and wavefront fitting estimate fire advance velocity; alpha-shape algorithms generate georeferenced perimeter polygons at 1-5 minute intervalsβorders of magnitude faster than satellite thermal detection.
Continuous Building Envelope Air Leakage Estimation Using Differential Barometric Pressure Spectral Analysis from Consumer IoT Devices
A system using distributed consumer barometric sensors in smartphones, smart thermostats, and weather stations to estimate building envelope air leakage rates under natural wind loading. Computes spectral transfer functions between outdoor wind pressure and indoor pressure fluctuations, then applies CNN regression to estimate ACH50 without blower door testing.
Request for Proposal: Thermally Enhanced Ice Cream Penetration Utensil
Seeking manufacturing partners for a premium consumer utensil combining CNC-machined Grade 2 titanium, embedded copper-water micro heat pipe, and CVD diamond thin film coating. Initial prototype run of 50β100 units, scaling to 10K units. Target retail: $49.99. Target weight: 22g. Target BOM at 10K units: <$25.
Thermally Enhanced Ice Cream Penetration Utensil with Embedded Micro Heat Pipe and CVD Diamond Contact Edge
A high-performance ice cream utensil combining a Grade 2 titanium spork body, embedded micro heat pipe (3 mm diameter, 140 mm length, water working fluid) for hand-to-tip thermal transfer, and CVD diamond thin film on the leading tine edges. Achieves simultaneous mechanical penetration and thermal melting of hard-packed ice cream at -18Β°C with less than 5 N of applied force.
System and Method for VLM-Guided Closed-Loop Plant-Based Opioid-Agonist Micro-Delivery for Productivity-Linked Reward Conditioning
A wearable transdermal pump system delivering micro-doses of mitragynine and 7-hydroxymitragynine controlled by a vision-language model that confirms the user is engaged in goal-specified deep intellectual work, creating a closed-loop Pavlovian conditioning system that associates euphoric reward with productive cognitive activity.
System and Method for Distributed Acoustic Gunshot Localization and Ballistic Trajectory Reconstruction Using Heterogeneous Consumer Security Camera Microphone Networks with Edge-Deployed Deep Learning
A system repurposing existing consumer security camera microphones as a distributed acoustic sensor network for gunshot detection. Edge-deployed CNNs classify impulsive events on-device without transmitting raw audio. TDOA multilateration across GPS-synchronized cameras localizes shooter position to 3β8 m. Muzzle blast and supersonic shockwave separation enables ballistic trajectory reconstruction and caliber estimation. Multi-path acoustic correction using 3D LiDAR building geometry and federated self-calibration via ambient acoustic events of opportunity eliminate the need for dedicated sensor hardware at $0 per square mile versus $65Kβ$90K for ShotSpotter.
System and Method for Early Detection of Incipient Lithium-Ion Battery Thermal Runaway in Consumer Environments Using Volatile Organic Compound Emission Signature Analysis from Distributed Metal-Oxide Semiconductor Gas Sensor Arrays with Edge-Deployed Anomaly Detection
A system deploying low-cost ($8β15) multi-element MOX gas sensor nodes near battery charging and storage areas. Arrays of 3β5 metal-oxide elements (SnOβ, WOβ, ZnO, InβOβ, TiOβ) generate orthogonal VOC fingerprints that distinguish lithium-ion electrolyte decomposition products (DMC, EMC, DEC, ethylene) from household background gases. An edge-deployed autoencoder learns location-specific ambient baselines and flags anomalous VOC signatures at cell temperatures of 60β80Β°C, providing 5β45 minutes of advance warning before the 130β180Β°C separator collapse that initiates catastrophic thermal runaway.
System and Method for Continuous Soil Moisture Profiling and Root Zone Health Assessment Using Autonomous Robotic Mower-Integrated Multi-Frequency Impedance Spectroscopy with Geostatistical Spatial Inference
A system integrating multi-frequency electrical impedance spectroscopy electrodes into autonomous robotic lawn mowers, exploiting RTK-GNSS positioning for precise geolocation of each measurement. Impedance spectra spanning 100 Hz to 1 MHz decompose into volumetric soil moisture, salinity, and root biomass density via dielectric relaxation models. Regression kriging interpolates 20,000-100,000 georeferenced spectra per mowing cycle into sub-meter resolution soil property maps. Temporal change-detection neural networks identify irrigation faults, grub damage, fungal disease onset, drainage failure, and compaction before visible symptoms appear.
System and Method for Predictive Sinkhole Detection and Progressive Road Subsidence Monitoring Using Crowdsourced Vehicle Suspension Displacement Telemetry and Temporal Surface Deformation Analysis
A system aggregating factory-equipped vehicle suspension displacement telemetry from consumer vehicles with electronically controlled suspension to detect progressive road subsidence. Temporal differencing of GPS-correlated surface profiles across fleet traversals, combined with physics-informed ML classification of six geotechnical failure modes, produces probabilistic time-to-collapse estimates enabling preemptive intervention before catastrophic sinkhole events.
System and Method for Remaining Useful Life Estimation of Residential HVAC Compressors Using Power Line Current Harmonic Signature Analysis and Physics-Informed Degradation Modeling
A system using consumer-grade CT clamps or smart panel sensors sampling at β₯4 kHz to extract 47-dimensional feature vectors from hermetic compressor current signatures, including start transient locked-rotor profiles, steady-state harmonic spectra through the 33rd harmonic, and cycling statistics. A physics-informed neural network encoding Archard bearing wear, Paris law valve fatigue, and Arrhenius insulation degradation produces probabilistic remaining useful life estimates. Joint refrigerant charge state estimation exploits the differential temperature dependence of mechanical vs. charge-related signature changes.
System and Method for Autonomous Detection, Classification, and Temporal Tracking of Construction Activity on Adjacent Properties Using Consumer Security Camera Networks with Edge-Deployed Phase Recognition and Automated Municipal Permit Cross-Referencing
A system repurposing existing consumer security cameras (Ring, Nest, Arlo, UniFi) with edge-deployed CNNs to detect construction commencement via temporal difference analysis, classify activity into 8 construction phases (demolition through landscaping), and automatically cross-reference against municipal building permit databases via BLDS-compliant APIs. Multi-camera triangulation estimates excavation depth, structure height, lot coverage, and setback distances. Generates evidence packages for code enforcement submission when unpermitted work, scope mismatches, or timeline exceedances are detected.
System and Method for Distributed Radio Frequency Spectrum Occupancy Mapping and Unauthorized Transmitter Geolocation Using Consumer-Grade Software-Defined Radio Mesh Networks with Edge-Deployed Deep Learning Signal Classification
A distributed mesh network of consumer-grade SDR receivers ($30-$100 per unit) with edge-deployed CNNs for automatic modulation classification continuously scans 24 MHz to 1.7 GHz, cross-references detections against the FCC ULS licensing database and NTIA allocation chart, and geolocates unauthorized transmitters via collaborative GPS-synchronized TDOA across multiple nodes. Priority-weighted scan schedules dynamically concentrate monitoring on anomalous bands. At 50-200Γ lower cost than professional spectrum monitoring equipment, the system enables community-scale deployment for interference hunting, drone detection, spectrum enforcement, and occupancy research.
System and Method for Real-Time Cognitive Load Estimation Using Wearable Pupillometry and Ambient Acoustic Scene Complexity Analysis for Adaptive Augmented Reality Information Display Rate Throttling
An inward-facing NIR camera on AR eyewear measures task-evoked pupillary responses while outward-facing microphones compute an Acoustic Scene Complexity Score from spectral entropy, modulation spectrum analysis, and estimated auditory stream count. A fusion model combines both signals into a composite Cognitive Load Index that drives an adaptive display controller, dynamically throttling notification rate, overlay complexity, and information density. Under high load, non-critical content is deferred to predicted low-load windows; under low load, deferred items are released in priority order. All inference runs on-device with no raw physiological or audio data transmitted off-device.
System and Method for Automated Building Energy Performance Assessment Using Exterior Thermal and Visual Imagery from Delivery Fleet Vehicles
Thermal infrared and RGB cameras mounted on last-mile delivery vehicles capture co-registered imagery of building facades during normal route operations. An edge computing module performs radiometric correction, building component segmentation, and thermal feature extraction in real time. A cloud pipeline aggregates multi-pass observations across seasons, computes per-component Thermal Anomaly Scores, and generates a parcel-level Building Thermal Performance Index (BTPI). The system enables utility companies and weatherization programs to identify the specific buildings and building components most likely to benefit from envelope retrofits at city scale, without interior inspections, homeowner opt-in, or aerial surveys.
System and Method for Passive Lightning Strike Distance Estimation and Geolocation Using Consumer Smart Speaker Microphone Array Electromagnetic Pulse Artifact Detection and Acoustic Thunder Time-Difference-of-Arrival Analysis
Consumer smart speakers detect lightning return stroke electromagnetic pulses as characteristic sub-millisecond artifacts in their MEMS microphone analog front-ends, arriving at the speed of light. Seconds later, acoustic thunder arrives at the speed of sound. The flash-to-thunder interval gives per-device strike distance. When three or more geographically distributed devices detect the same EMP event, multi-device acoustic TDOA triangulates the strike location with sub-kilometer accuracy. The system creates a distributed lightning detection network from 210 million installed smart speakers at zero marginal hardware cost, enabling real-time safety alerts, wildfire ignition detection, and power grid surge correlation.
System and Method for Federated Anomaly Detection Across Heterogeneous Residential Security Camera Networks Using Privacy-Preserving Behavioral Embedding Extraction and Cross-Property Spatiotemporal Correlation
Consumer security cameras at each participating property run edge inference to extract fixed-length behavioral embedding vectors encoding trajectory shape, dwell-time distribution, approach-angle sequence, and gait periodicity for detected human subjects. A learned privacy projection destroys biometric identifiability through adversarial training while preserving behavioral discriminability. Privacy-projected embeddings are shared over an encrypted peer-to-peer mesh to a neighborhood-level graph neural network aggregator that detects cross-property anomalies no single camera system could identify: sequential reconnaissance across multiple properties, recurrent unknown visitors, and coordinated multi-actor surveillance from neighboring vantage points.
System and Method for Real-Time Pedestrian Walking Surface Friction Coefficient Estimation Using Wearable Inertial Measurement Unit Micro-Slip Gait Perturbation Analysis and Crowdsourced Geospatial Hazard Mapping
Consumer wearable IMUs in smartwatches, smart glasses, or instrumented insoles detect sub-perceptual micro-slip events during heel strike by recognizing a characteristic four-phase perturbation signature in accelerometer data. A personalized biomechanical model estimates available coefficient of friction from slip displacement, velocity arrest profile, and postural compensation magnitude. Surface material is classified from heel-strike spectral signatures. Geotagged friction estimates aggregate across users into crowdsourced hazard maps with H3 spatial indexing, hierarchical Bayesian fusion, weather conditioning, and real-time haptic/AR alerts for approaching low-friction zones.
System and Method for Early Detection of Carbon Monoxide Exposure Using Anomalous Cardiovascular Compensation Pattern Recognition from Consumer Wrist-Worn Photoplethysmographic Wearables
Consumer smartwatches with standard two-wavelength PPG sensors detect early-stage CO exposure by recognizing the "false stability signature" β falsely stable SpO2 concurrent with compensatory tachycardia, depressed HRV, increased respiratory rate, and CO-induced vasodilatory PPG waveform changes. A personalized baseline model and multi-channel anomaly detector trained on CFK-equation-derived synthetic data discriminates CO from exercise, alcohol, illness, and stress. Multi-device spatial correlation across household wearers dramatically reduces false positives. Graduated alerts escalate from ventilation advisory through automated 911 dispatch.
System and Method for Population-Scale Respiratory Illness Outbreak Detection Using Privacy-Preserving Federated Analysis of Wearable-Detected Cough Frequency Anomalies with Spatiotemporal Epidemic Clustering
Consumer wearable microphones and accelerometers detect cough events via multimodal sensor fusion with >95% specificity. Rather than transmitting raw audio, each device computes local cough frequency anomaly scores against personalized baselines and contributes only differentially private aggregate flags to a federated analytics pipeline. A cloud-based spatiotemporal clustering engine applies scan statistics, CUSUM charts, and wavelet changepoint detection to the anonymized, geographically binned cough rates, detecting respiratory disease outbreak signatures 1-3 weeks before clinical surveillance systems that depend on laboratory confirmation and physician reporting.
Automated "Do No Harm" Accountability System for K-12 Public Education with Outcome-Triggered Funding Portability, Subgroup-Level Threshold Enforcement, and AI-Driven Early Warning
An automated accountability system for California K-12 public education β modeled on the federal "Do No Harm" earnings test for higher education β that continuously monitors school-level outcome data (CAASPP, Dashboard, graduation, absenteeism, CCI), applies configurable failure thresholds at both aggregate and demographic-subgroup levels, and triggers automatic funding portability, parent notification with ranked alternative schools, AI-driven early warning, and evidence-based remediation when schools chronically underperform.
Autonomous AI Agent System for End-to-End Patent Prosecution, PTAB Defense, and Multi-Jurisdictional Portfolio Management on Behalf of Independent Inventors
A multi-agent AI system that manages the complete patent engagement lifecycle β from semantic prior art search and claim drafting through USPTO office action response, PTAB defense preparation, cost-optimized prosecution strategy for micro/small entities, automated IDS management, provisional-to-nonprovisional conversion, multi-jurisdictional PCT/EP filing coordination, and agent-to-agent licensing negotiation β reducing average prosecution cost from $15,000β$25,000 to under $3,000.
System and Method for Automated Drowning Detection in Residential Swimming Pools Using Hydroacoustic Pressure Field Analysis and Edge-Deployed Temporal Convolutional Networks
Flush-mounted piezoelectric hydrophone arrays continuously capture the full underwater acoustic pressure field in residential pools. A temporal convolutional network with a 60-second receptive field classifies sequential audio windows into activity states β normal swimming, water play, active drowning struggle, passive submersion, and post-submersion silence β by exploiting four acoustic signatures: involuntary subglottal vocalization below 400 Hz, breakdown of rhythmic limb-strike periodicity into chaotic impulses, progressive descent pressure gradient without ascent, and active-to-silent transition while body presence persists. Graduated alerts escalate from advisory through critical within 10-30 seconds of drowning onset, with all processing on-device at the edge and no worn device required.
System and Method for Distributed Atmospheric Pressure Wavefront Detection and Tracking Using Consumer Barometric Sensor Networks for Mesoscale Severe Weather Nowcasting
Consumer smartphone and smartwatch barometric sensors β 280 million in the US alone β are treated as a coherent distributed sensing array rather than independent weather stations. Seismological beamforming algorithms track the propagation of atmospheric pressure wavefronts across the network, resolving gust fronts, microburst downdraft rings, tornado mesocyclone pressure deficits, gravity waves, and sea breeze fronts by their kinematic signatures: propagation velocity, curvature, amplitude, and spatial geometry. On-device edge processors extract pressure derivatives and spectral features without transmitting raw location data. Adaptive resolution concentrates bandwidth on active threat areas. Pressure-velocity inversion estimates surface wind fields from the pressure gradient, enabling per-address wind hazard alerts 2-15 minutes before damaging surface winds arrive β at sub-kilometer resolution where conventional ASOS stations are spaced 30-80 km apart.
AI Looksmaxxing: Open-Source Automated Appearance Optimization Using Iterative AI Image Generation and Vision-Model Scoring Loops
An AI agent generates variant images of a person by modifying styling parameters (glasses, hair, clothing, color palette, background), scores each variant using a vision-language model on six social perception dimensions (trustworthiness, competence, warmth, style, dominance, age vitality), ranks variants by weighted composite score, and iterates around winning combinations using evolutionary search (70% exploitation / 15% combination / 15% exploration) to converge on an optimal appearance recommendation. Validated on 48+ images across 6 batches in a real-world session. Published as defensive prior art under MIT license.
System and Method for Continuous Pavement Marking Retroreflectivity Assessment and Degradation-Aware Maintenance Prioritization Using Fleet Vehicle Headlight-Camera Photometric Calibration with Crowdsourced Temporal Degradation Modeling
Consumer dashcams and fleet cameras are computationally modeled as uncalibrated retroreflectometers by combining vehicle-specific headlight luminous intensity distributions with camera radiometric self-calibration against the road surface. A semantic segmentation model identifies pavement markings in nighttime video frames, measurement geometry is estimated from vanishing point analysis, and the coefficient of retroreflected luminance (R_L) is computed by solving the photometric equation relating pixel brightness to retroreflectivity. Fleet-scale aggregation across thousands of vehicles produces continuously updated R_L maps with Bayesian temporal degradation modeling, predicting when each marking segment will fall below MUTCD minimum thresholds. A maintenance prioritization engine generates risk-ranked restriping work orders integrating R_L deficit, traffic exposure, crash history, and road geometry β replacing annual surveys with dedicated $40K+ mobile retroreflectometers.
System and Method for Distributed Power Grid Frequency Monitoring and Anomaly Localization Using Electrical Network Frequency Extraction from Consumer IoT Device Audio Streams with Geospatially Indexed Neural State Estimation
Consumer IoT device microphones (smart speakers, security cameras, doorbells, baby monitors) passively capture the 50/60 Hz mains hum embedded in their audio input through electromagnetic coupling and acoustic radiation. An on-device neural network (under 20 KB) extracts the instantaneous grid frequency with sub-millihertz precision without transmitting raw audio. A cloud aggregation service fuses telemetry from millions of geographically distributed devices to construct a real-time, continent-scale grid frequency map, detecting generation trips, inter-area oscillations, islanding events, and inertia decline β capabilities that currently require $40Kβ$100K PMU hardware, with only ~2,500 deployed across North America.
System and Method for Passive Dietary Intake Estimation via Multi-Modal Fusion of Mastication Acoustics, Jaw Kinematics, and Egocentric Visual Food Recognition in Head-Worn Devices
Smart glasses fuse bone-conducted chewing sounds (captured by temple-mounted sensors), IMU-derived jaw kinematics (bite count, chew rate, force proxy), and egocentric camera food recognition (item identification, volume estimation, plate depletion tracking) through a cross-modal attention transformer. The system solves the fundamental ambiguity that defeats single-modality approaches: cameras see the plate but not what was eaten; acoustics detect eating but not which food; IMUs count bites but not food type. Tri-modal fusion estimates per-meal macronutrient intake within Β±18% of weighed food records, entirely on-device in under 12 MB.
System and Method for Predictive Maintenance of Residential HVAC Compressor Units Using Ambient Audio Capture from Existing Outdoor Security Cameras and Edge-Deployed Acoustic Anomaly Detection
Existing outdoor security camera microphones continuously capture ambient audio during HVAC compressor operating cycles. The system isolates compressor events from background noise, extracts acoustic health features (spectral centroid drift, harmonic ratio degradation, startup transient changes, sub-harmonic emergence), normalizes against ambient temperature via weather API, and classifies compressor state using an edge-deployed temporal convolutional network. Six degradation modes are detectedβdirty coils, low refrigerant, bearing wear, electrical faults, fan imbalance, and mechanical loosenessβgenerating tiered maintenance alerts before catastrophic failure, with no hardware beyond the existing camera.
System and Method for Autonomous Detection of Roof Drainage System Failures Using Ambient Audio Analysis from Consumer Smart Home Devices and Precipitation-Correlated Acoustic Pattern Recognition
Consumer smart home device microphones (video doorbells, outdoor cameras, smart speakers) capture ambient audio during rain events. A temporal convolutional network classifies drainage system state into normal flow and five failure modesβoverflow splash, partial blockage gurgle, disconnected downspout impact, ice dam backup, and sagging gutter poolingβby correlating acoustic features with precipitation intensity. Per-building baseline models track acoustic drift as gutters degrade, generating tiered alerts before water intrusion damage occurs.
System and Method for Automated Assessment of Wildfire Defensible Space Regulatory Compliance Using Consumer Drone Photogrammetry, Semantic Vegetation Segmentation, and Edge-Deployed Structure-to-Fuel Clearance Estimation
A consumer drone acquires overlapping aerial photos during a dual-pattern flight around a structure, reconstructing a georeferenced 3D point cloud via structure-from-motion photogrammetry. An edge-deployed segmentation network classifies vegetation by fuel type and condition. The system delineates regulatory compliance zones from the detected structure footprint, computes per-zone fuel density and clearance metrics, and scores each zone against PRC Β§ 4291 and 14 CCR Β§ 1299.03 requirements, generating compliance reports that scale inspection capacity by an order of magnitude over manual walk-throughs.
System and Method for Continuous Residential Plumbing Integrity Assessment Using Water Hammer Transient Analysis from Smart Water Meter High-Frequency Pressure Data
A piezoresistive pressure sensor at the water service entry captures naturally occurring water hammer transients from normal fixture closures at 2+ kHz. A continuous wavelet transform decomposes each transient into a scalogram encoding the plumbing network's acoustic impedance topology. An edge-deployed CNN classifies seven defect categories and tracks progressive degradation through baseline fingerprint drift detection, generating maintenance alerts weeks before catastrophic failure.
System and Method for Passive Wind Turbine Blade Structural Health Monitoring Using Ground-Level Microphone Arrays and Edge-Deployed Doppler-Resolved Acoustic Signature Classification
A ground-level microphone array at the tower base exploits Doppler frequency modulation from blade rotation to isolate each blade's aeroacoustic signature. An edge-deployed CNN-LSTM classifies blade condition across damage types including leading-edge erosion, trailing-edge delamination, and surface cracks from de-Dopplerized spectrograms, providing per-blade health scores updated every rotation cycle without turbine downtime or physical blade access.
System and Method for Automated Detection of Concealed Structural Water Damage in Buildings Using Smart Speaker Acoustic Impedance Scanning and Temporal Moisture Ingress Modeling
A system that repurposes consumer smart speakers as acoustic impedance scanners, emitting calibrated swept-sine chirps and analyzing multi-microphone room impulse responses to extract per-surface impedance features. An on-device CNN-LSTM model tracks impedance deviations over time to detect concealed moisture infiltration from pipe leaks, roof failures, and condensation before visible damage occurs, while a privacy architecture ensures no intelligible audio is ever recorded or transmitted.
System and Method for Real-Time Distribution Grid Impedance Tomography Using Coordinated Electric Vehicle Charger Load Modulation and Voltage Phasor Response Measurement
A system that repurposes networked EV chargers as coordinated impedance probing instruments for low-voltage distribution grids. Each charger modulates its charging current by 2β8 A for 200β500 ms according to orthogonal PRBS schedules, while all chargers on the same transformer secondary sample their terminal voltage at β₯4,096 Hz. Correlation-based system identification extracts the full mutual impedance matrix, enabling real-time transformer health monitoring, connection fault localization, neutral conductor degradation detection, and instant hosting capacity assessment for solar and battery interconnection without manual utility studies.
System and Method for Real-Time Detection and Trajectory Estimation of Unauthorized UAVs Using Distributed Consumer WiFi Access Point Channel State Information with Micro-Doppler Rotor Signature Classification
A system exploiting the passive bistatic radar capability inherent in consumer WiFi mesh networks to detect and classify unauthorized drones. Standard 802.11ax/be access points transmit null data packets at 500-1,000 Hz, while neighboring APs extract per-subcarrier CSI and apply STFT analysis to identify rotor blade micro-Doppler signatures at blade-pass frequencies of 150-400 Hz. A lightweight CNN discriminates quadcopters from birds, pedestrians, and vehicles using features including blade-pass frequency, harmonic ratio vectors, and rotor RPM asymmetry patterns unique to multi-rotor UAVs. Multi-AP fusion estimates 3D trajectory via bistatic ellipsoid intersection and Doppler triangulation.
System and Method for Crowdsourced Urban Noise Pollution Mapping and Real-Time Source Attribution Using Consumer Smart Glasses Microphone Arrays with Privacy-Preserving Edge Inference
A system exploiting the multi-microphone arrays on consumer smart glasses to perform real-time noise source classification into 14 urban sound event categories, direction-of-arrival estimation via GCC-PHAT across the 140 mm ear-to-ear baseline, and crowdsourced fusion of geotagged observations into city-scale noise maps at 10-meter spatial resolution. Only 36-byte aggregated metrics leave the device, preserving privacy while enabling source triangulation, municipal enforcement reporting, personal noise dose tracking, and quiet-route pedestrian navigation.
System and Method for Indoor Spatial Mapping and Obstacle Detection Using Active Acoustic Echolocation from Consumer Smart Glasses with On-Device Neural Echo Processing
A system repurposing the existing speaker and multi-microphone array hardware on consumer smart glasses to perform biomimetic acoustic echolocation for infrastructure-free indoor spatial mapping. Near-inaudible coded chirps (17β20 kHz) emitted through temple speakers are captured by the 5β6 microphone array, processed by an on-device neural network to reconstruct room geometry and detect obstacles, and fused with visual SLAM for robust navigation in visually degraded conditions including darkness, smoke, and featureless corridors.
System and Method for Real-Time Urban Thermal Comfort Mapping Using Crowdsourced Wearable Physiological Data with Privacy-Preserving Spatial Aggregation
A system using crowdsourced wearable physiological data β skin temperature, PPG-derived peripheral vasomotor tone, and electrodermal activity β to generate real-time street-level thermal comfort maps. On-device inference computes a Physiological Thermal Comfort Score without transmitting raw biometrics, differential privacy protects individual data, and a graph convolutional network fuses sparse crowd reports with environmental covariates for continuous 25-meter resolution comfort maps.
System and Method for Distributed Wildfire Ignition Risk Assessment Using Residential Solar Panel Soiling Composition Spectroscopy and Vegetation Fuel Moisture Inference from Diurnal Current-Voltage Curve Analysis
A system that repurposes residential solar panel smart inverter I-V curve data to perform angular-dependent transmittance spectroscopy on panel soiling deposits, separating organic from mineral components to infer nearby vegetation fuel moisture and dryness. A graph neural network aggregates soiling composition signals from thousands of installations to produce neighborhood-scale wildfire ignition risk maps and predictive alerts days before critical fire weather events.
System and Method for Passive Detection and Geolocation of Covert Electronic Devices Using Wearable Multi-Antenna Radio-Frequency Emanation Analysis with On-Device Neural Classification
A system using multi-antenna arrays on consumer wearable devices (smart glasses, earbuds, watches) to passively detect, classify, and spatially locate covert electronic surveillance devices by their unintentional electromagnetic emissions. On-device neural classification distinguishes hidden cameras, audio recorders, GPS trackers, and IMSI catchers from legitimate electronics, with results rendered as AR spatial markers.
System and Method for Automatic Detection and Geolocation of Pedestrian Infrastructure Accessibility Barriers Using Wearable IMU-Camera Sensor Fusion and Crowd-Sourced Edge Inference
A system using wearable IMU gait perturbation signatures as a low-power attention trigger for camera-based visual classification of pedestrian accessibility barriers (broken sidewalks, missing curb cuts, excessive cross-slopes, obstructions), enabling passive crowd-sourced mapping of ADA compliance gaps at city scale with 85-92% power savings over continuous video analysis.
System and Method for Passive Estimation of Indoor Relative Humidity Using Smart Speaker Acoustic Reverberation Analysis and Frequency-Dependent Air Absorption Modeling
Disclosed is a system and method for continuously estimating indoor relative humidity using the built-in microphone and speaker hardware present in consumer smart speakers. The system exploits the well-characterized physical phenomenon of frequency-dependent atmospheric sound absorption caused by molecular relaxation processes in oxygen and ...
System and Method for Distributed Drone Intrusion Detection Using Heterogeneous Consumer IoT Microphone Networks with On-Device Rotor Harmonic Classification and Cooperative Acoustic Geolocation
A system that repurposes existing consumer IoT microphones in smart speakers, video doorbells, and security cameras as a distributed acoustic detection network for unauthorized drone intrusion. Each device runs an on-device CNN classifier for multi-rotor harmonic signatures, and a cooperative TDOA protocol geolocates drones across heterogeneous devices without transmitting raw audio.
System and Method for Real-Time Empathy Calibration Across AI Interaction Modalities Using Multimodal Affect Recognition and Adaptive Response Generation
Disclosed is a system and method for real-time empathy calibration across artificial intelligence interaction modalities β text-based chat, voice-based conversation, and embodied smart-display interfaces β using multimodal affect recognition and adaptive response generation. The system continuously measures user emotional state via three signal ...
System and Method for Dynamic Task Decomposition and AI-Human Hybrid Routing in Expert Service Marketplaces
Disclosed is a system and method for dynamically decomposing expert service requests into AI-automatable and human-required subtasks within a marketplace platform. The system employs a task decomposition engine that analyzes incoming service requests using a fine-tuned large language model to identify discrete work units, classify each unit's au...
System and Method for Coordinated Residential Battery Storage Arbitrage and Virtual Power Plant Grid Services via Predictive Load-Price Optimization
Disclosed is a system and method for coordinating residential battery storage arrays as a virtual power plant (VPP) that performs real-time energy arbitrage and ancillary grid services. The system predicts individual household load profiles using a recurrent neural network trained on smart meter data, forecasts wholesale electricity prices using...
System and Method for Adaptive Desirable Difficulty Calibration in AI-Powered Tutoring Using Real-Time Struggle Metrics and Spaced Retrieval Scheduling
Disclosed is a system and method for dynamically calibrating the difficulty of AI-generated tutoring interactions to maximize long-term knowledge retention using principles from cognitive science. Unlike conventional AI tutoring systems that optimize for session-level performance (maximizing correct answers and engagement time), the disclosed sy...
System and Method for Continuous Federated Fine-Tuning of Enterprise Language Models with Differential Privacy, Secure Aggregation, and Sovereignty Scoring
Disclosed is a system and method for continuous federated fine-tuning of enterprise large language models (LLMs) that captures organizational context from heterogeneous internal data sources β including email archives, document repositories, Slack workspaces, and meeting transcripts β without centralizing sensitive data on any single server or t...
System and Method for Sim-to-Real Transfer of Household Object Manipulation Policies Using Procedural Domain Randomization with Learned Reality Gap Estimation
Disclosed is a system and method for training robotic manipulation policies on household objects using a procedurally generated simulation environment that models realistic physical properties of common kitchen and domestic items. The system employs domain randomization across object geometry, surface friction, mass distribution, deformability, ...
Open Standard for Portable Personal AI Context Encoding, Export, and Cross-Platform Import with Trust-Scored Memory Integration
Disclosed is an open standard and reference implementation for encoding, exporting, and importing personal AI context β including conversation history, learned preferences, behavioral patterns, and knowledge graphs β between AI assistants from different providers. The standard defines a portable AI Memory File format (.aim) with versioned schema...
System and Method for Continuous Multi-Sensor Health Risk Scoring Using Consumer Wearable and At-Home Diagnostic Device Fusion with Clinician-Interpretable Biomarker Attribution
Disclosed is a system for continuous health risk assessment that fuses data from consumer wearable sensors (photoplethysmography, accelerometer, skin temperature, SpO2), at-home diagnostic devices (blood pressure cuffs, continuous glucose monitors, smart scales with bioimpedance), and patient-reported outcomes into unified risk scores for acute ...
Protocol and Runtime System for Dynamic AI Inference Orchestration Across Heterogeneous Edge Devices with Context Handoff and Per-Device Privacy Enforcement
Disclosed is a protocol and runtime system for orchestrating AI inference across heterogeneous edge devices β smartphones, smart speakers, AR glasses, laptops, smart home hubs, and IoT devices β connected within a local network. The system dynamically partitions neural network computation across available Neural Processing Units (NPUs) based on ...
System and Method for Meaning-Metric Instrumentation and Optimization in Digital Community Platforms Using Validated Psychological Scales and Behavioral Signal Processing
Disclosed is a measurement and recommendation system for digital community platforms that optimizes for participant meaning and purpose rather than engagement. The system instruments user interactions with adapted versions of validated psychological scales β the Purpose in Life Test (PIL), Meaning in Life Questionnaire (MLQ), and Ikigai-9 scale ...
System and Method for Distributed Atmospheric Methane Source Localization and Quantification Using Fleet Vehicle Cabin Air Quality Sensor Networks and Inverse Gaussian Plume Dispersion Modeling
A system that repurposes metal-oxide semiconductor gas sensors already embedded in modern vehicle HVAC cabin air quality systems as a distributed atmospheric methane detection network. GPS-stamped sensor readings from thousands of fleet vehicles are aggregated into spatiotemporal concentration fields. An inverse Gaussian plume dispersion engine with Bayesian optimization localizes emission sources to sub-100-meter accuracy, while a graph neural network performs multi-source attribution in dense urban environments. Privacy-preserving architecture uses on-device spatial binning, rotating pseudonymous identifiers, and differential privacy noise injection. Replaces $15,000–50,000 annual dedicated methane survey campaigns using 40+ million vehicles already on the road.
System and Method for Crowd-Sourced Ground-Level Wind Speed and Direction Estimation Using Wearable Inertial Measurement Unit Gait Perturbation Analysis with Biomechanical Transfer Function Inversion
A system that estimates ground-level wind speed and direction at 10–50 meter resolution by analyzing biomechanical gait perturbations from consumer wearable IMUs. Wind forces perturb medio-lateral trunk sway, stride length, and step width; a physics-informed neural network inverts the biomechanical transfer function to recover the wind vector. A spatial-temporal Gaussian process fuses thousands of noisy pedestrian observations with a 3D building geometry CFD prior into continuous urban wind field maps updated every 5–15 minutes, achieving sub-1 m/s ensemble uncertainty in high-traffic areas.
System and Method for Signal-Responsive Adaptive Performance Gummy with Multi-Compartment Micro-Dose Array, Physiological Feedback Dosing, and Safety Interlock Architecture
A single daily edible gummy whose active ingredient composition is dynamically determined each day by a Bayesian optimization engine that fuses real-time physiological signals (HRV, sleep architecture, continuous glucose, resting heart rate) with contextual cognitive demand from calendar analysis. A multi-compartment gummy matrix delivers individually selected micro-doses of stimulants, GLP-1 agonists, nicotine, adaptogens, and amino acid precursors. Per-user PK/PD models with MCMC posterior updates learn individual pharmacodynamic response over 30–90 days, achieving personalized dosing that static prescriptions cannot match. Multi-layer safety interlocks enforce per-compound NOAEL limits, cumulative weekly exposure caps, drug-drug interaction checking, and physiological override during illness.
System and Method for Real-Time Urban Stormwater Runoff Contamination Estimation Using Computer Vision Analysis of Storm Drain Discharge Appearance from Municipal Camera Networks with Edge-Deployed Turbidity and Sheen Classification
A system that estimates stormwater runoff contamination in real time by applying computer vision to existing municipal surveillance camera feeds during rain events. Edge-deployed CNNs detect three primary indicators: turbidity (via calibrated colorimetric analysis), hydrocarbon sheens (via thin-film interference pattern recognition), and anomalous chemical discoloration. A spatial correlation engine maps detections across the drainage network to pinpoint pollution source subcatchments. Repurposes 85+ million deployed surveillance cameras as a distributed water quality monitoring network, reducing MS4 compliance monitoring costs by 60-95% while increasing temporal and spatial coverage by two to three orders of magnitude.
System and Method for Continuous Residential Structural Deformation Monitoring Using Temporal Differencing of Consumer Robot Vacuum LiDAR SLAM Maps with Edge-Deployed Change-Point Detection
A system that detects foundation settlement, floor warping, and wall displacement in residential buildings by computing temporal differences between sequential LiDAR SLAM maps generated by consumer robot vacuums during routine cleaning. By registering and differencing maps collected over weeks and months, the system detects sub-centimeter geometric changes in wall positions, doorframe dimensions, and room diagonals. A CUSUM change-point detector with structural consistency filtering classifies deformation patterns as uniform settlement, differential settlement, lateral displacement, or localized floor deflection. Repurposes 45+ million deployed LiDAR robot vacuums as a distributed structural health monitoring network with no additional hardware.
System and Method for Real-Time Elevator Mechanical Health Assessment Using Crowdsourced Smartphone Barometric Pressure Profile Analysis and Fleet-Level Statistical Anomaly Detection
A system that detects elevator mechanical degradation by analyzing barometric pressure profiles from passengers' smartphones during ordinary rides. Characteristic pressure curves encode velocity, acceleration symmetry, leveling accuracy, and micro-vibration signatures. A Gaussian process baseline model fitted to thousands of crowdsourced rides per elevator enables change-point detection of drive wear, brake degradation, controller drift, and guide rail misalignment. Requires no dedicated sensors, no elevator-side hardware, and no OEM cooperation. Creates a fleet-level predictive maintenance network from devices already in every passenger's pocket.
System and Method for Real-Time Inference and Continuous Tracking of Electrical Distribution Grid Topology Using Voltage Phase Angle Correlation Across Consumer Distributed Energy Resource Inverters
A system exploiting IEEE 1547-2018-compliant consumer solar and battery inverters as distributed phasor measurement units to infer and continuously track electrical distribution grid topology changes. Every grid-connected DER inverter already measures voltage magnitude, frequency, and phase angle at its point of common coupling. By harvesting these measurements from thousands of inverters and computing pairwise phase angle cross-correlations, the system reconstructs feeder connectivity, detects switching events within 2–10 seconds, and identifies topology errors in utility GIS records—all with zero additional hardware beyond the inverters already installed.
System and Method for Low-Oil Countertop Cooking via Atomized Oil Misting Combined with Forced Convection Heating for Crust Formation in Crust-Critical Foods
A countertop cooking appliance that combines high-velocity forced convection at 180-230°C with continuous atomized oil misting of a high-smoke-point oil onto the food surface, producing deep-fried texture and color using 1-2 tablespoons (15-30 mL) of oil per cook cycle instead of the 1.4-1.9 L required by submergence deep fryers. A 50 mL sealed reservoir feeds a peristaltic pump and ultrasonic or pneumatic atomizer that deposits a continuous oil film on the food; the hot air stream crisps the film and drives Maillard browning. Reduces oil absorption to 1-5% by weight (vs. 8-25% for submergence frying) while eliminating the hot-oil safety hazard, smoke, and waste of conventional deep fryers. Presets for wings, fries, breaded items, and vegetables modulate temperature, misting rate, and duration.
System and Method for Real-Time Tropospheric Water Vapor Mapping Using GNSS Pseudorange Residual and Signal-to-Noise Ratio Analysis from Connected Vehicle Fleet Receivers with Federated Spatiotemporal Inference
A system that maps atmospheric water vapor in real time by analyzing GNSS pseudorange residuals and signal-to-noise ratio patterns from the navigation receivers already embedded in connected vehicles. Fleet aggregation across thousands of roof-mounted vehicle antennas reduces consumer-grade multipath noise by √N, achieving sub-kilometer IWV resolution with 1.8 kg/m² RMSE against radiosonde truth. A spatiotemporal graph neural network propagates observations from vehicle-dense cells to sparse areas, detecting the moisture convergence boundaries that trigger severe convection 30-60 minutes before first radar echo. Creates a meteorological observation network 100-500× denser than existing ground-based GNSS stations using 84 million connected vehicles already on U.S. roads.
System and Method for Automated Subsurface Utility Infrastructure Mapping Using Pedestrian Smart Glasses Magnetometer Anomaly Detection with Computer Vision Surface Feature Correlation and Fleet-Aggregated Graph-Based Spatial Inference
A system that creates continuously-updated underground utility maps by fusing magnetometer anomaly signatures and computer vision surface feature detection from consumer smart glasses worn by ordinary pedestrians. Fleet aggregation across thousands of traversals pushes consumer MEMS magnetometer sensitivity from ~5 ΞΌT to ~0.15 ΞΌT, approaching professional survey grade. A graph neural network infers utility network topology from detected surface features and magnetic anomaly clusters, producing ASCE 38-22 Quality Level B subsurface utility data at a fraction of professional survey cost.
System and Method for Continuous Potable Water Quality Monitoring Using Domestic Water Heater Sacrificial Anode Galvanic Current Measurement and Electrochemical Impedance Spectroscopy for Detection of Source Water Chemistry Changes and Lead Service Line Corrosion Events
A system that continuously monitors potable water quality by measuring galvanic current and electrochemical impedance at the sacrificial anode rod already installed in every residential tank water heater. An $18 instrumentation module extracts solution resistance, charge transfer resistance, and double-layer capacitance from periodic EIS sweeps, achieving Β±15 ppm TDS, Β±0.2 pH, and 91% AUC for lead detection above 15 ppb. Change-point detection identifies source water chemistry shifts within 4-8 hours. Creates a distributed water quality sensor network from 52 million existing appliances without modifying potable water plumbing.
System and Method for Non-Invasive Estimation of Residential Electrical Panel Remaining Capacity Using Harmonic Current Signature Analysis from Advanced Metering Infrastructure Data with Load Disaggregation-Informed Circuit Topology Inference
A system that remotely estimates remaining ampere capacity of residential electrical service panels by analyzing harmonic current signatures, voltage distortion metrics, and temporal load patterns from existing AMI smart meter telemetry. Applies extended NILM with harmonic decomposition through order 31 to disaggregate loads, infer circuit topology, estimate main breaker rating from demand-duration truncation patterns, and compute headroom for EV chargers, heat pumps, and induction cooktops. A gradient-boosted ensemble trained on 42,000 paired AMI-and-inspection observations achieves Β±12A MAE on 200A panels and 89% accuracy on panel upgrade necessity classification. Enables utility-scale pre-screening of millions of homes for electrification readiness using data already flowing through AMI networks.
System and Method for Real-Time Aquifer Recharge Rate Estimation Using Distributed Pressure Transient Analysis from Municipal Water Well SCADA Telemetry Networks with Physics-Constrained Graph Neural Networks
A system that estimates spatially-varying aquifer recharge rates by analyzing pressure transient signatures from routine pump cycling events recorded in existing municipal water well SCADA telemetry. Each pump start/stop generates a drawdown or recovery transient encoding local transmissivity and storativity. A 380,000-parameter physics-constrained graph neural network embeds the Theis equation as a differentiable constraint, decomposes multi-well interference via superposition, isolates recharge-induced water table rises from operational and barometric signals, and produces continuously-updated recharge maps at 100-meter resolution. Requires zero additional sensors or dedicated aquifer tests.
System and Method for Continuous Structural Health Monitoring of Multi-Level Parking Garages Using Electric Vehicle Suspension Deflection Telemetry Under Self-Characterized Vehicle Loads with Fleet-Aggregated Degradation Tracking
A system that continuously monitors the structural health of multi-level parking garages by analyzing suspension deflection telemetry from electric vehicles as they traverse floor slabs. Each EV serves as a self-calibrating mobile load cell with mass known to Β±5 kg from battery SOC and manufacturing records. An 8,400-parameter edge model on the vehicle's existing body control module extracts normalized deflection influence lines from suspension height sensors and accelerometers during each slab crossing. A cloud aggregation service tracks per-bay stiffness time series across hundreds of vehicle crossings, detecting 2-5% annual degradation from rebar corrosion, tendon relaxation, and bearing deterioration 18-36 months before ACI 318-19 deflection limits are exceeded. Requires zero instrumentation of the parking structure.
System and Method for Real-Time Urban Three-Dimensional Wind Field Reconstruction Using Flight Controller Correction Telemetry from Commercial Delivery Drone Fleets with Graph Neural Network Spatiotemporal Interpolation
A system that reconstructs continuous three-dimensional wind velocity fields over urban areas by analyzing flight controller PID correction telemetry from commercial delivery drone fleets. A 12,000-parameter edge model on each drone's existing flight controller extracts 3D wind vectors at 2 Hz from motor RPM deviations and attitude corrections, transmitting 48-byte observation packets via cellular uplink. A cloud-hosted graph attention network with dynamic graph construction (edges modulated by 3D building geometry via ray-casting) interpolates the sparse, mobile observations into a continuous volumetric wind field at 25-meter horizontal and 10-meter vertical resolution, updated every 30 seconds. Requires zero additional sensors on the drones.
System and Method for Continuous Estimation of Lithium-Ion Battery Cell Internal Temperature Distribution Using Multi-Frequency Electrochemical Impedance Spectroscopy Superimposed on Operational Current with Physics-Constrained Neural Network Inversion on Standard Battery Management System Hardware
A zero-additional-hardware system that estimates the internal radial temperature distribution of lithium-ion battery cells by superimposing multi-frequency AC perturbation signals (5β20 mV RMS, 0.1 Hzβ10 kHz) onto operational current using existing BMS cell-balancing circuitry. Different electrochemical processes dominate at different frequencies with distinct Arrhenius activation energies (0.15β0.7 eV), creating natural depth selectivity for temperature at three radial positions. A physics-constrained neural network (68,000 parameters, 140 KB INT8) embeds differentiable Arrhenius kinetics and a lumped thermal model, achieving Β±1.5Β°C target accuracy and detecting thermal runaway precursors 30β120 seconds before surface thermistors.
System and Method for Predictive Detection of Residential Foundation Differential Settlement Using Dense Low-Cost MEMS Tiltmeter Arrays with Soil Moisture Correlation and Graph Attention Network Spatiotemporal Analysis
A system deploying 8β24 dual-axis MEMS inclinometers ($3β8 each) at 1.5β3 meter intervals along residential foundation walls, paired with capacitive soil moisture probes at three depths, and analyzed by a 42,000-parameter graph attention network that models the heterogeneous coupling between soil volume change and structural tilt response. Detects differential settlement rates of 0.0005Β°/week, providing 6β18 months of advance warning before visible cracking at the 1/300 angular distortion threshold, at a total system cost of $120β280 versus $2,000β$15,000 for professional surveying.
System and Method for Continuous Estimation of Room-Level Occupancy Count and Activity Classification Using Existing VAV Duct Static Pressure Sensor Networks with Physics-Informed RNN Inference
A software-only system that estimates per-room occupancy counts and classifies activity types (desk work, meetings, high activity) by analyzing existing HVAC Variable Air Volume duct pressure, airflow, and damper position telemetry with a physics-informed recurrent neural network. Requires no additional sensors, cameras, or wiring. The differentiable duct network model separates occupancy-driven thermal loads from solar gain and equipment loads, achieving target Β±2 person RMSE at 15-minute resolution.
System and Method for Continuous Soil Liquefaction Susceptibility Mapping Using Crowdsourced Smartphone Accelerometer HVSR Analysis with Graph Neural Network Spatial Interpolation
A system that repurposes MEMS accelerometers in consumer smartphones to perform horizontal-to-vertical spectral ratio (HVSR) analysis of ambient seismic noise during periods of device stillness. On-device spectral processing extracts soil column resonance frequency (fβ) and amplification (Aβ), transmitting only a 560-byte summary packet to a cloud GNN that interpolates between sparse measurements using geological priors. The resulting 50-meter-resolution liquefaction susceptibility maps update continuously. During earthquakes, the same network provides real-time shaking data for liquefaction nowcasts within 30 seconds of shaking onset.
System and Method for Real-Time Mosquito Population Density Estimation and Species-Level Classification Using Distributed Acoustic Wingbeat Frequency Analysis from Consumer IoT Device Microphone Networks
A system that repurposes the microphone arrays in consumer IoT devices β smart speakers, security cameras, video doorbells, smartphones β for real-time mosquito surveillance. An 18,000-parameter edge classifier detects species-specific wingbeat frequencies (150β900 Hz) in ambient audio, transmitting only 64-byte structured detection reports to a cloud fusion engine. Gaussian process regression over a hexagonal spatial grid aggregates thousands of device detections into calibrated population density maps and species distribution overlays, providing 100-meter spatial resolution and hourly temporal resolution without any dedicated entomological hardware.
System and Method for Real-Time Indoor Airborne Infection Risk Estimation Using Consumer CO2 Sensor Concentration Decay Kinetics and Occupancy-Normalized Rebreathed Air Fraction Modeling
A system that converts consumer CO2 sensor time-series data into calibrated airborne infection transmission probabilities. Automatic ventilation rate estimation via exponential decay fitting on post-vacancy CO2 segments replaces tracer gas testing. The Rudnick-Milton reformulation of Wells-Riley computes the rebreathed air fraction in real time, while a pathogen-specific quanta engine covers SARS-CoV-2, influenza, measles, TB, and RSV. Runs on the sensor's existing microcontroller in under 100 KB firmware, outputting a continuously updated infection risk index, ventilation adequacy score, and time-to-threshold alerts with no hardware beyond a single $15-179 NDIR CO2 sensor.
System and Method for Non-Invasive Bridge Structural Health Assessment Using Traffic-Induced Deflection Analysis from Consumer Dashcam Video with Edge-Deployed Physics-Informed Load Rating Estimation
A system that estimates bridge structural condition and load-carrying capacity by measuring traffic-induced deflection from consumer dashcam video. Sub-pixel phase-based optical flow resolves 0.05β0.25 mm vertical deck displacement from a moving vehicle, while ego-motion compensation using IMU data and fixed abutment reference features isolates the bridge response. A physics-informed Euler-Bernoulli beam model fits the measured deflection influence line to estimate effective flexural rigidity, which is compared against design values from the National Bridge Inventory. Crowdsourced across fleet vehicles, 200β500 crossings achieve Β±5% EI precision per bridge at $0.02β0.05 per crossing β replacing $5,000β50,000 biennial visual inspections with continuous monitoring at zero sensor installation cost.
System and Method for Detecting and Localizing Concealed Moisture Intrusion in Building Envelopes Using WiFi Channel State Information from Existing Indoor Wireless Access Point Infrastructure with Temporal Deep Learning and Dielectric Anomaly Mapping
A system that detects concealed water intrusion in walls, floors, and ceilings by analyzing WiFi channel state information from existing mesh access points. Water's relative permittivity (~78 at 2.4 GHz) dwarfs dry building materials (2β8), producing measurable changes in subcarrier amplitude, frequency-selective fading, and group delay as moisture accumulates. A temporal convolutional network trained on multi-scale CSI features distinguishes slow moisture signatures from human activity and thermal cycling, while multi-link dielectric tomography localizes the intrusion to specific wall sections β all at zero incremental hardware cost.
System and Method for Continuous Non-Contact Assessment of Asphalt Pavement Structural Condition Using Traffic-Induced Vibration Transfer Function Analysis from Distributed Acoustic Sensing on Existing Telecommunications Fiber Optic Infrastructure with Recurrent Neural Network Degradation Modeling
A system that repurposes existing buried telecommunications fiber optic cables as distributed acoustic sensors to continuously monitor asphalt pavement structural condition. Traffic-induced vibrations propagate through pavement layers to the fiber, and the frequency-dependent vibration transfer function encodes layer stiffness, damping, bond condition, and moisture state. A CNN-LSTM model trained on paired DAS measurements and ground-truth pavement condition surveys (PCI, FWD, GPR) estimates current condition and predicts remaining useful life for every meter of road along the fiber route, enabling network-scale monitoring at zero incremental sensor cost.
System and Method for Distributed Photovoltaic Array Fault Detection and Power Loss Attribution Using Inter-Inverter Production Correlation Analysis and Weather-Normalized Degradation Modeling
A system that detects, classifies, and quantifies faults in distributed photovoltaic installations by exploiting the statistical correlation structure of production telemetry across co-located microinverters. The system computes pairwise correlation coefficients and weather-normalized production ratios across all inverters within rolling time windows, classifying deviations into five fault categories (partial shading, soiling, cell degradation, connector resistance, inverter electronics degradation) based on each type's mathematically distinct signature in correlation-ratio feature space, then generates prioritized maintenance alerts with estimated energy loss and repair payback periods.
System and Method for Continuous Self-Calibration of Wearable Surface Electromyography Gesture Recognition Models Using Opportunistic Ground Truth Labels Derived from Concurrent Capacitive Touchscreen Input Events on Paired Devices
A system that continuously calibrates wearable sEMG gesture recognition models without explicit calibration sessions by exploiting concurrent touchscreen input events on Bluetooth-paired devices as opportunistic ground truth labels. Each touch event type (tap, swipe, pinch, keystroke) constrains the hand to a known biomechanical configuration, providing labeled sEMG training examples at a rate of 1,000–1,500 per day from natural phone usage. An on-device continual learning pipeline with elastic weight consolidation and experience replay incrementally adapts the gesture classifier to electrode shift, skin impedance drift, and cross-session variability, recovering to within 2% of peak accuracy within 45–90 minutes of natural use with zero user burden and no raw data leaving the device.
System and Method for Distributed Detection and Geolocation of Underground Natural Gas Distribution Pipeline Micro-Leaks Using Spatiotemporal Correlation of Metal-Oxide Semiconductor Gas Sensor Readings from Crowdsourced Consumer Indoor Air Quality Monitor Networks with Inverse Gaussian Plume Dispersion Modeling
A system that detects and geolocates underground natural gas pipeline micro-leaks by exploiting the metal-oxide semiconductor (MOX) gas sensors in 15–25 million deployed consumer indoor air quality monitors. MOX sensors (Sensirion SGP40, Bosch BME680, ScioSense ENS160) exhibit cross-sensitivity to methane, ethane, and mercaptan odorant at concentrations produced by nearby distribution leaks (1–1,000 ppm indoor enhancement). A cloud platform aggregates anonymized VOC readings, applies Gaussian process baseline estimation and DBSCAN spatial clustering to identify correlated anomalies, excludes indoor sources via wind direction, barometric, and stack effect correlation tests, then geolocates the subsurface leak source using MCMC-sampled inverse Gaussian plume dispersion modeling coupled with soil gas transport, achieving 30–80 meter accuracy from 5+ devices over 72 hours at zero incremental hardware cost.
System and Method for Autonomous Calibration and Drift Correction of Distributed Low-Cost Air Quality Sensor Networks Using Mobile Reference Platforms on Public Transit Vehicles with Graph Attention Network Calibration Transfer
A system deploying reference-grade air quality instruments (BAM, UV photometric O3, CAPS NO2) on municipal transit bus rooftops to provide intermittent colocation calibration events for distributed low-cost sensor networks as buses traverse fixed routes covering 60β85% of the urban street grid. Per-sensor drift models (Kalman-filtered linear β GBDT β conditional neural process) are fit from accumulated colocation data, then a graph attention network with multi-head attention over spatial, meteorological, and land-use edge features propagates calibration corrections to off-route sensors via learned atmospheric correlation structure, reducing network-wide PM2.5 RMSE from 8β12 Β΅g/mΒ³ to <3 Β΅g/mΒ³ at 5β15% the cost of equivalent stationary reference monitor coverage.
System and Method for Non-Invasive Classification of Buried Water Service Line Materials and Condition Assessment Using Vehicle-Mounted Multi-Frequency Electromagnetic Induction Arrays, Ground-Penetrating Radar, and Deep Learning Material Fingerprinting
A system deploying multi-frequency electromagnetic induction arrays (1 kHzβ100 kHz, 24 data channels) and dual-frequency ground-penetrating radar on utility fleet vehicles to classify buried water service line materials (lead, copper, galvanized steel, ductile iron, PVC) by exploiting material-specific conductivity-permeability signatures: lead's low-frequency quadrature-to-in-phase crossover at 8β15 kHz, copper's higher conductivity shifting crossover below 5 kHz, ferromagnetic materials' permeability-dominated response, and plastic pipes' GPR-only dielectric reflection. A dual-branch CNN fuses EMI spectrograms and GPR radargrams for >92% material classification accuracy at driving speeds, enabling city-scale LCRR-compliant lead service line inventories at $12β18K per vehicle versus $500β3,000 per excavation.
System and Method for Autonomous Detection and Classification of Overhead Electrical Distribution Fault Events Using Infrasound and Acoustic Signatures Captured by Existing Municipal Gunshot Detection Sensor Networks with Deep Learning Spectral Fingerprinting
A system that repurposes existing municipal gunshot detection acoustic sensor networks (20β25 wideband microphones per square mile, deployed in 170+ cities) to autonomously detect, classify, and geolocate overhead electrical distribution fault events, exploiting the distinctive acoustic signatures of arc faults (120 Hz amplitude modulation), conductor clashing (0.3β2 Hz periodicity), transformer explosions (2β20 Hz infrasound), and insulator flashover (8.33 ms power-frequency-locked double pulses), applying a deep CNN classifier on MFCC spectrograms with infrasound features and TDOA multilateration for 10β25 meter geolocation, enabling sub-minute fault alerts to utility SCADA systems at zero incremental hardware cost.
System and Method for Spatially-Resolved Atmospheric Particulate Matter Estimation Using Visible-Light Extinction Analysis from Existing Outdoor Camera Infrastructure with Neural Calibration Against Colocated Reference Monitors
A system that repurposes existing outdoor cameras (traffic, security, dashcam fleets) as hyperlocal PM2.5/PM10 sensors by analyzing visible-light extinction across reference targets at known distances, applying the Beer-Lambert-Bouguer law to compute spectral contrast attenuation, and calibrating via a CNN trained on synchronized imagery-monitor pairs, producing continuous 100-meter resolution urban air quality maps from the estimated 85 million outdoor cameras already deployed in the US at zero incremental hardware cost.
System and Method for Continuous Urban Heat Island Microclimate Mapping Using Fleet Vehicle External Temperature Sensor Telemetry, Spatiotemporal Kriging, and Land Use Regression Covariates
A system that harvests ambient air temperature from the NTC thermistors already installed in connected fleet vehicles, applies a vehicle-specific bias correction model compensating for engine heat soak, solar loading, and radiator effects, fuses corrected observations with land use regression covariates (impervious surface fraction, NDVI, sky view factor, building density), and interpolates via spatiotemporal kriging with anisotropic MatΓ©rn covariance to produce continuous street-level urban heat island maps at 50β100 m resolution, enabling heat vulnerability alerts, cool corridor routing, and urban greening ROI estimation using millions of already-connected fleet vehicles.
System and Method for Continuous Indoor Temperature Field Mapping Using Bluetooth Low Energy Beacon Battery Voltage Telemetry and Gaussian Process Spatial Interpolation
A system that infers ambient temperature from the battery voltage telemetry of existing BLE beacons, exploiting the electrochemical temperature coefficient of lithium coin cells (β0.3 to β0.5 mV/Β°C OCV, 200β400 mV IR-drop separation over 25Β°C under TX load) and applying Gaussian process regression with a MatΓ©rn 5/2 spatial kernel to produce continuous indoor thermal field maps with quantified uncertainty, enabling HVAC optimization, cold chain compliance, and building energy auditing at zero marginal hardware cost across billions of already-deployed beacons.
System and Method for Predictive Residential Electrical Wiring Degradation Detection Using Power Line Communication Channel State Information from Consumer Broadband-over-Powerline Adapters and On-Premises Edge Inference
A system repurposing OFDM channel estimation data from consumer broadband-over-powerline adapters to continuously monitor residential wiring health, detecting loose connections, insulation carbonization, moisture ingress, and thermal degradation through channel frequency response drift analysis, with multi-node fault localization via channel impulse response triangulation and on-premises temporal convolutional network inference.
System and Method for Automated Companion Animal Health Screening Using Smartphone Video-Based Gait Kinematics Analysis, Respiratory Rate Estimation, and Longitudinal Behavioral Anomaly Detection
A system using consumer smartphone video to perform markerless pose estimation on companion animals, extracting gait kinematics, respiratory rate, and body condition score from a single capture, with breed-specific normalization and longitudinal behavioral anomaly detection for early disease identification, all running on-device with federated learning for continuous improvement.
System and Method for Non-Contact Rotating Machinery Health Assessment Using Consumer Mobile Device Acoustic Emission Analysis and Cross-Domain Transfer Learning
A system using consumer smartphone microphones to capture acoustic emissions from rotating machinery, running on-device spectral decomposition and cross-domain transfer learning from vibration datasets to classify bearing faults, gear wear, and imbalance conditions, with federated learning for continuous improvement without transmitting raw audio off-device, and Weibull survival models for remaining useful life estimation.
System and Method for Predictive Remaining Useful Life Estimation of Asphalt Pavement Using Fleet-Vehicle-Mounted Thermal Infrared Imaging and Physics-Informed Thermal Inertia Inverse Modeling
A system that estimates remaining useful life of asphalt pavement at city scale using thermal inertia measurements from low-cost LWIR cameras on fleet vehicles, computing apparent thermal inertia via Bayesian inversion of a 1-D heat conduction model against multi-pass surface temperature observations, and predicting segment-level remaining useful life through a spatiotemporal convolutional neural network that detects subsurface degradation 12 to 24 months before visible distresses appear.
System and Method for Municipal Water Distribution Network Leak Detection and Localization Using Crowdsourced Pressure Telemetry from Consumer Smart Water Meters and Physics-Informed Graph Neural Networks
A system that detects and localizes leaks in municipal water distribution mains by aggregating high-frequency pressure telemetry from networks of consumer-grade smart water meters, using time-difference-of-arrival analysis of pressure transients propagating through the pipe network and a physics-informed graph neural network whose topology mirrors the utility pipe infrastructure, with privacy-preserving differential pressure reporting that prevents inference of household usage patterns.
System and Method for Non-Intrusive Residential Natural Gas Appliance Load Disaggregation Using Meter-Point Pressure Waveform Transient Analysis and Edge-Deployed Source Separation Neural Networks
A system that identifies individual natural gas appliances and estimates per-appliance consumption from a single high-resolution pressure transducer at the gas meter, using valve-opening transient signatures including onset slope, ring-down reflection patterns, and steady-state pressure offsets processed through a convolutional source separation network that resolves overlapping multi-appliance activations and enforces global flow consistency against the total metered flow.
System and Method for Continuous Urban Tree Health Assessment and Predictive Mortality Mapping Using Opportunistic Multi-Pass Spectral Imaging from Municipal Fleet Vehicles and Spatiotemporal Graph Neural Networks
A system that continuously monitors the health of individual urban trees by mounting multispectral cameras (RGB + NIR) on municipal fleet vehicles such as refuse trucks and street sweepers, performing on-device canopy segmentation and spectral index computation, resolving tree identities across multiple vehicle passes, and predicting 6-month mortality risk via a spatiotemporal graph neural network that models spatial relationships between neighboring trees to detect correlated decline from pests, disease, or shared environmental stress.
System and Method for Non-Invasive Sewer Lateral Condition Assessment and Predictive Blockage Detection Using Residential Fixture Drain Acoustic Emission Analysis and Edge-Deployed Spectral Neural Networks
A system that detects developing sewer line blockages and pipe deterioration by capturing acoustic emissions from ordinary residential drain events (toilet flushes, sink usage, shower flow) using a waterproof MEMS microphone at the building cleanout, extracting spectral features including pipe resonance splitting caused by partial obstructions, classifying seven condition states via an edge-deployed CNN, and tracking longitudinal spectral drift to predict time-to-blockage weeks before catastrophic failure.
System and Method for Continuous Indoor Radon Concentration Estimation Without Dedicated Radon Sensors Using Smart Home Barometric Pressure Differential Time-Series, Soil Permeability Transport Models, and Edge-Deployed Physics-Informed Neural Networks
A system that estimates indoor radon concentration without dedicated radon detectors by analyzing barometric pressure differential time-series from existing smart home sensors (thermostats, weather stations, smartphones), computing foundation-level indoor-outdoor pressure differentials accounting for stack effect, wind depressurization, and HVAC mechanical depressurization, and processing these through a physics-informed neural network embedding the Nazaroff advective soil-gas transport equation conditioned on USGS soil radon potential maps, generating continuous radon risk alerts and preemptive ventilation recommendations.
System and Method for Early Wildfire Detection and Ember Transport Corridor Prediction Using Distributed Consumer Weather Station Microclimate Anomaly Fusion and Physics-Informed Neural Networks
A system that fuses barometric pressure microvariations, wind vectors, temperature anomalies, humidity drops, and particulate matter data from the existing network of consumer weather stations to detect wildfire ignition events via spatiotemporal graph neural networks and predict ember transport corridors using physics-informed neural networks embedding Rothermel fire spread and Albini ember lofting models, issuing geofenced alerts to residents in predicted ember landing zones 15β45 minutes before ember arrival.
System and Method for Non-Invasive Continuous Building Envelope Thermal Performance Assessment Using Smart Thermostat Heating and Cooling Response Curve Analysis with Edge-Deployed Machine Learning
A system that continuously infers building envelope thermal resistance and infiltration parameters from heating and cooling response curves already recorded by commodity smart thermostats, cross-referenced with public weather API data, using a Bayesian state-space model to track degradation over months and years and generate actionable alerts for insulation failures, air seal defects, and moisture intrusion events without requiring any additional hardware, sensors, or site visits.
System and Method for Non-Intrusive Residential Water End-Use Disaggregation and Anomalous Flow Detection Using Smart Meter Hydraulic Transient Fingerprinting, Blind Source Separation, and Self-Supervised Contrastive Learning
A system that disaggregates whole-house water consumption into per-fixture end-use categories using hydraulic pressure transient signatures captured by existing smart water meters, applies non-negative tensor factorization for blind source separation of concurrent fixture events, and trains a temporal convolutional network via self-supervised contrastive learning across a utility service territory without labeled data, enabling per-fixture water accounting and real-time leak detection at less than $15 incremental hardware cost per meter.
System and Method for Continuous Road Surface Friction Coefficient Estimation Using Crowdsourced Vehicle Tire-Road Acoustic Emission, Wheel Speed Sensor Micro-Slip Analysis, and Self-Supervised Contrastive Learning for Real-Time Friction Mapping
A system that fuses tire-road acoustic emission captured by existing vehicle cabin microphones with wheel speed sensor micro-slip ratios during normal driving, trains a friction estimation model via self-supervised contrastive learning using cross-vehicle consistency on shared road segments, and aggregates per-vehicle estimates into real-time spatiotemporal friction maps served to navigation and autonomous driving systems, requiring no new hardware on vehicles manufactured after 2020.
System and Method for Real-Time Distribution Transformer Incipient Fault Detection and Remaining Useful Life Estimation Using Smart Meter Voltage Harmonic Spectrum Analysis and Federated Graph Neural Networks
A system that detects incipient distribution transformer faults by analyzing voltage harmonic spectra from existing smart meters using a federated graph neural network that models the radial distribution network topology, distinguishing transformer-originated harmonics from load and feeder sources, enabling per-transformer remaining useful life estimation at zero marginal hardware cost via firmware updates to deployed AMI meters.
System and Method for Real-Time Power Transmission Line Conductor Sag Estimation and Dynamic Line Rating Using Ground-Level Triaxial Magnetometer Arrays and Physics-Informed Recurrent Neural Networks
A system that deploys low-cost triaxial magnetometer arrays at ground level within transmission line rights-of-way, measures the 60 Hz magnetic field gradient produced by overhead conductors, and solves the inverse problem of conductor height estimation using a physics-informed recurrent neural network encoding the Biot-Savart law and IEEE 738 thermal balance equations, enabling real-time dynamic line rating that safely increases transfer capacity by 15–40% at $85 per sensor node with no conductor contact required.
System and Method for Real-Time Urban Flash Flood Depth Prediction and Dynamic Pedestrian-Vehicle Routing Using Distributed Stormwater Level Sensor Networks, LiDAR-Derived Digital Elevation Models, and Physics-Constrained Graph Neural Networks
A system that deploys low-cost ultrasonic water level sensors inside storm drain manholes and catch basins, fuses real-time hydraulic observations with NEXRAD radar rainfall data and municipal pipe network GIS, and uses a physics-constrained graph neural network to predict street-level flood depths 15–60 minutes before overflow occurs, feeding a routing API that dynamically reroutes pedestrians and vehicles around predicted inundation zones.
System and Method for Continuous Crop Water Stress Detection and Precision Irrigation Scheduling via Contactless Monitoring of Plant Xylem Cavitation Acoustic Emissions Using Ultrasonic MEMS Microphone Arrays and Edge-Deployed Neural Networks
A system that detects crop water stress by listening to ultrasonic clicks produced by xylem cavitation events using contactless MEMS microphone arrays positioned near plant stems, classifying stress levels with an on-device temporal convolutional network, and triggering precision irrigation only when and where plants signal physiological need, targeting 20–40% water savings over timer-based or soil-moisture-only systems at $13 per monitored plant.
System and Method for Non-Invasive Refrigerant Charge Estimation in Vapor-Compression HVAC Systems via Compressor Motor Current Signature Analysis and Edge-Deployed Time-Frequency Neural Networks
A system that estimates refrigerant charge level by clamping a $3 split-core current transformer around the compressor power cable and analyzing the motor current waveform's time-frequency signature with an on-device CNN, detecting Β±5% charge deviations and fault conditions (liquid slugging, active leaks, valve wear) without any refrigerant-side instrumentation, EPA certification, or system downtime.
System and Method for Continuous Subsurface Void Detection Beneath Roadway Surfaces Using Fleet-Vehicle-Mounted Acoustic Resonance Sensing and Edge-Deployed Spectral Convolutional Neural Networks
A system that repurposes ordinary municipal fleet vehicles (transit buses, refuse trucks, street sweepers) as mobile subsurface void detectors by mounting low-cost MEMS accelerometer arrays and contact microphones to vehicle undercarriages, analyzing the tire-road vibroacoustic coupling signature with an on-device spectral CNN, and aggregating multi-pass observations via Bayesian spatial fusion to build probabilistic city-scale void hazard maps without dedicated survey equipment.
System and Method for Continuous Microplastic Detection and Polymer-Type Classification in Municipal Water Distribution Systems Using Inline Multi-Angle Light Scattering and On-Device Neural Network Particle Identification
An inline flow-through optical cell using a 635 nm laser and eight-angle photodetector array that produces Mie scattering profiles for each particle, classified by an on-device 1D-CNN into polymer type (PE, PP, PS, PET, nylon) and size bin, enabling continuous real-time microplastic monitoring across water distribution networks at under $120 per sensor node.
System and Method for Non-Invasive Buried Pipe Condition Assessment Using Surface-Mounted Vibro-Acoustic Sensor Arrays and Physics-Informed Graph Neural Networks
A non-invasive system that deploys piezoelectric accelerometers at fire hydrants and valve boxes, propagates guided elastic waves through buried pipe walls, and uses a physics-informed graph neural network encoding network topology to infer pipe material, wall thickness, corrosion grade, and remaining useful life without excavation.
System and Method for Continuous Neurodegeneration Screening via Consumer Wearable Gait Microanalysis
An on-device inference pipeline that detects sub-clinical gait asymmetries from consumer IMU data, generating a longitudinal neurodegeneration risk score without cloud connectivity or clinical hardware.
System and Method for Automated Detection of Contradictions in Municipal Code Using Large Language Models
An LLM-based pipeline that ingests complete municipal code corpora, identifies logical contradictions between ordinances, and generates structured conflict reports with citation chains for legislative review.
System and Method for Passive Structural Health Monitoring of Bridges Using Consumer Smartphone Accelerometers and Computer Vision
A crowdsourced system combining smartphone accelerometer data from vehicles crossing bridges with periodic computer-vision crack detection from dashcam footage, generating structural health scores without dedicated sensor installations.
System and Method for Real-Time Pollinator Census in Agricultural Fields Using Edge-Deployed Bioacoustic Classification
A distributed network of low-cost MEMS microphone nodes running on-device audio classification models to identify, count, and map pollinator species activity across agricultural parcels in real time, enabling precision pollination management.
System and Method for Synthetic Grid Inertia Generation Using Coordinated Electric Vehicle Battery Discharge with Predictive Frequency Deviation Modeling
A hierarchical control system that monitors grid frequency in real time and coordinates millisecond-scale power injection from distributed EV batteries to provide synthetic rotational inertia, replacing retiring synchronous generators without dedicated grid-scale storage.
System and Method for Real-Time Multilateral Settlement Netting Across Heterogeneous Payment Rails Using Graph-Based Obligation Compression
A settlement engine that ingests payment obligations from multiple rails (ACH, Fedwire, SWIFT, RTP, stablecoin ledgers), constructs a real-time obligation graph, and computes optimal multilateral netting sets that reduce gross settlement volume by 60-80%, lowering systemic liquidity requirements without requiring participants to share a single ledger.