Derivative works — US Patent 8810803

Defensive Disclosure: Derivative Variations for US8810803

This document outlines derivative variations of US patent 8810803, "Lens system," intended to serve as defensive disclosure. The aim is to establish prior art for future incremental improvements, thereby rendering them obvious or non-novel, and to broaden the scope of publicly available knowledge in the relevant technical fields.

Derivatives for Independent Claim 1: System for projecting a pattern of light

Core elements of Claim 1:
A system comprising:

A light source including a plurality of emitters configured to emit light, the plurality of emitters arranged in a pattern.
A cluster of lenses located in front of the light source, wherein each lens of the cluster of lenses is configured to receive light from the plurality of emitters and the cluster of lenses is configured to concurrently focus and project light from each of the emitters in a plurality of directions.
A condenser lens located between the light source and the cluster of lenses, wherein the condenser lens is configured to concentrate light from each of the plurality of emitters towards a center of the cluster of lenses.

1. Material & Component Substitution

Derivative 1.1: Diffractive Optical Element (DOE) Lens Cluster with VCSEL Emitters

Enabling Description: The system replaces the traditional refractive lens cluster with a Diffractive Optical Element (DOE) array. Each element in the DOE array is designed to split incident light from each Vertical Cavity Surface Emitting Laser (VCSEL) emitter into a predefined, non-overlapping or partially overlapping pattern of light points. The light source comprises a two-dimensional array of individually addressable VCSELs operating at 940 nm. The condenser lens is a silicon-based micro-lens array, precisely fabricated using photolithography, designed to collimate and direct the divergent VCSEL emissions onto the corresponding DOE array elements with minimal loss. This allows for highly precise and compact pattern generation with reduced chromatic aberration.

graph TD
    A[VCSEL Array (940nm)] --> B{Micro-Lens Array (Condenser)}
    B --> C[DOE Array (Lens Cluster)]
    C --> D[Projected Pattern]

Derivative 1.2: Liquid Crystal Lens Array with Quantum Dot Emitters

Enabling Description: This variation utilizes a light source composed of a dense matrix of colloidal quantum dot (QD) emitters, selected for narrow-band emission in the near-infrared spectrum (e.g., 850 nm). The QD emitters are integrated onto a flexible substrate. The condenser lens is a gradient-index (GRIN) lens array, specifically tailored to collect and gently converge the broad angular emission from the QDs. The cluster of lenses is implemented as a liquid crystal (LC) lens array, where the focal length and displacement of each individual LC lens can be dynamically adjusted via applied electrical fields. This enables on-the-fly pattern adjustment, focusing, and aberration correction without mechanical movement.

graph TD
    A[Quantum Dot Emitters] --> B{GRIN Lens Array (Condenser)}
    B --> C[LC Lens Array (Cluster)]
    C -- Electrical Control --> D(LC Control Unit)
    C --> E[Dynamically Adjusted Pattern]

Derivative 1.3: Fiber Optic Light Source with Molded Polymer Lens Cluster

Enabling Description: The light source consists of an array of optical fibers, where the distal end of each fiber acts as a light emitter. These fibers are bundled and precisely arranged in a semi-random pattern. Light is injected into the proximal ends of the fibers from a central, high-power infrared laser diode module. The condenser lens is a large, injection-molded aspheric polymer lens (e.g., Zeonex) that efficiently collects light from the fiber array and directs it towards the cluster. The lens cluster itself is also an injection-molded array of polymer micro-lenses, selected for their low cost, high transmittance in the IR spectrum, and suitability for complex, non-spherical geometries, allowing for intentional pattern distortion or unique focal characteristics per lens element.

graph TD
    A[High-Power IR Laser Diode] --> B[Fiber Optic Array (Emitters)]
    B --> C{Molded Aspheric Polymer Lens (Condenser)}
    C --> D[Molded Polymer Lens Cluster]
    D --> E[Projected Pattern]

2. Operational Parameter Expansion

Derivative 1.4: Nanoscale Emitter Array for Micro-Pattern Projection

Enabling Description: The system features a light source composed of an array of plasmonic nano-antennas or sub-wavelength apertures, functioning as nanoscale emitters. These emitters are fabricated on a transparent substrate with a pitch of 100-500 nm, allowing for extremely dense patterns. The illumination is achieved by coupling light from a compact solid-state laser into the substrate. The condenser lens is a highly-numerical-aperture (NA) immersion objective lens designed for deep-UV or EUV wavelengths, concentrating the excitation light onto the nano-emitters. The lens cluster comprises a metasurface lens array, engineered to simultaneously collect and project the highly localized emissions into a micro-pattern (e.g., 10-100 micrometers in size) onto nearby surfaces, useful for micro-inspection or nanofabrication.

graph TD
    A[Solid-State Laser] --> B[Plasmonic Nano-Antenna Array (Emitters)]
    B --> C{Immersion Objective Lens (Condenser)}
    C --> D[Metasurface Lens Array (Cluster)]
    D --> E[Micro-Pattern Projection]

Derivative 1.5: High-Power Pulsed Laser System for Long-Range Detection

Enabling Description: This system is engineered for long-range object detection (100m to 1km+). The light source consists of a phased array of high-power pulsed fiber lasers, each operating at 1550 nm for eye-safe outdoor use. Each laser emits short (nanosecond) high-peak-power pulses. The emitters are arranged in a sparse, rectangular grid. The condenser lens is a large-aperture, highly robust f-theta scan lens, designed to withstand high optical power and to accurately direct the pulsed laser beams over a wide field of view towards the lens cluster. The lens cluster consists of an array of ruggedized, thermally stable fused silica lenses, each engineered with a slightly different angular displacement to create a wide-area, high-density pattern for improved signal-to-noise ratio in outdoor environments. The entire system is enclosed in a weather-sealed, temperature-controlled housing.

graph TD
    A[Pulsed Fiber Laser Array (1550nm)] --> B{F-Theta Scan Lens (Condenser)}
    B --> C[Fused Silica Lens Cluster]
    C --> D[Long-Range Projected Pattern (100m-1km+)]

Derivative 1.6: Terahertz Frequency Pattern Projection for Material Characterization

Enabling Description: This system operates in the terahertz (THz) frequency range for non-destructive material characterization. The light source consists of a multi-element array of quantum cascade lasers (QCLs) operating at distinct THz frequencies, arranged in a structured pattern. Each QCL acts as an emitter. The condenser lens is a large, low-loss TPX (polymethylpentene) lens, designed to efficiently collect and concentrate the THz radiation. The cluster of lenses is an array of custom-fabricated silicon-on-insulator (SOI) resonant cavity lenses, which individually shape and project the THz beams. This allows for the creation of a THz pattern that can penetrate various materials, and the interaction of the pattern with the material can be analyzed to determine material composition, density, and defects, particularly useful for non-metallic substances.

graph TD
    A[THz QCL Array (Emitters)] --> B{TPX Condenser Lens}
    B --> C[SOI Resonant Cavity Lens Cluster]
    C --> D[THz Pattern for Material Characterization]

3. Cross-Domain Application

Derivative 1.7: Surgical Navigation and Tissue Characterization

Enabling Description: In surgical applications, the system provides real-time guidance and tissue characterization. The light source, comprising a cluster of narrow-band LED emitters (e.g., 650nm, 850nm, 980nm), projects a multi-spectral, randomized light pattern onto the surgical field. A condenser lens concentrates these emissions. The lens cluster focuses and projects these patterns onto target tissues. A stereo camera system captures the distorted patterns, and a computing device uses stereopsis algorithms to reconstruct the 3D surface of the tissue and identify specific tissue types (e.g., nerve, artery, tumor margins) based on their unique spectral absorption and scattering properties of the projected multi-spectral pattern. This enhances precision and safety during complex procedures.

graph TD
    A[Multi-Spectral LED Emitters] --> B{Condenser Lens}
    B --> C[Lens Cluster]
    C --> D[Multi-Spectral Pattern on Tissue]
    D -- Reflected Light --> E[Stereo Camera System]
    E --> F[Computing Device (3D Reconstruction & Tissue ID)]
    F --> G[Surgical Display (Overlay)]

Derivative 1.8: Autonomous Vehicle Terrain Mapping and Obstacle Avoidance

Enabling Description: For autonomous vehicles, the system projects a dynamic pattern of infrared light onto the road and surrounding environment. The light source uses a MEMS-based laser scanning emitter array to project the pattern. A condenser lens collimates the individual beams. The lens cluster features a steerable micro-lens array that can adjust the projection angle and density of the pattern in real-time, focusing on areas of interest (e.g., upcoming obstacles, lane markers, road surface anomalies). This structured light pattern is captured by an onboard IR camera. The computing device analyzes the deformation of the projected pattern to generate high-resolution 3D point clouds of the terrain, detect pedestrian gestures, and identify potential hazards, even in low-light conditions.

graph TD
    A[MEMS Laser Scanner Emitter Array] --> B{Condenser Lens}
    B --> C[Steerable Micro-Lens Array]
    C --> D[Dynamic IR Pattern on Terrain]
    D -- Reflected Light --> E[Onboard IR Camera]
    E --> F[Computing Device (3D Point Cloud, Obstacle Detection)]
    F --> G[Autonomous Vehicle Control System]

Derivative 1.9: Industrial Quality Control and Defect Detection

Enabling Description: In manufacturing, the system is integrated into a quality control station to inspect surfaces of products (e.g., automotive panels, electronic circuit boards). The light source uses a high-density array of blue LEDs to project a finely detailed, pseudo-random light pattern onto the product surface. A wide-angle condenser lens ensures broad illumination. The lens cluster consists of a high-resolution Fresnel lens array. Two synchronized cameras capture the projected pattern from different angles. A computing device performs sub-pixel analysis of the pattern deformation to detect microscopic defects, surface irregularities, scratches, and inconsistencies in material finish that are invisible to the naked eye. This enables automated, high-speed inspection with precise defect localization.

graph TD
    A[High-Density Blue LED Array] --> B{Wide-Angle Condenser Lens}
    B --> C[Fresnel Lens Array]
    C --> D[Pseudo-Random Pattern on Product]
    D -- Captured Images --> E[Synchronized Dual Cameras]
    E --> F[Computing Device (Sub-Pixel Defect Analysis)]
    F --> G[QC Reporting System]

4. Integration with Emerging Tech

Derivative 1.10: AI-Optimized Adaptive Pattern Projection System

Enabling Description: This system integrates an AI-driven optimization engine. The light source consists of a reconfigurable emitter array (e.g., digital micromirror device - DMD, or dynamically addressable LED matrix). The condenser lens and lens cluster remain conventional. A high-speed camera captures the projected pattern on an object. The AI optimization engine, utilizing deep reinforcement learning, analyzes the camera feedback (e.g., pattern resolution, contrast, coverage, computational load for object tracking) and dynamically adjusts the emitter pattern, intensity, and the focal characteristics (if dynamic lenses are used) to achieve optimal object location and tracking performance for varying object geometries, distances, and ambient lighting conditions. The AI learns optimal patterns for specific recognition tasks.

graph TD
    A[Reconfigurable Emitter Array] --> B{Condenser Lens}
    B --> C[Lens Cluster]
    C --> D[Adaptive Pattern]
    D -- Feedback --> E[High-Speed Camera]
    E -- Image Data --> F(AI Optimization Engine)
    F -- Control Signals --> A

Derivative 1.11: IoT-Enabled Real-time Pattern Monitoring and Maintenance

Enabling Description: This system incorporates IoT sensors for real-time monitoring and predictive maintenance. Each component within the light source (individual emitters), condenser lens, and lens cluster (individual lenses) is equipped with embedded micro-sensors for temperature, current, light output, and optical alignment. These sensors communicate wirelessly via a low-power IoT network (e.g., LoRaWAN) to a central gateway. A cloud-based IoT platform aggregates this data, applying machine learning algorithms to detect anomalies, predict potential failures, and schedule proactive maintenance for the optical components. For instance, if an emitter shows reduced output or a lens begins to misalign, the system can self-diagnose and recommend corrective actions or activate redundant emitters/lenses to maintain pattern integrity.

graph TD
    subgraph Emitter/Lens Unit
        A[Emitter] -- Data --> Z(Micro-Sensors)
        B[Condenser Lens] -- Data --> Z
        C[Lens Cluster] -- Data --> Z
    end
    Z -- Wireless (LoRaWAN) --> D[IoT Gateway]
    D --> E[Cloud-based IoT Platform]
    E -- ML for Anomaly Detection --> F(Predictive Maintenance System)
    F --> G[Maintenance Alerts/Actions]

Derivative 1.12: Blockchain-Secured Pattern Projection for Authenticity Verification

Enabling Description: This system enhances security and data integrity by integrating blockchain technology. The specific characteristics of the projected pattern (e.g., emitter arrangement, dynamic adjustments, temporal sequences) are digitally signed and recorded as transactions on a private blockchain. A cryptographic hash of the pattern configuration is generated and timestamped. Any detected pattern by the camera system is then verified against the blockchain record to confirm its authenticity and integrity. This prevents tampering or spoofing of the projected light pattern, which is critical for applications like secure authentication (e.g., verifying a user's presence or action in a secure zone) or validating the provenance of scanned objects in a supply chain.

graph TD
    A[Light Source] --> B[Pattern Generator]
    B --> C[Projected Pattern]
    B -- Cryptographic Hash --> D[Blockchain Network]
    D -- Record Pattern Config --> E(Distributed Ledger)
    C -- Detected Pattern --> F[Camera System]
    F -- Hash Comparison --> G[Verification Module]
    G -- Validate against --> E

5. The "Inverse" or Failure Mode

Derivative 1.13: Safe-Failure Redundant Illumination System

Enabling Description: This system is designed with redundancy and a safe-failure mode. The light source comprises multiple independent emitter sub-arrays. The condenser lens is segmented, with each segment focusing light from a corresponding sub-array. The lens cluster also features redundant sections. In the event of a failure detected in any primary emitter, lens segment, or control circuit, the system automatically switches to a pre-configured, lower-power, and often wider-angle "safe mode" illumination pattern using the redundant components. This safe pattern might be visible (e.g., red LEDs) to indicate system degradation or a diagnostic pattern, ensuring that critical operations are not abruptly halted and operators are alerted to a reduced functionality state. The primary goal is to maintain minimal, stable illumination for continued (albeit degraded) object detection until full repair.

graph TD
    subgraph Primary Sub-System
        A[Primary Emitter Sub-Array] --> B{Primary Condenser Segment}
        B --> C[Primary Lens Cluster Section]
    end
    subgraph Redundant Sub-System
        D[Redundant Emitter Sub-Array] --> E{Redundant Condenser Segment}
        E --> F[Redundant Lens Cluster Section]
    end
    G(Failure Detection Unit) -- Detects Fault --> A
    G -- Activates --> D
    G -- Switches Pattern --> H(System Control Unit)
    C --> I[Normal Pattern]
    F --> J[Safe-Mode Pattern]
    H -- Selects --> I
    H -- Selects on Failure --> J

Derivative 1.14: Low-Power Diagnostic Pattern System

Enabling Description: This system includes a low-power diagnostic mode. The light source incorporates a dedicated set of very low-power emitters, distinct from the primary high-power emitters (e.g., miniature IR LEDs for primary, low-mA visible LEDs for diagnostic). The condenser lens and lens cluster are designed such that they can also direct light from these diagnostic emitters. Upon system startup, or when instructed, the system projects a simplified, low-density, and stable diagnostic light pattern using only these low-power emitters. This pattern is easily recognizable and can be used for initial system alignment, sensor calibration, or to verify basic functionality of the optical path without engaging the full high-power illumination, thus saving energy and extending component lifespan during idle periods or setup.

graph TD
    A[High-Power Emitters (IR)]
    B[Low-Power Diagnostic Emitters (Visible)]
    A --> C{Condenser Lens}
    B --> C
    C --> D[Lens Cluster]
    E(System Control) -- Normal Mode --> A
    E -- Diagnostic Mode --> B
    D --> F[Full Pattern]
    D --> G[Diagnostic Pattern]

Derivatives for Independent Claim 8: Method for projecting a pattern of infrared light

Core elements of Claim 8:
A method comprising:

Emitting, from a plurality of emitters, a plurality of lights arranged in a pattern.
Concentrating, via a condenser lens, the plurality of lights towards a central location of a cluster of lenses.
Receiving the concentrated light at a plurality of points within the cluster of lenses.
Concurrently focusing, from each lens of the cluster of lenses, the received and concentrated light from each of the plurality of emitters in a plurality of directions.

1. Material & Component Substitution (Method perspective)

Derivative 8.1: Method Using Electrically Tunable Organic LED (OLED) Emitters and Liquid Polymer Lenses

Enabling Description: This method involves emitting light from a dynamic array of electrically tunable Organic Light-Emitting Diodes (OLEDs), where the brightness and effective emission area of each OLED can be individually controlled to form an active light pattern. The light emitted is then concentrated by a dynamically shape-changing liquid polymer lens (e.g., electrowetting-based or mechanically deformable polymer), which acts as the condenser lens, towards the central region of a cluster composed of electro-optic polymer lenses. These electro-optic polymer lenses, in turn, concurrently focus and project the incident light, with their refractive indices (and thus focal lengths/displacements) being rapidly modulated by applied electric fields, allowing for fast, programmable pattern variations in multiple directions.

sequenceDiagram
    participant OLEDs
    participant LPL(Condenser)
    participant EOPL(Cluster)
    participant Object

    OLEDs->>LPL(Condenser): Emit tunable light pattern
    LPL(Condenser)->>EOPL(Cluster): Concentrate and shape light
    EOPL(Cluster)->>Object: Concurrently focus and project dynamic pattern

Derivative 8.2: Method Employing Micro-LED Emitters and Meta-Lens Array

Enabling Description: The method begins by emitting light from an extremely high-density array of inorganic micro-LEDs (e.g., GaN-based, operating at 850 nm). The pattern is formed by selective activation of these micro-LEDs. A multi-layer metasurface lens functions as the condenser lens, designed to collect and coherently guide the emitted light from the micro-LEDs towards a central focal region. This light is then received by a second metasurface lens array, which acts as the cluster of lenses. Each element of this meta-lens array concurrently manipulates the phase and amplitude of the incident light, focusing and projecting it in multiple, precisely engineered directions to form a complex, high-resolution light pattern without bulky refractive elements.

sequenceDiagram
    participant MicroLEDs
    participant MetaLens1(Condenser)
    participant MetaLens2(Cluster)
    participant Object

    MicroLEDs->>MetaLens1(Condenser): Emit high-density pattern
    MetaLens1(Condenser)->>MetaLens2(Cluster): Coherently guide light
    MetaLens2(Cluster)->>Object: Concurrently focus & project complex pattern

2. Operational Parameter Expansion (Method perspective)

Derivative 8.3: Method for Sub-millimeter Resolution Pattern Projection

Enabling Description: This method focuses on generating patterns with sub-millimeter resolution for precision measurement. It involves emitting highly collimated laser beams from an array of single-mode fiber-coupled laser diodes. The pattern is created by digitally modulating each laser diode. The emitted light is concentrated by a telecentric condenser lens system, ensuring that light rays approach the cluster of lenses parallel to the optical axis, minimizing distortion. The cluster of lenses consists of a microlens array with precisely controlled sag and pitch. The method includes a step of actively controlling the temperature of the entire optical path to maintain thermal stability, allowing for the concurrent focusing and projection of the sub-millimeter light pattern with high angular accuracy across the entire field of view.

sequenceDiagram
    participant LaserDiodes
    participant TelecentricCondenser
    participant MicrolensCluster
    participant Object
    participant TempControl

    LaserDiodes->>TelecentricCondenser: Emit collimated beams
    TelecentricCondenser->>MicrolensCluster: Concentrate with telecentricity
    MicrolensCluster->>Object: Concurrently focus & project sub-mm pattern
    TempControl->>MicrolensCluster: Maintain thermal stability
    TempControl->>TelecentricCondenser: Maintain thermal stability

Derivative 8.4: Method for Adaptive Pattern Generation at Varying Environmental Pressures

Enabling Description: This method is designed for systems operating under varying ambient pressures (e.g., deep-sea exploration, high-altitude atmospheric monitoring). Light is emitted from a robust, pressure-sealed array of high-brightness LEDs. The pattern of light is adapted in real-time based on pressure sensor feedback. The condenser lens, encased in a pressure-resistant housing, concentrates the light, with its optical properties compensated for pressure-induced changes in refractive index of the surrounding medium (e.g., water, rarefied air). The cluster of lenses, also pressure-compensated, concurrently focuses and projects the adapted pattern. The method includes a real-time calibration step where the projected pattern's characteristics are measured and adjusted to counteract optical distortions caused by external pressure fluctuations, ensuring consistent pattern quality.

sequenceDiagram
    participant LEDs
    participant PressureSensor
    participant Condenser(Pressure-Compensated)
    participant LensCluster(Pressure-Compensated)
    participant Environment

    LEDs->>Condenser(Pressure-Compensated): Emit pattern
    PressureSensor->>LEDs: Report ambient pressure
    Condenser(Pressure-Compensated)->>LensCluster(Pressure-Compensated): Concentrate light
    LensCluster(Pressure-Compensated)->>Environment: Project adapted pattern
    Environment-->>PressureSensor: Measure pressure
    PressureSensor->>LensCluster(Pressure-Compensated): Adjust for pressure distortion

3. Cross-Domain Application (Method perspective)

Derivative 8.5: Method for Precision Agriculture Plant Growth Monitoring

Enabling Description: This method is applied in precision agriculture to monitor plant growth and health. A drone-mounted system emits specific spectral patterns (e.g., narrow-band red, green, near-IR) from an array of LEDs onto crops. A wide-angle condenser lens concentrates these emissions. A ruggedized lens cluster concurrently focuses and projects these spectral patterns over a large area. A multispectral camera on the drone detects the scattered light. The method then involves analyzing the spectral reflectance/absorbance patterns of individual plants to determine growth stages, detect nutrient deficiencies, identify disease outbreaks, and assess water stress, enabling targeted intervention and optimizing resource use.

sequenceDiagram
    participant Drone(Emitters)
    participant Condenser
    participant LensCluster
    participant Crops
    participant MultispectralCamera
    participant ProcessingUnit

    Drone(Emitters)->>Condenser: Emit multi-spectral pattern
    Condenser->>LensCluster: Concentrate light
    LensCluster->>Crops: Project patterns
    Crops-->>MultispectralCamera: Scatter light
    MultispectralCamera->>ProcessingUnit: Capture multispectral images
    ProcessingUnit->>ProcessingUnit: Analyze spectral data for plant health

Derivative 8.6: Method for Underwater Object Detection and Mapping

Enabling Description: This method describes underwater pattern projection for object detection and mapping. Short-pulse blue-green laser diodes (selected for water penetration) emit light in a pattern. A pressure-resistant condenser lens concentrates the light, compensating for water's refractive index. A specially designed, anti-fouling lens cluster concurrently focuses and projects the laser pulses through the water. A synchronized underwater camera array captures the reflections. The method involves compensating for light absorption and scattering in water, using time-of-flight measurements from the pulsed pattern to generate high-resolution 3D maps of submerged structures, marine life, or seabed topography, overcoming the limitations of acoustic sonar in certain scenarios.

sequenceDiagram
    participant LaserDiodes
    participant Condenser(Water-Compensated)
    participant LensCluster(Anti-Fouling)
    participant Water
    participant UnderwaterCamera
    participant ComputingDevice

    LaserDiodes->>Condenser(Water-Compensated): Emit blue-green pulses
    Condenser(Water-Compensated)->>LensCluster(Anti-Fouling): Concentrate light
    LensCluster(Anti-Fouling)->>Water: Project pulsed pattern
    Water-->>UnderwaterCamera: Reflects pulses from objects
    UnderwaterCamera->>ComputingDevice: Capture synchronized images
    ComputingDevice->>ComputingDevice: Generate 3D underwater map (time-of-flight)

4. Integration with Emerging Tech (Method perspective)

Derivative 8.7: Method for Real-time Edge-AI Pattern Generation and Analysis

Enabling Description: This method integrates edge-AI for real-time pattern generation and analysis directly at the illumination source. Light is emitted from a smart emitter array, where each emitter's behavior is controlled by an embedded AI module. The condenser lens concentrates the light, and the lens cluster focuses and projects the pattern. The camera system is coupled directly to the emitter array, forming a closed-loop system. The method involves an edge-AI model, pre-trained on diverse object recognition tasks, analyzing the camera's raw image data of the projected pattern. This AI instantaneously adapts the emitted pattern (e.g., changes density, adds unique identifiers to regions) to optimize real-time object tracking or feature extraction for various tasks, minimizing data latency and bandwidth requirements by processing locally.

sequenceDiagram
    participant EmitterArray(Edge-AI)
    participant Condenser
    participant LensCluster
    participant Object
    participant Camera

    EmitterArray(Edge-AI)->>Condenser: Emit AI-optimized pattern
    Condenser->>LensCluster: Concentrate light
    LensCluster->>Object: Project pattern
    Object-->>Camera: Reflect light
    Camera->>EmitterArray(Edge-AI): Real-time image data
    EmitterArray(Edge-AI)->>EmitterArray(Edge-AI): Analyze & adapt pattern (Edge-AI)

Derivative 8.8: Method for Federated Learning-Enabled Pattern Optimization

Enabling Description: This method utilizes federated learning for continuous improvement of pattern projection across multiple distributed systems. Light is emitted, concentrated by a condenser lens, and focused/projected by a lens cluster as described. Instead of a single AI model, local AI models (running on edge devices) at each projection system generate optimal patterns based on local environmental conditions and object types. These local models periodically send only their learned parameter updates (not raw data) to a central server. The central server aggregates these updates to create a global, more robust AI model for pattern optimization, which is then pushed back to the distributed systems. This iterative process continuously refines the pattern projection method, improving its effectiveness and adaptability while preserving data privacy.

sequenceDiagram
    participant System1(Local AI)
    participant System2(Local AI)
    participant CentralServer(Global AI)
    participant Emitters

    System1(Local AI)->>Emitters: Project pattern (optimized locally)
    System2(Local AI)->>Emitters: Project pattern (optimized locally)
    System1(Local AI)->>CentralServer(Global AI): Send parameter updates
    System2(Local AI)->>CentralServer(Global AI): Send parameter updates
    CentralServer(Global AI)->>CentralServer(Global AI): Aggregate & update global model
    CentralServer(Global AI)->>System1(Local AI): Distribute global model updates
    CentralServer(Global AI)->>System2(Local AI): Distribute global model updates

5. The "Inverse" or Failure Mode (Method perspective)

Derivative 8.9: Method for Dynamic Pattern Degradation for Privacy Preservation

Enabling Description: This method implements dynamic pattern degradation to preserve privacy or reduce intrusiveness. The emitters are capable of emitting multiple distinct patterns. In a normal operational state, a complex, high-resolution pattern is projected for accurate object tracking. However, if the system detects the presence of unauthorized individuals, or enters a designated "privacy zone," the method automatically switches to emitting a significantly degraded, low-resolution, and simplified pattern (e.g., a few widely spaced dots or a diffuse glow). This degraded pattern is sufficient to confirm presence but insufficient for individual identification or detailed tracking, thereby preserving privacy while maintaining basic functionality. The transition can be triggered by external sensors, predefined geofences, or internal recognition algorithms.

stateDiagram-v2
    state "High_Resolution_Tracking" as HRT
    state "Privacy_Preservation_Mode" as PPM

    HRT --> PPM: Unauthorized_Presence_Detected / Enter_Privacy_Zone
    PPM --> HRT: Authorized_Presence / Exit_Privacy_Zone

    HRT: Project complex pattern for detailed tracking
    PPM: Project degraded pattern for presence detection only

Derivative 8.10: Method for Energy-Harvesting Aperiodic Pattern Projection

Enabling Description: This method describes a low-power system where the pattern projection is dynamically influenced by harvested energy. Light is emitted from an array of ultra-low-power LEDs or electro-chromic pixel elements. The condenser lens and lens cluster are passive components. The method involves harvesting ambient energy (e.g., solar, kinetic vibration) to power the emitters. When energy levels are high, a denser, more complex aperiodic pattern is generated. As energy levels drop, the method gracefully degrades the pattern, reducing the number of active emitters or the refresh rate, creating a sparser, less complex pattern while maintaining basic detectability. This ensures continuous, albeit variable, operation in energy-constrained environments.

sequenceDiagram
    participant EnergyHarvester
    participant PowerManagement
    participant UltraLowPowerLEDs
    participant PassiveOptics
    participant Environment

    EnergyHarvester->>PowerManagement: Supply harvested energy
    PowerManagement->>UltraLowPowerLEDs: Control power based on availability
    UltraLowPowerLEDs->>PassiveOptics: Emit pattern (variable density)
    PassiveOptics->>Environment: Project aperiodic pattern
    Environment-->>EnergyHarvester: Provide ambient energy

Combination Prior Art Scenarios

Here are three combination prior art scenarios where US8810803 could be combined with existing open-source standards to demonstrate obviousness or lack of novelty for certain improvements:

1. US8810803 + Open-Source Computer Vision Libraries (e.g., OpenCV)

Scenario: An improvement claiming novel methods for processing the projected pattern to determine object location or gestures.
Combination: The core system/method of US8810803 (projecting a random/semi-random light pattern using an emitter array, condenser, and lens cluster) combined with publicly available, open-source computer vision algorithms (e.g., those found in OpenCV's modules for feature detection, stereo correspondence, optical flow, or background subtraction) for analyzing the detected pattern on an object.
Enabling Description: A person skilled in the art would recognize that after detecting the pattern on an object with a camera (as taught by US8810803, e.g., in Claim 7), standard computer vision techniques readily available in libraries like OpenCV could be applied. For instance, cv2.StereoBM or cv2.StereoSGBM (for block matching or semi-global block matching) could be used to process images from two cameras capturing the projected pattern, deriving disparity maps and thus 3D depth information. Similarly, feature descriptors like SIFT or ORB (also in OpenCV) could be applied to uniquely identify patches of the projected pattern as mentioned in the patent's detailed description. Therefore, a system performing these steps would be an obvious application of known computer vision methods to the output of US8810803's projection system.

2. US8810803 + MQTT Protocol for IoT Data Communication

Scenario: An improvement claiming an IoT-enabled system for monitoring the health or status of the light projection components.
Combination: The system of US8810803 integrated with widely adopted open-source IoT communication protocols like MQTT for transmitting sensor data related to the illuminator's operational parameters.
Enabling Description: Consider a system as described in Claim 1 of US8810803. It would be obvious for a person skilled in the art to incorporate sensors (e.g., thermistors for LED emitters, current sensors for power consumption, photodetectors for monitoring light output) into the light source and lens assembly. To enable remote monitoring or predictive maintenance, these sensor readings could be transmitted using a lightweight, publish-subscribe messaging protocol like MQTT (an OASIS standard, with open-source client libraries available for various platforms). An embedded microcontroller at the illumination system (acting as an MQTT client) would publish sensor data to an MQTT broker, which would then distribute it to subscribed monitoring applications. This combination integrates the existing optical system with standard, open-source networking practices for industrial monitoring, rendering such a data transmission "improvement" obvious.

3. US8810803 + Linux Kernel's GPIO/PWM Drivers for Emitter Control

Scenario: An improvement claiming software-defined control over the individual emitters in the light source, particularly for creating dynamic patterns.
Combination: The light source with multiple emitters (as in Claim 1 or 8 of US8810803) controlled by a computing device utilizing open-source drivers and interfaces available in the Linux kernel for General Purpose Input/Output (GPIO) or Pulse Width Modulation (PWM).
Enabling Description: The patent specifies that emitters in the light source can be "turned on or off via an electronic control system thereby allowing the pattern emitted from the light source 110 to vary" (Description, Col. 6, lines 34-36). For a computing device (e.g., an embedded Linux board) controlling an array of LEDs, it is well-known in the art to use GPIO pins for simple on/off control or PWM signals for brightness modulation. The Linux kernel provides standard, open-source drivers and userspace interfaces (e.g., /sys/class/gpio or /sys/class/pwm) to control these hardware peripherals. Thus, implementing a software system on a Linux-based controller to dynamically switch or dim individual emitters to create variable patterns, building upon the basic electronic control described in the patent, would be an obvious engineering task for a skilled practitioner familiar with embedded Linux development.