Patent 8471812

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

Active provider: Google · gemini-2.5-flash

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Defensive Disclosure for US Patent 8471812: Pointing and Identification Device

This Defensive Disclosure aims to establish prior art for derivative variations of US Patent 8471812, making future incremental improvements by competitors obvious or non-novel. The current date is April 26, 2026.

Derivative Variations

Independent Claim 1 Derivatives: Pointing and Identification Device

Claim 1: A pointing and identification device (PID) comprising: a digital camera; a laser pointer configured to project a visible spot for aiming the digital camera; and a communication component configured to communicate with an external computer system; wherein the PID is configured to: capture an image of a target via the digital camera; identify the target based on the captured image; determine a precise location on the identified target pointed to by the laser pointer; and transmit information related to the identified target and the precise location to the external computer system.

Derivative 1.1: Material & Component Substitution (Non-Visible Spectrum Pointing)

  • Enabling Description: A pointing and identification device (PID) employs an infrared (IR) laser diode (e.g., 850 nm wavelength, 100mW power) for projecting a non-visible spot onto a target, thereby eliminating visual distraction while maintaining aiming functionality. The digital camera is replaced with an IR-sensitive CMOS imaging sensor array (e.g., 1/2.3-inch format, 12 MP resolution, with an integrated IR band-pass filter at 840-860 nm) capable of capturing images in the IR spectrum. The communication component utilizes a secure, short-range Ultra-Wideband (UWB) module (e.g., IEEE 802.15.4z compliant) for high-bandwidth, low-latency data transmission to a localized external computer system. Target identification and precise location determination are performed by the external computer system using a neural network trained on IR imagery, with the IR laser spot being detected as a distinct IR hotspot against the target background.
  • Mermaid Diagram:
    graph TD
    A[PID Device] --> B{IR Laser Diode};
    A --> C{IR CMOS Camera Sensor};
    A --> D{UWB Communication Module};
    C --> E[Capture IR Image];
    B --> F[Project IR Spot on Target];
    E --> G[Transmit IR Image to External System];
    D --> G;
    G --> H[External Computer System];
    H --> I{Neural Network for IR Image Analysis};
    I --> J[Identify Target];
    I --> K[Detect IR Spot Location];
    J & K --> L[Determine Precise Location];
    H --> M[Transmit Info to User/Application];

Derivative 1.2: Operational Parameter Expansion (Micro-Scale Precision Pointing)

  • Enabling Description: A micro-pointing and identification device (μPID) is integrated into the stage assembly of a high-resolution optical microscope. The "laser pointer" is miniaturized to a focused ultraviolet (UV) micro-laser (e.g., 375 nm wavelength, 1 mW power, beam spot size <1 µm), controlled by a galvanometer mirror system for precise steering within the microscope's field of view. The digital camera is a high-magnification, high-frame-rate CCD sensor (e.g., 20 MP, 1000 fps) observing the sample through the microscope objective. The communication component is a dedicated optical fiber link (e.g., 10 Gbps Ethernet over OM4 multimode fiber) to a high-performance computing cluster. The μPID identifies microscopic features (e.g., specific cell organelles, semiconductor defects) on a substrate based on image analysis algorithms (e.g., blob detection, feature matching) run on the cluster. The UV micro-laser spot, distinguishable by its specific wavelength and small size, is used to designate sub-micron locations for subsequent manipulation by robotic micro-tools or localized chemical deposition. Operational parameters include temperature stabilization to within 0.1°C and vibration isolation to <5 nm RMS.
  • Mermaid Diagram:
    graph TD
    A[μPID Device (Microscope Integrated)] --> B{UV Micro-Laser & Galvo System};
    A --> C{High-Mag CCD Sensor};
    A --> D{Optical Fiber Link (10Gbps)};
    C --> E[Capture Micro-Image];
    B --> F[Project UV Micro-Spot on Sample];
    E --> G[Transmit Micro-Image to HPC Cluster];
    D --> G;
    G --> H[HPC Cluster];
    H --> I{Image Analysis Algorithms};
    I --> J[Identify Microscopic Features];
    I --> K[Detect UV Micro-Spot Location];
    J & K --> L[Determine Precise Micro-Location];
    H --> M[Control Micro-Tools/Processes];

Derivative 1.3: Cross-Domain Application (Precision Agriculture Plant Health Monitoring)

  • Enabling Description: A pointing and identification device (PID) is mounted on an unmanned aerial vehicle (UAV) for precision agriculture. The PID incorporates a multi-spectral camera (capturing visible, NIR, and SWIR bands) for comprehensive plant health assessment. A modulated green laser pointer (e.g., 532 nm, 50mW, pulsed at 10 kHz) is used to project a visible spot on specific plants for aiming. The communication component is a long-range cellular modem (e.g., 5G NR, mmWave capable) for real-time data streaming to a cloud-based agricultural analytics platform. The UAV-mounted PID captures multi-spectral images of crops. The analytics platform identifies individual plants or specific plant diseases/nutrient deficiencies (e.g., using convolutional neural networks trained on plant imagery and disease signatures) and determines the precise location pointed to by the laser. This information is then used to trigger targeted pesticide application by companion agricultural robots, localized nutrient delivery, or to log specific plant health issues for manual inspection.
  • Mermaid Diagram:
    graph TD
    A[UAV-Mounted PID] --> B{Multi-Spectral Camera};
    A --> C{Modulated Green Laser};
    A --> D{5G NR Communication Module};
    B --> E[Capture Crop Multi-Spectral Image];
    C --> F[Project Green Spot on Plant];
    E --> G[Transmit Data to Cloud Platform];
    D --> G;
    G --> H[Cloud Agricultural Analytics Platform];
    H --> I{CNN for Plant Health/Disease ID};
    I --> J[Identify Plant/Condition];
    I --> K[Detect Laser Spot Location];
    J & K --> L[Determine Precise Geographic Location];
    H --> M[Trigger Targeted Agricultural Action];

Derivative 1.4: Integration with Emerging Tech (AI-Optimized Contextual PID)

  • Enabling Description: A pointing and identification device (PID) integrates on-device AI for real-time contextual awareness and predictive aiming. The digital camera features a high-resolution RGB-D sensor (e.g., combining 4K RGB with structured light depth sensing) providing both visual and 3D geometric data. The laser pointer is dynamically controlled by an embedded AI inference engine (e.g., running a lightweight YOLO-variant model on a dedicated NPU) which predicts user intent and adjusts laser power, beam shape (e.g., crosshair, bounding box), and modulation frequency based on the detected target and ambient conditions. The communication component utilizes Wi-Fi 6E for high-throughput, low-latency transfer to an edge computing node. The PID captures RGB-D images. The on-device AI identifies the target (e.g., a specific component on a factory floor, a rare book on a shelf) and its 3D coordinates. The AI then computes the optimal laser projection parameters and precise location, transmitting this rich contextual data (object ID, 3D location, confidence scores, suggested actions) to the edge node for further processing or action execution.
  • Mermaid Diagram:
    graph TD
    A[PID Device with On-Device AI] --> B{RGB-D Sensor};
    A --> C{Dynamic Laser Pointer};
    A --> D{Wi-Fi 6E Comm. Module};
    A --> E[Embedded AI Inference Engine];
    B --> F[Capture RGB-D Image/Depth];
    F --> E;
    E --> G[Analyze Image/Depth for Target ID];
    E --> H[Predict User Intent/Adjust Laser];
    H --> C;
    E --> I[Identify Target & 3D Location];
    I --> J[Determine Precise Location (AI-Enhanced)];
    J --> K[Transmit Contextual Data to Edge Node];
    D --> K;
    K --> L[Edge Computing Node];
    L --> M[Further Processing/Action Execution];

Derivative 1.5: The "Inverse" or Failure Mode (Privacy-Preserving PID)

  • Enabling Description: A privacy-preserving pointing and identification device (PP-PID) is designed to operate with limited functionality or fail safely to protect sensitive information. In its default "privacy mode," the digital camera captures only highly pixelated, low-resolution grayscale images (e.g., 160x120 pixels, 4-bit grayscale) or transmits only quantized depth data without texture. The laser pointer is limited to a low-power, wide-beam "area illumination" mode (e.g., defocused 650nm laser, 1mW, beam spread to 10cm diameter at 1m) preventing precise single-point designation. The communication component implements end-to-end encryption (e.g., AES-256) and transmits only anonymized, aggregated metadata about object categories (e.g., "humanoid detected," "electronic device present") and general areas, rather than specific identities or precise coordinates. Upon detection of a privacy-triggering event (e.g., facial recognition of an unauthorized person, entry into a restricted zone via GPS), the PP-PID automatically switches to a "fail-safe" mode where the laser is deactivated, the camera captures no new data, and the communication component sends only a "privacy breach detected" alert. Target identification in privacy mode occurs only at a categorical level (e.g., "vehicle," "furniture") using embedded, privacy-by-design, differentially private machine learning models.
  • Mermaid Diagram:
    graph TD
    A[PP-PID Device] --> B{Low-Res Grayscale/Depth Camera};
    A --> C{Defocused Low-Power Laser};
    A --> D{Encrypted Comm. Module};
    A --> E[Privacy-by-Design ML Model];
    B --> F[Capture Pixelated Image/Quantized Depth];
    C --> G[Project Area Illumination Spot];
    F --> E;
    E --> H{Categorical Target Identification (e.g., "furniture")};
    H --> I[Transmit Anonymized Metadata];
    D --> I;
    A -- "Privacy Trigger (e.g., unauthorized face)" --> J{Fail-Safe Mode Activation};
    J --> C_OFF[Laser OFF];
    J --> B_OFF[Camera OFF];
    J --> D_ALERT[Comm. "Privacy Breach" Alert];

Independent Claim 12 Derivatives: Method for Identifying an Object

Claim 12: A method for identifying an object, comprising: providing a pointing and identification device (PID) comprising a laser and a camera; a user pointing the PID's laser at an object and activating a button on the PID; the PID capturing an image of the object via the camera; determining a context of the object based on the captured image, the context being selected from a group comprising a television screen, a computer screen, and a real-world object; and identifying the object based on the determined context.

Derivative 12.1: Material & Component Substitution (Acoustic Identification)

  • Enabling Description: A method for object identification utilizes a pointing and identification device (PID) equipped with a phased array ultrasonic transducer (PAUT) and an acoustic sensor array (e.g., MEMS microphone array). Instead of a laser, the user points a focused ultrasonic beam (e.g., 40 kHz, 10W peak power) from the PAUT at an object and activates a button. The PID then emits the ultrasonic pulse and simultaneously captures the reflected acoustic signature using the MEMS microphone array. A signal processing unit within the PID analyzes the time-of-flight, amplitude attenuation, and frequency shifts of the reflected sound waves to construct a 3D acoustic map of the object's surface and internal structure. The "context" (e.g., air, water, solid wall) is determined by the acoustic properties of the medium and the reflected signal characteristics. Object identification is performed by comparing the derived acoustic map against a database of known acoustic profiles using machine learning algorithms (e.g., recurrent neural networks), identifying, for example, structural integrity in industrial components, or material composition of submerged objects.
  • Mermaid Diagram:
    sequenceDiagram
    Actor A as User
    Participant P as PID (PAUT & Acoustic Sensor Array)
    Participant S as Signal Processing Unit
    Participant D as Acoustic Profile Database
    A->P: Point Ultrasonic Beam & Activate Button
    P->P: Emit Focused Ultrasonic Pulse
    P->P: Capture Reflected Acoustic Signature
    P->S: Transmit Acoustic Data
    S->S: Analyze Time-of-Flight, Attenuation, Freq Shift
    S->S: Construct 3D Acoustic Map
    S->S: Determine Acoustic Context (e.g., Air, Water)
    S->D: Query Database with Acoustic Map
    D-->S: Return Potential Matches
    S->A: Identify Object (e.g., "Faulty Weld," "Submerged Pipe")

Derivative 12.2: Operational Parameter Expansion (Deep Space Astronomical Object Identification)

  • Enabling Description: A method for identifying astronomical objects employs a pointing and identification device (PID) integrated with a robotic deep-space telescope array (DSTA) in low Earth orbit. The "laser" is represented by precision pointing of the DSTA's main optical axis. The "camera" is a cryogenic, highly sensitive astronomical CCD array (e.g., >1 gigapixel, cooled to -150°C) capturing long-exposure images across multiple spectral bands. The user "points" by providing celestial coordinates or selecting an area of interest via a ground station interface, activating a command to initiate observation. The DSTA captures images of a celestial region. The "context" (e.g., galaxy cluster, stellar nursery, exoplanet system) is determined by initial broad-field image analysis and spectral classification. Object identification (e.g., a specific quasar, a newly discovered supernova, an exoplanet with biosignatures) is achieved through advanced astrophysical modeling, comparison with astronomical catalogs (e.g., Gaia, SDSS), and machine learning classifiers operating on the spectral and morphological features of the captured data. The "precise location" would be its astrometric coordinates.
  • Mermaid Diagram:
    graph TD
    A[User (Ground Station)] --> B[DSTA Interface];
    B --> C[PID (Telescope Array)];
    C --> D{Astronomical CCD Array (Cryogenic)};
    C --> E{Precision Pointing System (Optical Axis)};
    D --> F[Capture Multi-Spectral Long-Exposure Images];
    E --> G[Point DSTA at Celestial Region];
    F --> H[Transmit Raw Astronomical Data];
    H --> I[Ground-Based HPC Cluster];
    I --> J{Initial Broad-Field Analysis};
    J --> K[Determine Astronomical Context];
    K --> L{Advanced Astrophysical Modeling & ML};
    L --> M[Compare with Astronomical Catalogs];
    M --> N[Identify Astronomical Object (e.g., "Quasar 3C 273")];
    L --> O[Determine Astrometric Coordinates];
    N & O --> P[Report to User/Scientists];

Derivative 12.3: Cross-Domain Application (Underwater Resource Exploration)

  • Enabling Description: A method for identifying underwater resources utilizes a pointing and identification device (PID) integrated with a Remotely Operated Vehicle (ROV) or Autonomous Underwater Vehicle (AUV). The PID employs a blue-green laser (e.g., 473 nm, 1W) with a wide-angle diffuser for illuminating underwater targets, and a low-light, high-dynamic-range subsea camera with a pressure-compensated lens system. The "pointing" is achieved by maneuvering the ROV/AUV and aiming the laser at a target. The PID captures images. The "context" (e.g., seabed, shipwreck, marine life) is determined by image segmentation and object detection algorithms adapted for underwater visibility conditions (e.g., accounting for turbidity and light absorption). Object identification (e.g., mineral deposits, specific species of deep-sea coral, archeological artifacts) is performed by a dedicated onboard AI processor comparing features against geological or biological databases. The laser spot provides precise localization for robotic sampling or detailed photographic surveys.
  • Mermaid Diagram:
    graph TD
    A[ROV/AUV with PID] --> B{Subsea Camera (Low-Light, HDR)};
    A --> C{Blue-Green Laser (Diffused)};
    A --> D{Onboard AI Processor};
    B --> E[Capture Underwater Images];
    C --> F[Illuminate Underwater Target];
    E --> D;
    D --> G[Image Segmentation & Object Detection];
    G --> H[Determine Underwater Context (e.g., "Seabed")];
    H --> I[Compare Features to Geo/Bio Databases];
    I --> J[Identify Underwater Resource/Object];
    J --> K[Log Location for Robotic Sampling/Survey];
    D --> L[Transmit Identified Data to Surface];

Derivative 12.4: Integration with Emerging Tech (Blockchain-Verified Asset Tracking)

  • Enabling Description: A method for identifying an object integrates blockchain for immutable record-keeping with a pointing and identification device (PID). The PID comprises a hyperspectral imaging camera (e.g., 400-1000 nm, 128 bands) capable of detecting unique material "fingerprints," and a structured light projector for precise 3D object mapping. A user points a coded laser (e.g., modulated with a unique sequence for device authentication) at a high-value asset (e.g., an artwork, a pharmaceutical batch) and activates a button. The PID captures hyperspectral and 3D images. On-device AI performs context determination (e.g., "warehouse inventory," "museum display") and object identification by correlating hyperspectral signatures and 3D geometry with digital twins stored on a blockchain. Each identification event, including the object's unique ID, timestamp, PID's verified ID (via coded laser modulation), and the precise 3D location, is then cryptographically hashed and recorded as a transaction on a distributed ledger (e.g., Hyperledger Fabric). This creates an auditable, unalterable chain of custody and verification for physical assets.
  • Mermaid Diagram:
    sequenceDiagram
    Actor U as User
    Participant P as PID (Hyperspectral Camera, Struct. Light, Coded Laser)
    Participant O as On-Device AI
    Participant B as Blockchain Network
    U->P: Point Coded Laser & Activate Button
    P->P: Capture Hyperspectral + 3D Images
    P->O: Transmit Image Data
    O->O: Determine Context (e.g., "Warehouse")
    O->O: Identify Object (Hyperspectral + 3D Fingerprint)
    O->B: Submit Identification Event (Object ID, Timestamp, PID ID, Location, Hash)
    B->B: Verify Transaction (Coded Laser Auth, Cryptographic Hashing)
    B-->O: Confirmation/Receipt
    O->U: Feedback: "Asset ID: XYZ Verified on Blockchain"

Derivative 12.5: The "Inverse" or Failure Mode (Limited-Disclosure Diagnostic PID)

  • Enabling Description: A method for object identification includes a limited-disclosure diagnostic mode in the event of system malfunction or security alert. The pointing and identification device (PID) comprises a low-power, wide-beam visible laser (e.g., 635 nm, 1mW, beam divergence 10 degrees) and a monochrome, low-resolution CMOS camera (e.g., VGA, global shutter). When operating in this diagnostic mode, the user points the diffuse laser at a malfunctioning system (e.g., a server rack, industrial machinery). The camera captures images, but object identification is restricted to a pre-programmed, coarse-grained library of "critical components" (e.g., "power supply unit," "network switch," "motor assembly"). The system explicitly avoids identifying specific models, serial numbers, or sensitive data. The "context" determination is limited to "internal system environment." Instead of a precise location, the PID indicates a "general area of concern" (e.g., "upper-right quadrant"). The device transmits only anonymized diagnostic codes (e.g., "Laser_Reflectivity_Low," "Image_Contrast_Degraded") and the coarse object category via a secure, one-way data diode to a dedicated diagnostic server, preventing any potential data exfiltration or precise location mapping during compromised operation.
  • Mermaid Diagram:
    graph TD
    A[PID (Diagnostic Mode)] --> B{Low-Res Monochrome Camera};
    A --> C{Low-Power Diffuse Laser};
    A --> D{Onboard Diagnostic Logic};
    B --> E[Capture Coarse Image];
    C --> F[Illuminates General Area];
    E --> D;
    D --> G[Limited Context Determination ("Internal System")];
    D --> H[Coarse Object Identification (e.g., "PSU")];
    D --> I[Generates Diagnostic Codes];
    H & I --> J[Transmit Anonymized Data (One-Way Diode)];
    J --> K[Dedicated Diagnostic Server];
    K --> L[Alert Maintenance Personnel];

Independent Claim 13 Derivatives: Method for Enabling a User to Act Upon an Item

Claim 13: A method for enabling a user to act upon an item, comprising: providing a pointing and identification device (PID) comprising a laser and a camera; a user designating an item by pointing the PID's laser at the item and activating a button on the PID; the PID recording an image of the item via the camera; the PID providing feedback to the user regarding the designated item; and the user responding to the feedback, wherein the response is selected from a group comprising confirming the designated item, refining the designation of the item, and selecting an action to perform on the item.

Derivative 13.1: Material & Component Substitution (Haptic/Auditory Feedback PID)

  • Enabling Description: A method for enabling user action utilizes a pointing and identification device (PID) designed for non-visual feedback. The PID includes an invisible near-infrared (NIR) laser (e.g., 980 nm, eye-safe Class 1) for pointing, and a NIR-sensitive global shutter camera. Instead of a visual display, the PID incorporates a localized haptic feedback module (e.g., an eccentric rotating mass motor array for directional vibration) and a directional audio transducer (e.g., ultrasonic speaker for parametric audio beaming). When the user designates an item with the NIR laser, the PID captures an image and identifies the item. Feedback is provided haptically (e.g., a specific vibration pattern indicating "object recognized") and audibly (e.g., a synthesized voice whispering the item's ID directly to the user's ear via the narrow audio beam). The user responds by specific gestural inputs detected by an inertial measurement unit (IMU) within the PID (e.g., a "nod" to confirm, a "shake" to refine) or by verbal commands captured by a noise-canceling microphone, triggering subsequent actions.
  • Mermaid Diagram:
    sequenceDiagram
    Actor U as User
    Participant P as PID (NIR Laser, NIR Camera, Haptic, Dir. Audio, IMU/Mic)
    Participant S as Image/Audio Processor
    Participant D as Object ID Database
    U->P: Point NIR Laser at Item & Activate Button
    P->P: Capture NIR Image
    P->S: Transmit Image
    S->S: Identify Item
    S->P: Feedback (Haptic Vibration, Parametric Audio ID)
    P->U: Experience Haptic/Audio Feedback
    U->P: Gestural Input (IMU) / Verbal Command (Mic)
    P->S: Transmit Response
    S->S: Interpret Response (Confirm/Refine/Action)
    S->A: Perform Action on Item

Derivative 13.2: Operational Parameter Expansion (Deep-Sea Robotic Manipulation PID)

  • Enabling Description: A method for enabling user action on items in extreme deep-sea environments uses a pointing and identification device (PID) integrated into a manned submersible or ROV/AUV control system. The "laser" is a high-power, subsea-rated blue laser (e.g., 450 nm, 5W) for long-range pointing through water, and the "camera" is a multi-beam sonar imaging system complemented by a low-light deep-sea camera with high-intensity LED array illumination. The user designates an item (e.g., a hydrothermal vent anomaly, a fragile organism) by visually aiming the laser from the submersible or guiding the ROV/AUV to point the laser, then activating a control. The PID records a 3D sonar image and optical image. Feedback is provided on a high-resolution, pressure-tolerant display inside the submersible, showing a 3D reconstruction of the target with an overlaid identification (e.g., "Sulfide Chimney," "Tube Worm Colony"). The user responds via joystick controls or a specialized human-machine interface to confirm, refine the designation (e.g., outlining a sub-section of the vent), or select a robotic arm action (e.g., "collect sample," "deploy sensor," "observe with IR camera").
  • Mermaid Diagram:
    graph TD
    A[Submersible/ROV PID] --> B{Subsea Blue Laser};
    A --> C{Multi-Beam Sonar + Deep-Sea Camera};
    A --> D{Pressure-Tolerant Display};
    A --> E{Control Interface (Joystick/HMI)};
    B --> F[Point Laser at Deep-Sea Item];
    C --> G[Record Sonar/Optical Image];
    G --> H[Image/Sonar Processing];
    H --> I[Identify Item (e.g., "Vent Anomaly")];
    I --> D[Display 3D Model with ID];
    E --> J[User Response (Confirm/Refine/Action)];
    J --> K[Execute Robotic Arm Action];

Derivative 13.3: Cross-Domain Application (Smart City Infrastructure Management PID)

  • Enabling Description: A method for enabling user action in smart city infrastructure management utilizes a pointing and identification device (PID) deployed by maintenance crews. The PID comprises a range-gated LiDAR system (for precise distance and 3D mapping) and a visible-light high-resolution camera. The user designates a specific piece of infrastructure (e.g., a faulty streetlamp, a blocked drain, a damaged public sensor) by pointing the PID and activating a trigger. The PID captures LiDAR point cloud data and a visual image. Feedback is provided via an augmented reality (AR) overlay on the PID's display, which superimposes relevant data (e.g., maintenance history, sensor readings, component specifications, recommended repair procedures) onto the live view of the designated item. The user responds by interacting with the AR interface (e.g., using gaze tracking or integrated buttons) to confirm the issue, log a repair request into the city's asset management system, or select a remote diagnostic action (e.g., "reboot smart sensor," "query traffic flow data from intersection").
  • Mermaid Diagram:
    graph TD
    A[Maintenance Crew PID] --> B{Range-Gated LiDAR};
    A --> C{High-Res Visible Camera};
    A --> D{AR Display};
    A --> E{Interaction Buttons/Gaze Track};
    B & C --> F[Capture LiDAR Point Cloud + Image];
    F --> G[Process Data & Identify Infrastructure Item];
    G --> H[Query City Asset Management System];
    H --> I[Retrieve Item Data/History];
    I --> D[AR Overlay Feedback on Live View];
    E --> J[User Response (Confirm/Log Request/Remote Action)];
    J --> K[Update Asset Management System / Trigger Remote Action];

Derivative 13.4: Integration with Emerging Tech (IoT-Enabled Predictive Maintenance PID)

  • Enabling Description: A method for enabling user action integrates IoT sensors and AI-driven predictive analytics with a pointing and identification device (PID). The PID comprises a thermal imaging camera (e.g., microbolometer array) for detecting heat signatures and a low-power laser rangefinder. The user designates an industrial component (e.g., a motor, a pump, an electrical cabinet) by pointing the PID's laser rangefinder at it and activating a button. The PID captures a thermal image and precise range. On-device AI (e.g., a small CNN) analyzes the thermal signature to identify potential anomalies (e.g., overheating bearings, loose electrical connections). Simultaneously, the PID queries nearby IoT sensors (e.g., vibration, current, acoustic) via a local LoRaWAN gateway. Feedback is provided on the PID's display, showing the thermal image with identified hot spots, predicted remaining operational life (from AI analytics correlating thermal data and IoT sensor data), and suggested maintenance actions. The user responds to this predictive feedback by confirming the anomaly, scheduling proactive maintenance (updating a CMMS via the PID's communication component), or initiating a remote diagnostic routine on the IoT-connected component.
  • Mermaid Diagram:
    sequenceDiagram
    Actor U as User
    Participant P as PID (Thermal Cam, Laser Rangefinder, LoRaWAN, On-Device AI)
    Participant I as IoT Sensors (Vibration, Current, Acoustic)
    Participant L as LoRaWAN Gateway
    Participant C as Cloud Predictive Analytics
    U->P: Point Laser at Component & Activate
    P->P: Capture Thermal Image + Range
    P->P: On-Device AI Analyze Thermal Anomaly
    P->L: Query Nearby IoT Sensors (via LoRaWAN)
    L->I: Request Sensor Data
    I-->L: Transmit Sensor Data
    L-->P: Forward Sensor Data
    P->C: Transmit Thermal, Range, Sensor Data
    C->C: Correlate Data for Predictive Maintenance
    C-->P: Return Predicted RUL & Suggested Actions
    P->U: Display Thermal Image, RUL, Suggested Actions
    U->P: Confirm Anomaly / Schedule Maintenance / Remote Diagnostic
    P->C: Update CMMS / Trigger Remote Action

Derivative 13.5: The "Inverse" or Failure Mode (Low-Power Fail-Safe Action PID)

  • Enabling Description: A method for enabling user action incorporates a low-power, fail-safe mode for critical operational contexts, such as emergency situations or battery depletion. The pointing and identification device (PID) comprises a monochromatic camera (e.g., low-power CMOS, 30fps) and a dim, always-on LED indicator instead of a laser (providing only a general aiming guide). When the PID enters "low-power mode" (e.g., battery <10%) or "emergency mode" (triggered by an external signal), its functionality for action selection is severely curtailed. The user designates an item (e.g., an emergency exit, a safety valve) by pointing the LED indicator and activating a button. The PID captures a low-resolution monochrome image. Feedback is simplified to only binary confirmation (e.g., "Exit Identified: Yes/No," "Valve Identified: Open/Closed") displayed as large, high-contrast text or auditory tones. The range of available actions is pre-filtered to only "safe" or "critical" actions (e.g., "Open Valve," "Signal Help," "Record Event," "Lockdown"). Any attempt to perform complex or non-critical actions is blocked, and the system prompts the user to either confirm one of the limited safe actions or cancel, preventing erroneous commands under duress or power constraints.
  • Mermaid Diagram:
    sequenceDiagram
    Actor U as User
    Participant P as PID (Monochrome Camera, LED Indicator, Low-Power Logic)
    P->P: Enter Low-Power/Emergency Mode
    U->P: Point LED at Item & Activate Button
    P->P: Capture Low-Res Monochrome Image
    P->P: Identify Item (Simplified Logic)
    P->P: Filter Actions to "Safe/Critical" Only
    P->U: Display Binary Confirmation/Limited Actions
    U->P: Select Confined Action / Cancel
    P->P: If Action Selected: Execute Safe Action
    P->P: If Cancel: Return to Idle

Combination Prior Art Scenarios

  1. US8471812 + OpenCV for Image Recognition and Calibration:
    The core functionality of US8471812, particularly the identification of a target based on a captured image (Claim 1 and 12) and determining the precise location, can be implemented and enhanced using the open-source OpenCV (Open Source Computer Vision Library). OpenCV provides robust algorithms for feature detection, object recognition (e.g., using SIFT, SURF, ORB, or pre-trained deep learning models), camera calibration, and image processing tasks like laser spot detection. For example, the PID's camera could capture images, and OpenCV functions could perform lens distortion correction, then use template matching or object detection to identify the target, and finally, precisely locate the laser spot using centroid detection or Hough transforms, especially after differentiating the laser-on/off images (as described in the patent). This combination makes the image processing and recognition aspects of the patent obvious in light of widely available open-source computer vision tools.

    • Open Source Standard: OpenCV (e.g., version 4.x)
    • Relevant Claims: Claims 1, 12, 13 (image capture, target identification, location determination, feedback generation).

  2. US8471812 + MQTT for IoT and Remote Control Integration:
    The communication component and transmission of information from the PID to an external computer system (Claim 1) or for enabling user actions (Claim 13) can be seamlessly integrated using MQTT (Message Queuing Telemetry Transport), an ISO standard (ISO/IEC PRF 20922) lightweight messaging protocol for small sensors and mobile devices. A PID could publish identified item data and precise locations as MQTT messages to a broker, which then relays this information to various subscribing applications, IoT platforms, or remote control systems. For instance, after a user designates a piece of machinery with the PID (Claim 13), the PID could publish an MQTT message containing the machine ID and the chosen action (e.g., "start_motor", "read_diagnostics") to a topic like /factory/machine_id/command. A subscribing PLC or cloud application could then execute the action. This makes the wireless communication and remote actuation aspects of the patent obvious when combined with standard IoT protocols.

    • Open Source Standard: MQTT (e.g., Eclipse Mosquitto broker, Paho MQTT client libraries)
    • Relevant Claims: Claims 1, 13 (communication component, transmitting information, selecting an action).

  3. US8471812 + ROS for Robotic Interaction and Digital Space Management:
    The broader concept of a Reference Framework (RFW) that actively tracks objects and allows a PID to designate objects for an RFW (as mentioned in the patent description, though not explicitly in the independent claims provided) and the enabling of user actions (Claim 13) can be realized using the Robot Operating System (ROS). A PID (e.g., a handheld device) could communicate with a ROS-enabled robotic platform (which could act as an RFW) via standard ROS messages. For example, when a user points the PID at an object (Claim 13) and selects an action like "pick up," the PID could publish a /object_designation message containing the object's identified ID and precise 3D pose, and a /robot_command message with the "pick_up" instruction. A subscribing ROS node on a robotic arm could then interpret these messages to autonomously manipulate the designated item. This integrates the PID into a dynamic, robotic digital space, making complex interactions obvious with an open-source robotics framework.

    • Open Source Standard: ROS (Robot Operating System, e.g., ROS 2)
    • Relevant Claims: Claims 1, 13 (transmitting information, selecting an action, interaction with external systems).

Generated 5/17/2026, 12:48:48 AM