U.S. Patent Position Analysis

Section 02 — Patent Architecture

Core Existing Patent Architecture

System and Method of Determining Unusual or Emergency Occurrences Within a Livestock Property. The invention is not merely tracking cows: it is the orchestration of a property model, a livestock model, distributed sensing and external information into event determination, severity, visualisation, alerting and remedial response.

Architecture at a glance

Property modelLivestock modelSensorsExternal data
  1. Event determination layer
    ↓
  2. AI / machine intelligence
    ↓
  3. Severity intelligence
    ↓
  4. Property visualisation
    ↓
  5. Alert orchestration
    ↓
  6. Remedial action engine

Layer A

Livestock Property Model

Property boundary → defined zones within the property → fixed property features.

  • Property boundary
  • Defined zones within the property
  • Fencing
  • Roads
  • Sheds
  • Water outlets
  • Water storage
  • Feed storage

Layer B

Livestock Model

Livestock associated with the property → livestock associated with particular zones → identification of number and location of livestock relevant to an occurrence.

  • Livestock associated with the property
  • Livestock associated with particular zones
  • Number of livestock relevant to an occurrence
  • Location of livestock relevant to an occurrence

Layer C

Sensor / External Data Layer

Multiple sensors distributed across the property and/or external third-party information services. The published claims expressly contemplate an external emergency authority accessible through an API enabling live data exchange.

  • Multiple distributed property sensors
  • External third-party information services
  • External emergency authority accessible through an API
  • Live data exchange

Layer D

Event Determination Layer

Processing sensor and external data to determine whether an unusual occurrence or emergency condition has occurred or is occurring.

  • Unusual livestock movement
  • Cessation of movement
  • Livestock entering another zone
  • Missing livestock
  • Damaged fixed feature
  • Wildfire
  • Flood
  • Road blockage
  • Earthquake
  • Water damage
  • Extreme weather

Layer E

AI / Machine Intelligence Layer

The specification describes application of AI techniques to determine the most likely unusual occurrence or emergency situation, and describes machine-learning feedback embodiments.

  • AI determination of most likely occurrence
  • Multi-source information reconciliation
  • Machine-learning feedback and training embodiments

Layer F

Severity Intelligence

A particularly important differentiator: severity determined from event and property context rather than a single sensor threshold.

  • Event category
  • Number of livestock
  • Number of humans
  • Proximity of livestock and humans to the event
  • Property / zone location
  • Nearby fixed features capable of worsening the event
  • Resources capable of addressing the event
  • Location of emergency services

Layer G

Property Visualisation

Graphical interface presenting the property and the determined event in spatial context.

  • Land area
  • Property boundary
  • Zones
  • Fixed features
  • Location of event
  • Type of event

Layer H

Alert Orchestration

Generation of alerts relating to detected unusual occurrences and emergencies, where alert level can reflect severity.

  • Alert generation
  • Severity-reflective alert level

Layer I

Remedial Action Engine

Potential generation of recommended remedial actions. One of the major areas of technical whitespace highlighted throughout this mapping.

  • Event type
  • Severity
  • Emergency services
  • Distance and location of emergency services
  • Relevant property resources
  • Fixed features useful in addressing the emergency

Illustrative embodiment

Resource-aware emergency intelligence

  1. Fire detected
    ↓
  2. Where are livestock?
    ↓
  3. What property zones are threatened?
    ↓
  4. What fixed features increase risk?
    ↓
  5. Where is available water?
    ↓
  6. Where are emergency services?
    ↓
  7. What action is appropriate?

Illustrative embodiment based on the patent architecture. This is not proof of an implemented commercial system.

Severity chain

Severity is contextual, not a threshold

  1. Event
    ↓
  2. Animals
    ↓
  3. Humans
    ↓
  4. Location
    ↓
  5. Infrastructure
    ↓
  6. Resources
    ↓
  7. Emergency services
    ↓
  8. Severity score

Whitespace flag

The remedial action engine is one of the major areas of technical whitespace highlighted throughout the competitive mapping. Detection alone does not reach it.