# US 2019/0166823 A1 · Selective Action Animal Trap

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- Number: US 2019/0166823 A1
- Document ID: US20190166823A1
- Status: Published application
- Kind: A1
- Application number: 16/121,703
- Filed: 2018-09-05
- Published: 2019-06-06
- Earliest claimed: 2017-12-04
- Assignees: Caldera Services LLC
- Inventors: Aaron Dick
- Web page: https://patentgrep.com/patent/US20190166823A1

## CPC

- A01M 31/002: {Detecting animals in a given area}
- A01M 23/02: Collecting-traps
- A01M 23/18: with pivoted closure flaps
- A01M 23/38: Electric traps
- A01M 29/16: using sound waves
- A01M 29/24: using electric or magnetic effects, e.g. electric shocks, magnetic fields or microwaves
- G06F 18/2413: based on distances to training or reference patterns
- G06F 18/24133: {Distances to prototypes}
- G06F 18/2431: Multiple classes
- G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]}
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06V 40/103: {Static body considered as a whole, e.g. static pedestrian or occupant recognition}
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- A01M: CATCHING, TRAPPING OR SCARING OF ANIMALS (appliances for catching swarms or drone-catching A01K57/00; fishing A01K69/00 - {A01K99/00}; biocides, pest repellants or attractants A01N); APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A method and a system provide an animal trap that records digital images of animals with a camera, convolves the digital image with a kernel to create convolved feature maps that are used as input to a classifier algorithm, producing classification confidence scores that identify the animals. An algorithm categorizes the classified animal and selects an action based on the categorization. The trap has actions to deter the benign or beneficial animals and actions to detain or kill the pest animals. With this method and system, the trap is able to target pest animals with minimal harm to benign or beneficial animals.

## Claims

### Claim 1

A selective action animal trap system comprising one or more digital cameras located in proximity to a trap, connected to computer processing circuitry configured to process the image data into convolution feature maps, that are further processed by computer processing circuitry into animal classification confidence scores, and selecting the action of the trap to one of; no action, deter, detain or kill based on the classification confidence scores.

### Claim 2 (depends on 1)

The system of claim 1 wherein the computer processing circuitry executes a pre-trained convolutional neural network classifier or region-based convolutional neural network object detector.

### Claim 3 (depends on 1)

The system of claim 1 wherein the trap action detains an animal in a wire cage trap assembly by closing one or more trap doors.

### Claim 4 (depends on 1)

The system of claim 1 wherein the trap action deters or kills an animal by electrical shock, pressure waves, percussion, or electromagnetic radiation.

### Claim 5 (depends on 1)

The system of claim 1 wherein the said computer processing circuitry sends and receives network messages with a remote user containing current and historical operational state information.

### Claim 6

A method for trapping an animal, comprising:
acquiring a set of digital images of one or more animals,
by way of convolution and classification algorithms run on a computer, process the said digital image set to train the algorithm parameters to create a pre-trained convolution and classification algorithm,
acquiring digital images from a camera located in proximity to a trap,
by way of the said pre-trained convolution and classification algorithm run on a computer, produce animal classification confidence scores for each image,
by way of a trap action algorithm run on a computer, selecting the trap action to; no action, deter, kill or detain based on the animal classification confidence scores.

### Claim 7 (depends on 6)

The method of claim 6 wherein the said convolution and classifier algorithms are applied to more than one Regions of Interest (ROI) within the digital image and assigning animal classification confidence scores to each ROI.

### Claim 8 (depends on 6)

The method of claim 6 wherein the said convolution and classifier algorithms are a convolutional neural network classifier or region-based convolutional neural network object detector.

### Claim 9 (depends on 6)

The method of claim 6 wherein the trap action detains an animal in a wire cage trap assembly by closing one or more trap doors.

### Claim 10 (depends on 6)

The method of claim 6 wherein the trap action deters or kills an animal by electrical shock, pressure waves, percussion or electromagnetic radiation.

### Claim 11 (depends on 6)

The method of claim 6 wherein the said computer sends and receives network messages with a remote database or user containing current and historical operational state information.

### Claim 12 (depends on 6)

The method of claim 6 wherein the said trap action algorithm incorporates information from network information services and databases.

### Claim 13 (depends on 6)

The method of claim 6 wherein the said computer algorithms are replaced or modified by over-the-air network updates.

### Claim 14

A method for trapping an animal, comprising:
acquiring digital images from a camera located in proximity to a baited wire cage trap assembly with open trap doors,
by way of a software program run on a computer, analyzing the said digital images using a Gaussian Mixture Model (GMM) motion detector to determine if an animal is present,
by way of a software program run on a computer, when said animal is detected by said GMM motion detector, further processing the digital images with a pre-trained convolution neural network and classification algorithm to generate animal classification confidence scores,
by way of a software program run on a computer, comparing the said animal classification confidence scores to threshold values and actuating the trap if the score of one or more animal classes exceeds the threshold value,
by way of a software program run on a computer and trap control system, selecting the trap action to detain pest animals by closing the trap doors, or deterring non-pest animals from entering the trap by electrifying the cage to deliver a non-lethal shock.

### Claim 15 (depends on 14)

The method of claim 14 wherein the convolution neural network and classification algorithm is pre-trained on a set of animal digital images to have classification confidence score accuracy expressed as a probability of more than 70% for each animal class.

### Claim 16 (depends on 14)

The method of claim 14 wherein the said computer sends and receives network messages containing current and historical operational state information with remote databases and users.

### Claim 17 (depends on 14)

The method of claim 14 wherein the said computer software programs are replaced or modified by over-the-air network updates.