US20200154696A1

US20200154696A1 - Methods and systems for discriminately dispatching birds within a predefined area

Info

Publication number: US20200154696A1
Application number: US16/685,720
Authority: US
Inventors: Brian Carnell
Original assignee: Birdbrain; Birdbrain Inc
Current assignee: Birdbrain; Birdbrain Inc
Priority date: 2018-11-16
Filing date: 2019-11-15
Publication date: 2020-05-21
Also published as: US20200154695A1; US11291198B2; US20200156784A1

Abstract

Systems and methods for dispatching pest animals such as birds, rodents and other predefined pest animals from a particular area using computer vision and artificial intelligence to identify the pest and machine learning to continuously improve the system. The system is set to continuously monitor its predefined area for motion and an electronic system is used to collect electronic representation of an intruder and submit the representation to the system for processing. Based on the results of its identification, the system will make an intelligent decision as to whether to activate the dispatch mechanism.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. Patent Provisional Application No. 62/768,516, U.S. Patent Provisional Application No. 62/768,542, U.S. Patent Provisional Application No. 62/768,564, U.S. Patent Provisional Application No. 62/768,588, U.S. Patent Provisional Application No. 62/768,718, U.S. Patent Provisional Application No. 62/768,638, and U.S. Patent Provisional Application No. 62/768,653, all filed on Nov. 16, 2018. These and all other referenced extrinsic materials are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The field of the invention is automated pest deterrence.

BACKGROUND

The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
The need to dispatch pest animals which find their way into warehouses and industrial buildings or that congregate on electrical equipment such as substations is well known. Today the most efficient mechanism for dealing with these intruders is a maintenance employee with a pellet gun or traps that indiscriminately use force or electricity to dispatch animals.
Pest birds and other pest animals, cost businesses hundreds of millions every year by causing contamination to food and other consumables which can lead to diseases in humans such as avian influenza. Fecal deposits left behind by pest animals can contaminate food supplies causing sickness and death. As well as consumable contamination, intrusion by the pest animals entering areas where machinery and electrical equipment is housed can cause destruction of property costing the operator for repairs and in some cases replacement.
There are many different systems available today for dispatching unwanted pests, but all have their drawbacks and limited effectiveness when it comes to being discriminate about the activation of their mechanisms. Some of these systems utilize electrical shock to control and dispatch pest animals, but these systems are subject to moisture and can endanger people when they are not properly installed or are encountered by humans. Other systems rely on indiscriminate trigger mechanisms and do not have the capability to observe their targets and make intelligent decisions as to whether to activate the dispatch mechanism.
For example, in U.S. Pat. No. 5,949,636 titled “Portable pest electrocution device with resistive switch trigger” to Johnson, a system was devised that would electrocute a pest when a resistance switch was activated. The system utilizes a firing mechanism that is activated by a leakage of electricity. This device is effective for the dispatch but does not discriminate as to what would be causing the leakage, thereby endangering a human who might not be familiar with the mechanism itself, such as a child.
UK Patent No. GB2480496A teaches using a camera to capture images of an area frequented by honeybee pests and utilizes pattern recognition algorithms to locate, identify and track the position of a target pest, and then applies a laser beam directly to the pest of sufficient intensity and duration as to generate enough heat locally to either kill or incapacitate it. Chinese Patent No. CN202890329U to Wu et al, teaches an automatic aiming laser bird repellent device based on pattern recognition. However, lasers are often ineffective especially during bad weather conditions. Moreover, neither of them teaches using artificial intelligence system to deter birds.
Thus, there is still a need for a system that discriminately dispatching birds within a predefined area.
All publications identified herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

SUMMARY OF THE INVENTION

The present invention uses a camera, machine learning, and artificial intelligence to determine if a predefined target pest animal exists within the field of vision of the system, and then targets and dispatches the pest animal based on a predefined set of parameters.
In one embodiment, an automated system for targeting and dispatching a bird within the system's field of view comprises:

- a. Electronically detecting the bird with computer vision;
- b. Software for identifying the bird;
- c. Software to dis-engage the unit when something other than a predefined bird is moving in its field of view.
- d. Software for targeting the bird; and
- e. Software to activate the system for dispatching the bird (electrocution or projectile or any other method) when positively identified;

In another embodiment, a rodent is dispatched within a building, room or other defined space, comprising:

- a. A defined space having an opening having a size that limits entry of various pest animals;
- b. Electronically detecting the pest animal;
- c. Software for identifying the pest animal;
- d. Software to dis-engage the unit when something other than a predefined pest animal is moving in its field of view.
- e. Software to target the pest animal; and
- f. Software to activate the system for dispatching the pest animal when positively identified.

Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic showing use of an AI system to dispatch a pest animal.

FIG. 2 is a schematic of a motion sensor detecting a pest animal in a defined space.

DETAILED DESCRIPTION

In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints, and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.
The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value with a range is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.
The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.
As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously.
As used herein, the term “automated system” refers to a system which detects birds in the digital cameras field of vision, targets the pest animal, dispatches the intruder and then notifies personnel to dispose of the dispatched bird.
As used herein, the term “flying animal” refers to any flying animal, including birds, bats, and the like, which are large enough to be a nuisance on a given piece of property.
As used herein, the term “particular area” refers to an area wherein the user of the system wants to keep the area free from invasion of pest animals. This might be an industrial building, an airport terminal, a commercial building, or the like.
As used herein, the term “Artificial Intelligence” refers to a software system used for accessing and determining the positive identification of a pest animal using a knowledge dataset before engaging dispatch mechanism.
As used herein, the term “Machine Learning” refers to the system's ability to learn what is and is not a pest animal with and without input from an operator. While the learning mode can be automatic, in one embodiment, a user has the ability to initiate the systems training mechanism and positively identify pest animals to build upon its intelligence.
As used herein, the term “software that operates the system” refers to software on a computer, on the web, on the endpoint, or any other place which coordinates the activity of detecting the location within the predefined target area and of the existence of a pest animal within.
In use, the system monitors inside the predefined target area for anything in motion in that area. In one embodiment, it is a recognition system that can tell the difference between a pest animal and some other movement such as a hand being placed inside the target area. If a pest animal is detected, the system is activated and directed to dispatch the pest animal.
As used herein, the term “dispatch” means to kill the pest animal.
In FIG. 1, a stand 101 for a bird 103 to perch on is situated directly in front of the electronic camera system 102 monitoring for motion. When motion is detected the image processor 121 processes the image of the bird 103 and uses an artificial intelligence system 122 to identify the object in its field of vision. If the AI system 122 determines it is a pest animal, a command is sent to activate a dispatch mechanism (e.g., drone 123). When the dispatch is completed the pest animal 103 is then dropped from the mechanism into a collection point and the system 100 resets and notifies the operator of its action.
In FIG. 2, an electronic camera 210 defines a target area 212 in a contained unit 216. A motion detector system 213 operates to detect a pest animal 215 inside the predefined area 212. The system 200 detects the existence of the pest animal 215 in a contained unit 216 with the opening 217 which allows for ingress and egress of the pest animal 215 into the target area 212.
It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Claims

What is claimed is:

1. An automated system for detecting a pest animal, comprising:

a digital camera that electronically captures images of its visual area; and

a processor configured to execute software instructions stored on a non-transitory computer-readable medium, wherein the software instructions are configured to continuously monitor the images and to identify the pest animal from the images;

and send a dispatch signal when the pest animal is identified.

2. The automated system in claim 1, wherein the automated system is capable of discriminately activating a deterrent device.

3. The automated system in claim 1, wherein the digital camera continuously captures images of its visual area.

4. The automated system in claim 1, wherein the automated system is trained to manually identify pest animals.

5. The automated system in claim 1, wherein the automated system is trained to automatically identify pest animals.

6. The automated system in claim 1, further comprising a motion detector.

7. The automated system in claim 1, further comprising using a software system to identify the pest animal by using a knowledge dataset.

8. The automated system in claim 1, further comprising using machine learning to continuously improve identification accuracy of the automated system.

9. The automated system in claim 1, further comprising using a projectile to dispatch the pest animal.

10. The automated system in claim 1, further comprising using electrocution to dispatch the pest animal.

11. A method for dispatching pest animals inside of a particular area, comprising:

electronically defining a particular area with computer vision;

electronically detecting with computer vision when there is a pest animal inside the particular area;

engaging a dispatch mechanism associated with a positive identification of the pest animal inside the particular area; and

disengaging the dispatch mechanism when there is a negative identification of the pest animal inside the particular area.

12. The method according to claim 11, wherein detection is accomplished by utilizing computer vision.