The new way of identifying micro-organisms
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The new AI technology platform identifies micro-organisms, and matter (pollution) from indoor and outdoor surfaces, or microbes and matter that have become airborne.  Our technology can also identify microbes and matter on a seabed, in space or in motion such as those on a moving object such as a ball.

Among many applications, Decontaminate Inc, an Artificial Intelligence company incorporated in Delaware utilizes new technology formed from 2019 to 2021.  The patent applications filed with United States Patent and Trade Office “USPTO” and the International Patent Cooperation Treaty office “PCT” started in early 2019, before the pandemic.  There are many different families of artificial intelligence and machine learning patents that were filed including nano-technology applications.

Our artificial intelligence technology platform is basic in understanding but detailed in operation.

The basic understanding of the Artificial Intelligence Machine Learning Micro-Organism Identification Application “AIMMOIA” can identify threats with 98%.  The software consists of many specific algorithms that manage and learn through machine learning and artificial intelligence.  The hardware consists of but is not limited to a platform (or connected platforms) that are entirely managed by artificial intelligence algorithms whereby drones, robots and mechanical arms place and collect microscope slides (and plates of various materials) from the ground, ceilings, walls and tunnels on conveyor belts for large pools.

Drone and Robot Direct Wireless Charging Systems
" DWCS " and " WPT
Eliminating the need to replace or interrupt scans to recharge batteries
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The System Implements and manages power sources, power generation and power supply, recharging of chargeable devices, transfer of power to and from storage devices, powering drones and robots with power storage devices for a surface and airborne micro-organism and matter identification system using artificial and machine learning algorithms. The entire application including all energy sources, power generation components, hardware, software, mobile components and ancillary hardware components is called  “ AIMlapp ”. With many shortened scanning runs because the batteries were needed to be replaced or taken out of service for recharging, our new charging system for drones and robots eliminates the need for downtime and utilizes a continuous charging system for both drones and robots and some of their sensors.

Micro-Organism Identifying Applications from the past
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The challenge in the past was getting an accurate reading on surface for airborne matter.  The overriding issue was obtaining the sample without it being tainted and secondarily transporting the sample to a lab.  Technology in the past did not have artificial intelligence combined with mobile applications for immediate application.

The time needed in the past for microbe identification was between 24 to 48 hours where the initial sample taken for examination needed to be obtained without  disruption (tainting) and then transported to a lab.  That time between the moment the sample was taken and the final result depending on sophistication of equipment used and man-powered involved could be as long as a week.

Most of the time involved was due to the time the microbe needed to incubate (or grow) in a petri dish. That has all changed with the invention of AIMMOIA.

Artificial Intelligence Micro- Organism and Matter Identification Application “AIMMOIA”
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How to obtain micro-organisms  and matter without contaminating the sample.

One of our applications for obtaining samples before identification is by using static electricity to attract microbes and matter to plates and slides.  Other times we will use magnetics, sticky proteins or just air flow.

Once the sample is obtained, the drones and robots retrieve the slide and place the slide on a conveyor belt.  The conveyor belt has 4 types of applications for identity, lenses and condensers relating to light and electron microscopes, x-ray machines and NMR machines.  The lenses stream high definition pictures to laptops, cellphones, tablets and servers which then processes through a library of identification algorithms.  If more data is required, we use cantilevers with fiber optic beams.

Many forms of Data Points
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The best way to obtain accurate data is to obtain many data samples.  Our application utilizes millions of data samples to ascertain what singular form (and clusters thereof) of microbes and matter from pictures and or weight.  Our applications also absorb data on behaviors of people, animals, buildings and environments. Some buildings are sick while others cause pollution in adjacent structures.  An important component of our application is the behaviors of people.  One basic behavior that our application notes is if the toilet seat is left up during flushing which is the result of feces and urine detected outside of the restroom.  Some data can be obtained by our application scanning security tape of behaviors in and outside of residences and businesses such as if shoes are left at the door. The machine learning algorithms also learn to predict outcomes from indoor and outdoor environments.

Under Water, Above Water and in Space
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Space component

The behaviors of micro-organisms in the international space station are different due to microgravity. How they form, grow, survive, and how to identify and eliminate them is the subject of this paper.  Our artificial intelligence platform identifies and understands how to eliminate micro-organisms that are not on earth.  The  AI platform  as constructed for the International space station “ISS" consists of three components: software, hardware and manual or automated applications. The elimination applications utilize biosurfactants to eliminate microbes and create a non-stick surface eliminating microbial buildup.  The software consists of artificial intelligence and machine learning algorithms that learn about space, hardware components such as folded mirror lenses and conveyor belts.  The application can be implemented manually or be managed by the artificial intelligence platform. The platform will catalog “all” microbes that are lurking on surfaces and in the air in the ISS.

The ISS is a closed system but is susceptible to whatever enters the system such as microbes from payloads, humans and what are within and on the outside of humans. The ISS is subject to microgravity, radiation, and carbon dioxide”  Microbes are known to survive and even thrive in extreme environments, and the microbes that are present on the ISS may have existed since the inception of the ISS while others may be introduced each time new astronauts or payloads arrive.

Since the beginning of the ISS, routine microbial monitoring of surfaces, air, and water has occurred using culture-based techniques as per the National Aeronautics and Space Administration’s (NASA) operations and maintenance requirement procedures.  The process is antiquated, in accurate and prone to tainting of the sample.

How we will enable NASA to accurately detect and eliminate pathogens and microbes that are airborne and on surfaces is by using exascale testing with conveyors belts, drones and robots.  The way our technology works is by having the slides on the conveyor belts having a static electricity charge to attract microbes and matter to the slide and plates on the conveyor belts for accurate identification.

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Underwater  -Lake beds and the sea floor

Unmanned underwater (or undersea) vehicles using artificial intelligence and machine learning for the identification of underwater micro-organism, matter and pollution identification.  The AI/ML application scours the floor of large bodies of water for micro-organisms, matter and pollution using unmanned robots that either crawl the floor or propel themselves through the open water. The website is  “ WAter Microbe Identification Application” and is currently under construction.


The underwater application utilizes artificial intelligence and machine learning algorithms, robots, underwater direct wireless charging systems “UDWCS” (or tethered powered sources to the charging pads or directly to the robots from ships above), three tiered (side by side) closed containers with conveyors belts, cantilevers, high definition lenses (with and without condensers) on either the robots and conveyor belts. The pictures are then transferred by wire or wirelessly to the ship on the surface of the water or to servers on land.


The application can work in two distinct ways, one by robots embedded with high definition lenses or by conveyor belts and cantilevers in a closed container for more accurate microbes and matter seafloor identification.  The application is also designed to use both at one time. 


The underwater robots have various components. A high definition lens (and condenser) embedded in the bottom of the robot, the ability to crawl the ocean floor (tracks and wheels) or travel through the water (rudder and propellers) and the option of a tethered cord from a ship from the surface or a UDWCS.


For accurate identification of underwater microbes, matter and pollution, the application uses a 3-tiered closed conveyor belt system. The container can also be powered the same way the robots are powered.  The robot with a mechanical arm (opened or closed box on arm) for obtaining underwater samples enters the closed container. That part of the container closes and evacuates the water. Then the robot moves into the second part of the container which is dry, has the conveyor belts with high definition lens and transmission equipment for accurate identification as described in our other USPTO applications. The robot opens the microbe trap box on the mechanical arm and places the sample on the conveyor belt for identification using light, electron microscopes or X-ray machines. After identification, the system is cleaned with water, the water is evacuated and the robot(s) exits the second part of the container to the third where another application may commence such as obtaining the weight of microbe using cantilevers.  The AI/ML platform determines if cantilevers or more testing is required.


The way in which the AI/ML algorithms work are by learning about underwater environments, their occurrences, the behaviors of the underwater life and how underwater micro-organisms, matter and pollution have an effect and all aspects of the water bed (seafloor). 


The robot can be powered directly from the ship (tethered), from the floor using UDWCS or power itself.  The robot can be powered by batteries or by nuclear power.


The size of the application can nanotechnology, normal sized or a combination of both.