Using artificial intelligence combined with nanotechnology for securing, manipulating and positioning microorganisms for accurate identification.
Our artificial intelligence platform utilizes combined machine learning algorithms and nanotechnology components to attract, secure, manipulate and position pathogenic and non-pathogenic microorganisms for identification using images, weight, circumference, height, and length. The components in the artificial intelligence platform are machine learning algorithms, exascale, manned and unmanned drones and robots, high magnification lenses, cantilevers and sensors. The list and function of these and other components are listed in our prior USPTO applications.
This combination technology of artificial intelligence, machine learning and nanotechnology platform for the securing, manipulating and positioning of micro-organisms (pathogenic) and non-disease forming micro-organisms (non-pathogenic) will hereafter be referred to as the “AIML platform”. This AIML platform accurately identifies all microbes in a controlled indoor setting as well as an uncontrolled outdoor setting. The focus of this application is the weight, mass, circumference, height and width of microbes through imaging and weight. The data being obtained is managed by an AIML platform that constantly learns. The microbe data collection is “staged” in a nanotechnology canister or on a section of the cannister hereafter called the staging area. The microbes are secured, manipulated and positioned by machine learning algorithms and nanotechnology components. This identifying application of biological germs, viruses, bacteria, fungus, protozoa, molds, allergens, disease forming microbes (pathogens) and non-disease forming microbes (non-pathogenic micro-organisms) reduces the time for detection with higher accuracy. All disease forming microbes (pathogens) and all non-disease forming microbes (non-pathogenic micro-organisms) hereafter will be referred to as Stealth Vectors “SVs”.
The AIML platform
The “AIML” platform through the many specific algorithms managing the various nanotechnology components decide at the initial start of the operation what actions are needed to secure, manipulate, position and evaluate SV data in exascale (billions of calculations). The AIML platform, includes different components of (manned and unmanned) drones and robots, sensors, lasers, cantilevers, laptops, desktops, servers (connected and not connected) and high definition lenses which are attached to and/or embedded in the drones and robots and/or stationary platforms. After the images and data is collected by the AIML platform, the data is transferred to various algorithmic AIML platform libraries for matching, evaluation, calculation and forecasting. Whether the data is deemed to be viable or NOTAU “not able to be used”, it continues its evaluation with the obtained data while seeking more data. There are many continuing processes and calculations from software and hardware components where billions of pieces of data are evaluated to determine if pathogenic material is in fact present. Billions of pieces of data lead to a better accurate understanding of what pathogenic threats are present as opposed to small collections of data that take time and are inaccurate. Whether SVS are dead or alive, the AIML platform still collects data as to what is and what may become of the SV. Our machine learning algorithms learn from both dead, alive, single, clusters and combinations of SVs. For example, a larger virus may contain a smaller virus and sometimes viruses invade bacteria while at times a giant virus actually had a smaller virus attached to it. One of the AIML platform algorithms detected an unknown SV where a virus was “sick” and attached from another virus. This data was manually entered into the AIML platform.
The staging area with nanocanisters and nanoladders
The drones and robots have attached or embedded nanotechnology canisters. A nontechnology canister is a small enclosure that can be square, rectangle, circular or oblong. The cannister has ladders and with very expensive models are equipped with lenses (nanotechnology lenses and nanotechnology hardware components) and cantilevers which is also referred to as the staging area. The lenses obtain images of the SVs while the cantilevers obtain the weight of the SVs. The algorithmic process varies where laser sensors would be used, then the lenses, then the cantilevers. One or more of the nanotechnologies cannister components may be used and not in a particular order as determined by the AIML platform. The AIML platform may also deploy canisters that are affixed to an indoor or outdoor surface that is either mobile or stationary. The AIML platform determines where and how to deploy the staging areas. The deployment may depend on particular geographic areas, environmental circumstances, rural and urban settings or such data as food processing plants to name a few instances. If an oil refinery operation is upwind, the AIML platform will decide to use different sensors after using its geographical mapping and industrial database. The physical SVs pass through staging areas that are open or partially opened with one or more sides (or sections) closed. The staging areas have rows and or columns of nano ladders where each ladder is referred to as the “secure step station” where each nanoladder is colored and numbered. Some secure step stations rungs of the ladder are replaced with bars with beads through them. On occasion, the bars can be electrostatically charged “manipulated” (see Stealth Vector Staging, 8 listed) or coated with a sticky substance. Figure 1 shows the nanoladders without sides of the canister. Figure 1 shows the rungs with beads (left hand side) and with beads (right hand side). The beads range in size from small to large where the layout varies. A large bead may be in the middle of smaller beads or vice versa. The nano beads vary in size from one half of a micron to 26.5 microns in diameter. Figure 2 shows rows and columns of nanoladders in a canister with rungs and beads.
For more information on our pathogen identification technology, please go to the AI pathogen identification page.