Technology for identifying foreign objects on a filter

Abstract

This disclosure enables the identification of the type of foreign matter deposited on a filter (e.g., an air filter) and various corresponding computational processes. This identification is technically beneficial in that it does not require specialized knowledge or laboratory analysis, enables non-destructive and rapid inspection, and at the same time enables the detection of early signs of foreign matter presence (e.g., mold growth), allowing for timely intervention as needed.

Description

[Technical Field]

[0001] <Cross-references to related patent applications> This patent application claims priority to U.S. Provisional Patent Application No. 63 / 469,856, filed on 31 May 2023. That application is incorporated herein by reference in its entirety for all purposes.

[0002] This patent application claims priority to U.S. Provisional Patent Application No. 63 / 548,251, filed on 13 November 2023. That application is incorporated herein by reference in its entirety for all purposes.

[0003] This disclosure relates to a technique for identifying foreign matter on a filter. [Background technology]

[0004] Traditionally, air conditioner air filters have been photographed to determine if they need replacing. However, this photographic method does not allow for the identification of the type of foreign matter adhering to the air filter or for any calculations to be performed based on that information. [Overview of the project] [Problems that the invention aims to solve]

[0005] This disclosure enables the identification of the types of foreign matter deposited on a filter (e.g., an air filter) and various computational processes based on that identification. Such identification is technically beneficial because it allows for non-destructive and rapid inspection without requiring specialized knowledge or laboratory analysis, and simultaneously enables the detection of early signs of the appearance of foreign matter (e.g., mold growth). This allows for timely intervention as needed. This can be achieved in various ways. [Means for solving the problem]

[0006] One example of such a method is a step of causing an input device (e.g., a camera) to capture an image of a collection of foreign matter (e.g., mold growth) collected in the input section of a filter that can be installed or installed to filter a directed flow of gas (e.g., air), wherein the collection of foreign matter is formed by the directed flow of gas, and the image shows a pattern representing the collection of foreign matter; a step of causing the input device to transmit the image to a network interface (e.g., a transmitter) so that the network interface transmits the image to a server; and a step of causing the server to (i) receive the image, (ii) detect a pattern (e.g., by a computer vision algorithm), (iii) identify a descriptor (e.g., a name) for the pattern, and (iv) perform a computational operation (e.g., instruct a display to show a message) in response to the capture of the image based on the descriptor.

[0007] Another example of such a method is the step of causing an input device (e.g., a camera) to capture an image of a collection of foreign matter (e.g., mold growth) collected in an input-side section of a filter that is installable or installed to filter a directional flow of gas (e.g., air), wherein the collection of foreign matter is formed by the directional flow of gas, and the image shows a pattern representing the collection of foreign matter; and the step of causing the input device to transmit the image to a processor, as a result of which the processor (i) detects the pattern (e.g., by a computer vision algorithm), (ii) identifies a descriptor (e.g., a name) for the pattern, and (iii) performs an operation (e.g., instructs a display to show a message) in response to the capture of the image based on the descriptor, wherein the input device and the processor are arranged together (e.g., within a locale).

[0008] These methods can be implemented as at least one system or device configured to perform these operations, or as a non-temporary medium (e.g., memory) storing an instruction set executable by a processing unit (e.g., single-core processor, multi-core processor, programmable logic controller (PLC), graphics card, graphics processing unit (GPU), tensor core unit, tensor processing unit (TPU), application-specific integrated circuit (ASIC), controller, edge processor, system-on-a-chip (SOC), hardware accelerator, neural network (NN) accelerator, machine learning (ML) accelerator) for performing these operations. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 shows an embodiment of a system for imaging a filter, which includes a server according to this disclosure. [Figure 2] Figure 2 is a flowchart illustrating an embodiment of a method for using the system of Figure 1 according to this disclosure. [Figure 3] Figure 3 shows an embodiment of a touchscreen of a smartphone or tablet computer equipped with a camera, the touchscreen displaying a graphical user interface (GUI) presenting a questionnaire for the user to fill out according to the present disclosure. [Figure 4] Figure 4 shows an embodiment of the GUI of Figure 3, which presents an image of a filter having a collection of foreign objects that can be captured or have been captured by a camera, in accordance with this disclosure. [Figure 5] Figure 5 shows an embodiment of the GUI of Figure 3, which displays a series of possible messages generated based on a questionnaire filled out by the user and a descriptor of the set of detected foreign objects, according to this disclosure. [Figure 6] Figure 6 shows an embodiment of an input device and filter provided within the defined domain based on this disclosure. [Figure 7]Figure 7 shows one embodiment of a filter located outside the definition area including an input device, in accordance with this disclosure. [Figure 8] Figure 8 shows one embodiment of a system for imaging filters without using a server, in accordance with this disclosure. [Figure 9] Figure 9 is a flowchart showing one embodiment of how to use the system shown in Figure 8. [Modes for carrying out the invention]

[0010] As described above, this disclosure enables the identification of the type of foreign matter deposited on a filter (e.g., an air filter) and various computational processes based on that identification. Such identification is technically beneficial because it enables non-destructive and rapid inspection without requiring specialized knowledge or laboratory analysis, and at the same time enables the detection of early signs of the appearance of foreign matter (e.g., mold growth). This allows for timely intervention as needed. This can be achieved in various ways.

[0011] One such method includes the steps of: causing an input device (e.g., a camera) to capture an image of an aggregate of foreign matter (e.g., mold growth) that has accumulated in the input section of a filter that is installable or installed for filtering a directional flow of gas (e.g., air), wherein the aggregate of foreign matter is formed by the directional flow of gas, and the image shows a pattern representing the aggregate of foreign matter; causing the input device to transmit this image to a network interface (e.g., a transmitter), the network interface to transmit the image to a server; and causing the server to (i) receive the image, (ii) detect a pattern (e.g., by a computer vision algorithm), (iii) identify a descriptor (e.g., a name) for the pattern, and (iv) perform a computational process (e.g., instruct a display to show a message) based on the descriptor in response to the image capture.

[0012] Another example of such a method is the step of causing an input device (e.g., a camera) to capture an image of an aggregate of foreign matter (e.g., mold growth) that has accumulated in the input-side section of a filter that is installable or installed to filter a directional flow of gas (e.g., air), the aggregate of foreign matter being formed by the directional flow of gas, and the image showing a pattern representing the aggregate of foreign matter; and the step of causing the input device to transmit the image to a processor, the processor comprising the steps of (i) detecting the pattern (e.g., by a computer vision algorithm), (ii) identifying a descriptor (e.g., a name) for the pattern, and (iii) performing a computational process (e.g., instructing a display device to display a message) based on the descriptor in response to the capture of the image, wherein the input device and the processor are located in the same place (e.g., within the same locale).

[0013] These methods may also be implemented as at least one system or device configured to perform these operations, or as a non-temporary medium (e.g., memory) that stores a set of instructions that can be executed by a processing unit (e.g., single-core processor, multi-core processor, PLC, graphics card, GPU, tensor core unit, tensor processing unit (TPU), ASIC, controller, edge processor, SOC, hardware accelerator, NN accelerator, ML accelerator) for performing these operations.

[0014] This disclosure will be described in more detail with reference to the various figures mentioned above. These figures illustrate some embodiments of this disclosure. However, this disclosure can be implemented in many different forms and should not necessarily be construed as being limited to the embodiments disclosed herein. Rather, these embodiments are provided to ensure that this disclosure is thorough and complete and to fully convey the various concepts of this disclosure to those skilled in the art.

[0015] The various terms used in this specification can mean directly or indirectly, completely or partially, temporarily or permanently, operating or non-operating, individually or collectively. For example, when an element is described as "above", "connected to", or "coupled to" another element, the element may be directly above, connected to, or coupled to it, or there may be intervening elements, including indirect or direct modifications. In contrast, when an element is described as "directly connected to" or "directly coupled to" another element, there are no intervening elements.

[0016] Similarly, the term "or" used in this specification means an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X adopts A or B" means any of the natural inclusive permutations. That is, the condition "X adopts A or B" is satisfied in any of the cases where X adopts A, X adopts B, or X adopts both A and B.

[0017] Similarly, each of the various singular forms "a", "an", "the" used in this specification is construed to include the various plural forms (e.g., two, three, four) unless otherwise indicated by the context. For example, the term "a" or "an" means "one or more", and the same is true even when the phrase "one or more" is also used in this specification.

[0018] Furthermore, the terms "comprises", "includes", "contains", "has", or "comprising", "including", "containing", "having" (or any tense thereof) when used herein specify the presence of the stated features, integers, steps, operations, elements, or components but do not preclude the presence and / or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof. "Comprising", "having" (or any tense thereof) specify the presence of the stated features, elements, steps, operations, components, or components but do not preclude the presence and / or addition of one or more other features, elements, steps, operations, components, components, or groups thereof. Further, when something is described herein as being "based on" something else, such description refers to a basis that may also be based on one or more other things. That is, unless otherwise stated, "based on" as used herein means inclusively "based at least in part on" or "based at least partially on".

[0019] As used herein, relative terms such as "lower", "bottom", "upper", "top", etc. are used to describe the relationship of one element to another as shown in the accompanying drawings. Such relative terms are intended to include different orientations of the illustrated technology in addition to the orientation depicted in the accompanying drawings. For example, if the device in a series of accompanying drawings is turned over, various elements described as being "below" other elements will be in an orientation where they are "above" the other elements. Similarly, if the device in one of the figures of an embodiment is turned over, various elements described as being "under" or "beneath" other elements will be in an orientation where they are "above" the other elements. Thus, various exemplary terms such as "under" and "bottom" can include both upward and downward orientations.

[0020] Furthermore, terms such as “first,” “second,” etc., may be used in this specification to describe various elements, components, regions, layers, subsets, figures, or sections. However, these elements, components, regions, layers, subsets, figures, or sections should not necessarily be limited by these terms. Rather, these terms are used to distinguish one element, component, region, layer, subset, figure, or section from another element, component, region, layer, subset, figure, or section. Accordingly, the first element, component, region, layer, subset, figure, or section described below may be referred to as the second element, component, region, layer, subset, figure, or section without departing from this disclosure.

[0021] In this specification, the terms “approximately” or “substantially” refer to a variation of ±10% from the nominal value / term. Such variation is always included in any value / term described herein, whether or not it is specifically mentioned.

[0022] In this specification, the terms “or other,” “combination,” “combinable,” and “or combination thereof” refer to all permutations and combinations of the items listed before the term. For example, “A, B, C, or combination thereof” is intended to include at least one of A, B, C, AB, AC, BC, or ABC, and also includes BA, CA, CB, CBA, BCA, ACB, BAC, or CAB, where the order is important in the particular context. Continuing this example, combinations that include one or more items or repetitions of items are also explicitly included, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, etc. A person skilled in the art will understand that, unless clearly limited by the context, there is generally no limit to the number of items or items in any combination.

[0023] Features or functions described in relation to a particular embodiment may be combined or partially combined in any permutation or combination in various embodiments or in combination thereof. Different aspects or elements of the embodiments disclosed herein may also be combined or partially combined in a similar manner. Those skilled in the art will understand that, unless clearly limited by context, there is generally no limit to the number of items or items in any combination.

[0024] Some embodiments may, individually or collectively, constitute components of a larger system. In this case, other procedures may take precedence over or otherwise modify their application. Furthermore, it is not necessary to have multiple steps performed before, after, or simultaneously with an embodiment, as disclosed herein. It should be noted that at least some or all of the methods or processes disclosed herein may be performed in any way, at least partially, by at least one entity.

[0025] In this specification, some embodiments will be described with reference to illustrations of idealized embodiments (and intermediate structures). Therefore, variations from the various shapes shown will be expected, for example, as a result of manufacturing techniques and tolerances. Thus, the various embodiments should not necessarily be interpreted as being limited to various specific shapes of the regions illustrated herein, but rather include, for example, deviations in shape due to manufacturing.

[0026] Any or all elements disclosed herein may be formed from identical structurally continuous parts, such as a single or monolithic structure, or they may be manufactured separately or joined together, such as an assembly or module. Any or all elements disclosed herein may be manufactured by any manufacturing process, including additive manufacturing, machining, or any other type of manufacturing process. For example, manufacturing processes include three-dimensional (3D) printing, laser cutting, computer numerical control (CNC) routing, milling, press working, stamping, vacuum forming, hydroforming, injection molding, lithography, fastening, bonding, nailing, stapling, screwing, and the like.

[0027] Furthermore, unless otherwise specifically defined, all terms used herein (including technical and scientific terms) have the same meaning as those generally understood by those skilled in the art in which this disclosure pertains. Accordingly, terms as defined in general dictionaries should be interpreted in a way consistent with their meaning in the context of the relevant art, and not in an idealized or overly formal sense unless expressly defined herein.

[0028] All patents, published patent applications, and non-patent literature referenced or cited herein are incorporated herein by whole by reference to the same extent as each individual patent, published patent application, or non-patent literature is explicitly cited separately, for all purposes. For further clarity, all incorporated by reference include the incorporated publications as if they were copied / attached to this specification and as if they were originally included herein for all purposes herein. Accordingly, any reference that any matter is disclosed herein includes all subject matter incorporated by reference as described above. However, if any disclosure incorporated herein by reference conflicts with this specification in whole or in part, this specification shall prevail to the extent of the conflict, or to the extent of the broader disclosure or broader definition of a term. Also, if any such disclosures conflict with each other in whole or in part, the later disclosure shall prevail to the extent of the conflict.

[0029] Figure 1 shows an embodiment of a system for imaging a filter, which includes a server according to the present disclosure. Specifically, it comprises a frame 102, a filter 104, a collection of foreign objects 106, a field of view (FOV) 108, an input device 110, a network interface 112, a controller 114, a network 116, a server 118, a database 120, a geolocator 122, and a data actor 124. The input device 110 is embodied as at least one of a camera 110a, a radar 110b, or a transducer 110c, each of which may be housed in or a component of at least one of a fixed form factor 110d or a mobile form factor 110e. For example, at least one of the fixed form factor 110d or the mobile form factor 110e may include at least one of the input device 110, the network interface 112, or the controller 114. The mobile form factor 110e may be materialized as a wearable form factor 110f.

[0030] Frame 102 is constructed of paper material, but may also be made of at least one of the following suitable materials: wood, metal, plastic, cloth, fiberglass, carbon, foam, or other suitable materials, and may be waterproof, water-resistant, water-repellent, washable, reusable, rinseable, cleanable, or none of the above. Frame 102 is rectangular, but may also be of different shapes (e.g., open, closed, symmetrical, asymmetrical, square, triangular, circular, pentagonal, octagonal, elliptical, cylindrical, cubic, teardrop, star). Frame 102 is sized to be held in the hand of a healthy adult at least 18 to 65 years of age, but this configuration is not mandatory. Frame is fixed, but may be movable during imaging as disclosed herein. Frame 102 is optional.

[0031] If a frame 102 is present, the filter 104 is housed in the frame 102 (e.g., by attachment). The filter 104 is made of a woven, mesh, or pleated material (e.g., cotton, fiberglass, paper, plastic), but at least one of the following materials may be used: wood, metal, plastic, woven, paper, fiberglass, carbon, foam, or other suitable materials, and may be waterproof, water-resistant, water-repellent, washable, reusable, rinseable, machine washable, or none of the above. The filter 104 is rectangular in shape, but can be of different shapes (e.g., open, closed, symmetrical, asymmetrical, square, triangular, circular, pentagonal, octagonal, elliptical, cylindrical, cubic, teardrop, star). If the frame 102 houses the filter 104, the frame 102 is handheld by a healthy adult at least 18 to 65 years of age, but this configuration is not mandatory. The filter is fixed, but may be movable during imaging as disclosed herein. The filter 104 can be installed internally (e.g., in a nacelle, engine bay, body, aircraft structure, chassis, engine, motor, pump, blower, or fan) or externally (e.g., on the underside, frame, or flight control surface). These are applicable to any vehicle, manned or unmanned, in the air (e.g., vertical or horizontal takeoff), on land (e.g., cars, buses, trucks, tractors, tanks), at sea (e.g., ships, boats, submersibles, or submarines), or in space (e.g., satellites, space stations).

[0032] The filter 104, or the frame 102 housing the filter 104, is installed (e.g., inserted) into the gas directional channel and is configured to remain within the channel as the gas directional channel passes through the filter 104. Thus, the filter 104 has an input side through which the gas flows in as a directional flow and an output side through which the filtered gas flows out as a directional flow. The gas directional channel can be a straight path, a curved path, a sinusoidal path, or any other suitable path that passes through the filter 104. This path is perpendicular to the input side of the filter 104, but can also take an acute or obtuse angle with respect to the input side of the filter 104.

[0033] The directed flow of gas can be controlled by a blower, fan, or other suitable directed flow source (e.g., on, off, gradual). This can be done by pushing (e.g., blowing) or pulling (e.g., sucking) the gas, either upstream or downstream of the filter 104 or the frame 102 housing the filter 104. For example, the directed flow of gas may include propulsion, gravity, inertia, temperature fluctuations (flux), or other suitable means by which the gas flows. The directed flow path of gas may be provided within a tubular member (e.g., tube, conduit, duct, pipe, hose) configured to house the filter 104 or the frame 102 housing the filter 104 (e.g., permanently or temporarily). The tubular member may have a linear, curved, sinusoidal, or other suitable shape in the longitudinal direction. In terms of cross-sectional shape, tubular members may have open, closed, symmetrical, asymmetrical, square, rectangular, triangular, circular, pentagonal, octagonal, elliptical, teardrop, star-shaped, or other suitable shapes. Tubular members may be made of metal or plastic, but other suitable materials (e.g., rubber) may also be used, and may be opaque, translucent, or transparent. Tubular members may be rigid (e.g., cannot be bent by hand without tools by a healthy adult aged 18 to 65) or flexible (e.g., can be bent by hand without tools by a healthy adult aged 18 to 65). For example, tubular members may be components of or placed inside heating, ventilation, and air conditioning (HVAC) systems, air handlers, window air conditioners, mini-split air conditioning systems, or other suitable systems. These may be residential or industrial, fixed (e.g., inside a building) or mobile (e.g., inside or on a vehicle). For example, the gas is air, and may be at ambient temperature (e.g., room temperature, outdoor temperature), or it may be conditioned (e.g., heated: between approximately 70°F and 90°F), or cooled (e.g., between approximately 70°F and 90°F).For example, directed gas flow occurs in a duct transporting air by a blower or fan, and that air is air-conditioned (e.g., currently) or air-conditioned (e.g., in the future), which may be done via a furnace, condenser, heat pump, or other suitable air conditioning equipment. For example, the gas may be a single gas or a collection of multiple gases, and may be either incompressible or compressible. For example, the gas may be air. For example, the gas may be a noble gas. For example, the gas may be natural gas or synthesis gas. For example, the gas may be argon (Ar), methane (CH4), carbon dioxide (CO2), acetylene (C2H2), ethylene (C2H4), propane (C3H8), hydrogen (H2), parahydrogen (p-H2), ammonia (NH3), nitrogen (N2), oxygen (O2), helium (He), xenon (Xe), krypton (Kr), neon (Ne), carbon monoxide (CO), n-butane (C4H). 10 ), 1-butene (C4H8), 2-cis-butene (C4H8), 2-trans-butene (C4H8), cyclopropane (C3H6), deuterium (D2), dimethyl ether ((CH3)2O), ethane (C2H6), fluorine (F2), n-heptane (C7H 16 ), n-hexane (C6H 14 ), hydrogen sulfide (H2S), isobutane (C4H 10 ), isobutene (C4H8), isohexane (C6H 14 ), isopentane (C5H 12 ), methanol (CH3OH), neopentane (C5H 12 ), nitrous oxide (N2O), nitrogen trifluoride (NF3), n-pentane (C5H 12) Hydrogen chloride (HCl), nitrogen monoxide (NO), propene (C3H6), propyne (C3H4), sulfur dioxide (SO2), sulfur hexafluoride (SF6), toluene (C7H8), trifluoroiodomethane (CF3I), water vapor (H2O), bromochlorodifluoromethane (CBrClF2), methyl bromide (CH3Br), 1,1,2-trichloro-1,2,2-trifluoroethane (C2F3Cl3), 1-chloro-1,1-difluoroethane (C2H3ClF2), 1-chlorodifluoromethane (CHClF2), 1-chlorotrifluoromethane (CCl2F4), 1,2-dichloro-1,1,2,2-tetrafluoroethane (C2Cl2F4), dichlorodifluoromethane (CCl2F2), dichlorofluoromethane (CCl 12 F), 2-chloro-1,1,1-trifluoroethane (C2H2ClF3), chloropentafluoroethane (C2ClF5), methyl chloride (CH3Cl), bromotrifluoromethane (CBrF3), nitrogen dioxide (NO2), silane (SiH4), trichloromonofluoromethane (CCl3F), ozone (O3), or at least one of other suitable gases may be included.

[0034] On the input side of the filter 104, an aggregate 106 of foreign matters photographed in the gas-introduced flow is disposed. This aggregate 106 of foreign matters may be any one or a plurality of organic substances, inorganic substances, or other suitable substances (including particles and elementary particles smaller than an atom). For example, the aggregate 106 of foreign matters may contain at least one particle (e.g., growth, spore, contaminant, fiber) of a plant group (e.g., plants, shrubs, grass, trees), an animal group (e.g., rodents, birds, animals), or an inorganic substance (e.g., contaminants). For example, particles belonging to at least one of the plant group, the animal group, or the inorganic substance refer to rocks, paper, dust, lint, glass fiber, skin, hair, insects, animals, birds, smoke, trees, weeds, grass, pollen, mold, bacteria, viruses, fungi, or other suitable substances.

[0035] The input device 110 is configured to orient the field of view 108 toward the input side of the filter 104, thereby ensuring that the foreign matter aggregate 106 is, is, or is within the field of view 108. Therefore, the input device 110 is positioned or oriented, manually or automatically, to face or oppose the input side of the filter 104, and is positioned coaxially or offset with respect to the gas channel so that the input side of the filter 104 is located within the imaged field of view 108, as disclosed herein. For example, the input device 110 may be positioned or imaged perpendicular, acute, or obtuse to the input surface of the filter 104, so that the foreign matter aggregate 106 is, is, or is within the field of view 108. The field of view 108 is conical, but this configuration is not essential. For example, the FOV 108 may have a right cylindrical, oblique cylindrical, or other suitable shape. The input device 110 is fixed, but is movable during imaging, as disclosed herein.

[0036] The input device 110 may be embodied as at least one of a camera 110a, a radar 110b, or a transducer 110c. Thus, the input device 110 is powered by at least one of either a mains power source (e.g., via a power cable, electrical cord, or extension cord) or an energy storage device (e.g., a battery or capacitor). Optionally, if the input device 110 is embodied as a camera 110a, the input device 110 includes or is connected to a light source (e.g., a flash unit) to powerfully assist, enhance, or enable image acquisition (e.g., via either a commercial power source or an energy storage device) as disclosed herein. However, if the input device 110 is embodied as either a radar 110b or a transducer 110c, this configuration may be appropriately adapted. Accordingly, the light source is positioned or directed, manually or automatically, to face or opposite the input side of the filter 104, and is positioned coaxially or offset with respect to the gas-guided channel so that the input side of the filter 104 receives illumination suitable for imaging as disclosed herein. For example, the light source can be oriented or irradiated perpendicular, acute, or obtuse to the input side of the filter 104, as disclosed herein, so that the aggregate of foreign matter 106 is properly illuminated.

[0037] Camera 110a includes infrared cameras (e.g., when gas is heated or cooled), monochrome cameras (e.g., under low-light conditions), telephoto cameras (e.g., for remote photography), thermal cameras (e.g., when gas is heated or cooled), dynamic cameras (e.g., for mobile use for troubleshooting or monitoring), static cameras (e.g., to ensure consistency of FOV 108), cameras with wide-angle lenses (e.g., to ensure FOV 108 due to spatial constraints), webcams (e.g., for ease of installation for real-time monitoring), security cameras or surveillance cameras (e.g., for integration into video management systems), smartwatch cameras (e.g., for ease of shooting), pen-type cameras (e.g., for ease of shooting), digisscope cameras (e.g., for improved focus accuracy), three-dimensional (3D) cameras (e.g., for improved depth perception), and ultraviolet (UV) cameras. The camera may be at least one of the following: a camera (e.g., for improved biological perception), an area scan camera (e.g., for improved resolution), a line scan camera (e.g., for improved dynamic range), an electronic camera (e.g., for a high signal-to-noise ratio), a water-resistant camera (e.g., for resistance to moisture), a waterproof camera (e.g., for resistance to water), a high-sensitivity or low-light camera (e.g., for imaging in dark places), a robotic camera (e.g., for heuristic automation), a holographic image sensor (e.g., for improving the detection rate of foreign object aggregates 106), a laparotronic camera (e.g., for expanding the dynamic range), a microscope camera (e.g., for improving the detection rate of foreign object aggregates 106), a strobo-tachometer (e.g., for more accurate detection of foreign object aggregates 106 by visual "freezing" of motion), or a laser sensor camera (e.g., for improving the detection rate of foreign object aggregates 106).

[0038] The input device 110 may be at least one of either a time-of-flight radar (e.g., for stationary use) or a Doppler radar (e.g., for mobile use).

[0039] The input device 110 may be a primary transducer, a secondary transducer, or any other suitable type of transducer. For example, the transducer may be an ultrasonic transducer (e.g., for use in foggy or steamy conditions).

[0040] Input devices 110, such as a camera 110a, radar 110b, or transducer 110c, may be embodied as a fixed form factor 110d or a movable form factor 110e. Thus, the input device 110 may be attached (e.g., supported, suspended, mounted, fixed, fastened, magnetically attached, fitted, bonded) or stabilized to a structure (e.g., one present in the camera 110a), whether a single structure or an assembled structure. For example, the structure may be a mounting part (e.g., L-shaped, U-shaped, V-shaped, C-shaped) which is either a monolithic structure or an assembly, and the filter 104 may be mounted to a frame 102, and the input device 110 may be mounted to a mounting part extending from at least one of the frame 102 or the filter 104. For example, the structure may be a tripod, a bracket (e.g., L-shaped, U-shaped, V-shaped, C-shaped), which may be either a single molded part or an assembled part. Alternatively, it may be a base (e.g., T-shaped, inverted T-shaped), which may be a one-piece molded part or an assembly. The input device is mounted on at least one of a tripod, bracket, or base. For example, the structure may be a gyro stabilizer, and the input device may be stabilized by the gyro stabilizer. For example, the input device 110 may be mounted on a mounting part (e.g., L-shaped, U-shaped, V-shaped, C-shaped) for a diagnostic tool (e.g., a sensor, gas sensor, thermometer, pressure sensor, humidity sensor), and the mounting part may be a one-piece molded part or an assembly. For example, the input device 110 may be mounted on a mounting part (e.g., L-shaped, U-shaped, V-shaped, C-shaped) which may be a one-piece or assembly structure located inside a protective case (e.g., a handheld case, a mountable case). For example, the input device 110 may be mounted on a mounting section (e.g., L-shaped, U-shaped, V-shaped, C-shaped) which is either a one-piece or assembled structure, extending from a display (e.g., a screen, a touchscreen). For example, the input device 110 can be mounted on a mounting section (e.g., L-shaped, U-shaped, V-shaped, C-shaped) which is either a one-piece or assembled structure, located behind glass (including glass-like or glass fiber material).For example, the input device 110 may be mounted on a retractable (e.g., manual, automatic) fixture (e.g., L-shaped, U-shaped, V-shaped, C-shaped). For example, the input device 110 can be mounted on a fixture (e.g., L-shaped, U-shaped, V-shaped, C-shaped) that is either a one-piece or assembled structure, extending from a sensor (e.g., a temperature sensor, pressure sensor, humidity sensor) that detects at least one of foreign matter, sections, or gases. For example, the input device can be mounted on a fixture (e.g., L-shaped, U-shaped, V-shaped, C-shaped) that is either a one-piece or assembled structure, extending from a light source (e.g., a lamp, light bulb, flash unit). However, these configurations can be omitted. For example, the filter 104 may be mounted to the frame 102 (e.g., by fixing, bonding, fitting, or magnetic attachment), and the input device 110 may be configured to extend from the frame 102. Similarly, the input device 110 may be configured to extend from the filter 104. Regardless of whether a structural design is adopted or whether a filter 104 is present, the input device 110 can be installed internally (e.g., in a nacelle, engine bay, body, aircraft structure, chassis, engine, engine frame or chassis supporting the engine, motor, pump, blower, fan) or externally (e.g., on the underside, frame, flight control surface). It is applicable to any vehicle, manned or unmanned, in the air (e.g., vertical or horizontal takeoff), on land (e.g., car, bus, truck, tractor, tank), at sea (e.g., ship, boat, submersible, submarine), or in space (e.g., satellite, space station). For example, the input device 110 and filter 104 may be placed inside a submersible marine vehicle (e.g., for noise reduction). For example, the vehicle may be a robot equipped with the input device 110 in any of the air, land, sea, or space environments. For example, the input device 110 may be attached to a manually or automatically controlled articulated arm.

[0041] The input device 110 (e.g., a camera) may be housed in or be a component of a fixed form factor 110d. For example, the fixed form factor 110d may include the input device 110, a network interface 112, and a controller 114. For example, the fixed form factor 110d may be a wall, floor, ceiling, household appliance (e.g., a water heater, a heater, an air conditioner, a pump, a refrigerator), furniture (e.g., a chair, a table, a sofa, a shelf), a container (e.g., a pot, a frying pan, a planter), a desktop computer, a workstation computer, or any other suitable fixed form factor within a building.

[0042] The input device 110 (e.g., a camera) may be housed in or a component of a mobile form factor 110e. For example, the mobile form factor 110e may include the input device 110, a network interface 112, and a controller 114. For example, the mobile form factor 110e may be a laptop computer, a tablet computer, a mobile phone (e.g., a terrestrial network phone, a satellite phone), a feature phone, a smartphone, a phablet computer, a wearable computer, or other suitable mobile computing form factor. For example, the input device 110 may be a smartphone camera. Regardless of the form factor, note that the input device 110 (e.g., a camera) is either front-facing or rear-facing, at least one of the two.

[0043] The mobile form factor 110e may be a wearable form factor 110f. Therefore, the input device 110 may be hosted by or a component of the wearable form factor 110f. For example, the wearable form factor 110d can be worn by a person, animal, bird, fish, mannequin, doll, robot, or other suitable object. For example, the wearable form factor 110d may include a headset computer, eye-frame computer, clothing computer, hat computer, watch, smartwatch, or other suitable wearable form factor. For example, the input device 110 may be housed (e.g., fixed, attached, fitted) or a component of a collar worn by an animal (e.g., a dog) or robot.

[0044] The controller 114 may be a processing unit (e.g., a single-core processor, a multi-core processor, a PLC, a graphics card, a GPU, a tensor core unit, a TPU, an ASIC, a controller, an edge processor, a SOC, a hardware accelerator, a NN accelerator, or an ML accelerator). The controller 114 controls the input device 110. Therefore, the controller 114 is powered by one or more of either a mains power supply (e.g., via a power cable, electrical cord, or extension cord) or an energy storage device (e.g., a battery or capacitor), whether the controller 114 is the same as or different from the input device 110.

[0045] The network interface 112 may be hardware logic programmed for communication with communication devices (e.g., hardware logic, transmitters, receivers, transceivers, chips, network cards, routers, base stations, cell sites, satellites). Communication may take place in line of sight (e.g., optical, acoustic) or beyond line of sight (e.g., radio, microwave). For example, communication may take place over at least one of the following: a local area network (LAN), a wide area network (WAN), a personal area network (PAN), a mesh network, a satellite network, a neutrino network, or a cellular network. For example, a PAN may include the Bluetooth® protocol, the ZigBee protocol, or other suitable protocol. The controller 114 controls the network interface 112. Thus, the network interface 112 is powered by either a mains power source (e.g., via a power cable, electrical cord, extension cord) or an energy storage device (e.g., a battery, capacitor), which may be the same as or different from either the input device 110 or the controller 114, or both.

[0046] Network 116 may be at least one of the following: LAN, WAN, PAN, mesh network, satellite network, neutrino network, or cellular network. Network interface 116 communicates with network 116.

[0047] Server 118 (for example, a single server, a group of servers, a server cloud, or a server farm) may be a web server or an application server. Server 118 communicates with network 116.

[0048] Database 120 exists separately and independently from server 118, but this configuration is not mandatory, and server 118 can also host database 120. Database 118 may be located locally on server 118 or remotely from server 118. Database 120 stores a series of records, each record containing a primary key, a descriptor of a foreign object item (e.g., mold, hair) (e.g., name), and a series of images depicting the foreign object item 106 (e.g., different angles, colors, shapes). For example, each record may include a type descriptor and subtype descriptor of the foreign object item, as needed. For example, the descriptor may be "mold" and the type descriptor may be "Mucor genus". Other information (common name / scientific name, description, map showing observation location, peak season, factors causing attachment to filter 104, etc.) can also be stored in the records. The database is relational, but this configuration is not mandatory, and it can be built in a different way (e.g., graph type, in-memory type).

[0049] The geolocator 122 may be hardware logic (e.g., a chip) programmed for communication with a location information network, whether on the ground or satellite. For example, the location information network may be a Global Positioning Satellite System (e.g., GPS, Galileo, Beidou). The controller 114 controls the geolocator 122. Therefore, the geolocator 122 is powered by at least one of the following: the input device 110, the controller 114, or the network interface 112, either a mains power source (e.g., via a power cable, electrical cord, extension cord) or an energy storage device (e.g., a battery, capacitor). The geolocator 122 is optional.

[0050] Data actor 124 receives datasets from either network interface 112 or server 118 via network 116, and as disclosed herein, data actor 124 is programmed to receive datasets from either network interface 112 or server 118 via network 116, process the datasets, and transmit the datasets to either network interface 112 or server 118 via network 116. Although data actor 124 is separate and independent from server 118, this configuration is not mandatory, and data actor 124 and server 118 may be a single physical or virtual computing machine. Data actor 124 is optional.

[0051] In a certain operating mode, system 100 enables input device 110 to capture an image (e.g., in JPEG, BMP, GIF, PNG, EPS, WebP, SVG, HEIF, XCF, AVIF, AVI, MPEG, MPEG4, 5GigE, 4K, or 8K format). At the input side of filter 104 (e.g., held by the user after removal from or before insertion into a tubular member) or installed (e.g., inside a tubular member), it generates an image showing the pattern of aggregates of foreign matter 106 formed from the directional flow of gas (e.g., air) in order to filter the directional flow of gas (e.g., air). System 100 enables input device 110 to transmit the image (or a copy thereof) to network interface 112 (e.g., by instruction or via controller 114). The network interface 112 then transmits the image (or a copy thereof) to server 118 (e.g., via network 116). System 100 includes server 118 (i) receiving an image (or a copy thereof), (ii) detecting a pattern in the image (e.g., based on a query to database 120), (iii) identifying a descriptor of the pattern (e.g., mold) (e.g., based on a query to database 120), and (iv) performing computational processing (e.g., providing the descriptor to be displayed) based on the descriptor in response to the image being captured.

[0052] Figure 2 is a flowchart illustrating an embodiment of a method using the system of Figure 1 according to this disclosure. Specifically, method 200 includes a series of blocks 202-212 performed by system 100.

[0053] In block 202, the input device 110 captures an image (e.g., in JPEG, BMP, GIF, PNG, EPS, WebP, SVG, HEIF, XCF, AVIF, AVI, MPEG, MPEG4, 5GigE, 4K, 8K format) of a collection of foreign matter 106 (e.g., mold growth) that has accumulated in the input-side sections (e.g., center, side, top, bottom) of a filter 104 that is installable (e.g., held by the user after removal from or before insertion into a tubular member) or installed (e.g., inside a tubular member) for filtering a directional flow of gas (e.g., air), the collection of foreign matter 106 being formed by the directional flow of gas, and the image shows a pattern representing the collection of foreign matter 106. The image may be a photograph or a video. The input-side sections of the filter 104 may be a portion or all of the input-side sections of the filter 104. The collection of foreign matter 106 is within the field of view 108. Images may be captured when no directional gas flow is occurring (for example, when the source of the directional gas flow (e.g., blower, fan) is not operating). This method of capture reduces image artifacts (e.g., blown dust) in the image, allowing server 118 to improve the accuracy of pattern detection. However, this method is not mandatory, and images may be captured while the directional gas flow is occurring, which may occur when the source of the directional gas flow (e.g., blower, fan) is operating. However, if image artifacts are present in the image, these artifacts may be configured to be removed by server 118 using appropriate computer vision algorithms, particularly when accessing machine learning models trained to detect those artifacts.

[0054] In block 204, the input device 110 transmits the image (or a copy thereof) to the network interface 112 (for example, via or instructed by the controller 114). The network interface 112 then transmits the image (for example, via network 116) to the server 118. For example, the network interface 112 can transmit the image to the server 118 via at least one of a local area network (LAN), wide area network (WAN), personal area network (PAN), mesh network, satellite network, or cellular network. Note that the original or a copy may be transmitted for auditing, redundancy, or historical purposes.

[0055] In block 206, server 118 receives an image (or a copy thereof) containing a pattern.

[0056] In block 208, server 118 detects patterns in an image (or a copy thereof). Server 118 detects patterns using various computer vision algorithms. For example, server 118 detects patterns based on image quality, shape, color, size, position, orientation, depth, illumination, signal wavelength / frequency (e.g., echo measurements), pixel variation (e.g., color, temperature, brightness), or other image attributes related to patterns (e.g., spatial proximity). For example, server 118 can detect patterns by querying APIs (e.g., Google Vision AI, ChatGPT 4-o, Facebook LLaMA, Google Gemini, Microsoft Copilot) using or based on an image. For example, server 118 can detect patterns by accessing an ML model (e.g., stored in database 120) trained to detect foreign objects. For example, server 118 may access a reference image (e.g., stored in database 120) depicting a section without a pattern and identify the difference between the image from input device 110 and the reference image to detect that the difference is the pattern. For example, the server 118 may include querying the database 120 and using certain computer vision techniques (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), or You Only Look Once (YOLO)) to detect mold (or other foreign matter 106) on the filter 104.Therefore, there may be a data acquisition phase (e.g., building an image dataset containing mold on filter 104), a data annotation phase (e.g., annotating foreign object regions in the dataset to provide true labels for training an appropriate model), a model training phase (e.g., training an object detection model (e.g., CNN, YOLO) on the annotated dataset to identify and classify foreign object regions on filter 104), a model evaluation phase (e.g., evaluating the performance of the trained model on a retained test set using metrics such as accuracy, recall, and mean precision (mAP)), and a deployment phase (e.g., deploying the trained model to edge devices or servers to detect mold in real time from camera footage or still images). For example, server 118 can detect patterns by employing a YOLO algorithm (e.g., v1, v2, v3, v4, v5, v6) that is operational (e.g., trained) based on a dataset (e.g., obtained from either a laboratory or public data source) stored in database 120. For example, the dataset may contain 10,000 images from one data source and 10,000 images from another data source, each image exhibiting some pattern depicting various foreign objects, as disclosed herein. However, it should be noted that other types of computer vision algorithms (e.g., CNN, RNN, Faster R-CNN, RetinaNet) are also possible.

[0057] In block 210, server 118 identifies descriptors (e.g., mold) for a pattern. This may include querying database 120. Because misidentification and multiple identification may occur based on the detected pattern, server 118 may identify multiple descriptors and then generate suggestions, along with confidence scores, for the most likely match candidates based on the visual pattern. Server 118 may detect patterns or identify descriptors for patterns by querying database 120, which stores a set of patterns and a set of pattern descriptors corresponding to the set of patterns (e.g., one-to-one data correspondence, many-to-one data correspondence, one-to-many data correspondence, many-to-many data correspondence), as disclosed herein.

[0058] In block 212, server 118 performs calculations based on descriptors in response to the captured image (for example, by sending descriptors to be displayed). This processing may be performed in real time. This calculation processing can take various forms, and any form may or may not be customized based on geolocation information. Geographic location information may be obtained from geolocator 122 (for example, via controller 114) or manually entered (for example, a calculation terminal prompts the user to enter a postal code or town name). For example, this calculation processing may or may not be performed by data actor 124, or data actor 124 may or may not be involved in it at least partially. For example, as further illustrated in the context of Figures 3-5, based on a descriptor, the computing operation causes the server 118 to send a message via the network 116 to a computing terminal (e.g., a desktop computer, laptop computer, smartphone, terrestrial network phone, satellite phone, tablet computer, wearable computer, or in-vehicle computer) causing the computing terminal to output content (e.g., text, images, audio) (e.g., via a screen or speaker). For example, the content may include a descriptor. For example, the content may indicate that the filter 104 should be replaced or cleaned. For example, the content may include (e.g., itself) recommendations (e.g., text, images, audio) for a user profile associated with at least one of the input device 110 or the filter 104 (e.g., during initial setup or maintenance). For example, the recommendations may comply with applicable laws (e.g., federal, state, and local laws) and regulations (e.g., government agency regulations, FCC regulations, and FDA regulations). Alternatively, the truthfulness of such content may be audited before it is programmed to display it. For example, recommendations may include boilerplate text (e.g., "Please consult a doctor").For example, recommendations may be health-related recommendations (e.g., leave this area, take allergy medication). For example, health-related recommendations may be personalized (e.g., leave this area because you declared you have a pollen allergy, take Benadryl or Zyrtec). For example, health-related recommendations may be based on a user profile (as pre-entered or default settings) that includes a set of health-related attributes (e.g., a list of known allergies, a list of known lung diseases), and health-related recommendations may be personalized based on a set of personal attributes. For example, recommendations may include a unified resource locator (URL) to a webpage (e.g., a personalized web portal, a knowledge web portal, an e-commerce web portal) containing a set of information (e.g., text, images, audio) related to the descriptor. For example, the webpage may describe a physical product related to the descriptor (e.g., a filter, an air purifier filter, a facial mask, a cleaning solution, cleaning tools, a brush, a medicine, personalized medicine). For example, a webpage may offer products (e.g., filters, air purifier filters, facial masks, cleaning solutions, cleaning tools, brushes, pharmaceuticals, personalized medicines) or services (e.g., cleaning services, personalized cleaning services, medical services, personalized cleaning services) for sale, lease, or other appropriate commercial activity, which may be based on a user profile (as pre-entered or default settings). For example, controller 114 obtains location information from geolocator 122 (or location information can also be obtained from the user). This location information is associated with one or more of the input devices, filters, or computing terminals. Thus, this recommendation may be customized to match the weather forecast associated with one of the input devices, filters, or computing terminals based on the location information.For example, if this location information is obtained from a geolocator 122 via a controller 114 (or if the location information is obtained from a user), the recommendations may be customized to match a set of environmental data (e.g., pollen count, UV index, air quality index (AQI), particulate matter measurement (PMM)) collected based on the location information associated with one or more input devices, filters, or computing terminals. For example, as a computing process, the server may form a dataset (e.g., text, images, audio) based on or containing descriptors and transmit the dataset to at least one of the following: a third-party server (e.g., a personalized web portal, a knowledge web portal, an e-commerce web portal), a third-party point-of-sale (POS) terminal (e.g., a cash register), or a third-party (e.g., a relative, a vendor) computing terminal (e.g., a desktop computer, a laptop computer, a tablet computer, a mobile phone, a feature phone, a smartphone, a phablet, a wearable computer, an in-vehicle computer). For example, the calculation process may be the process of displaying a report on a computing terminal (e.g., a desktop computer, laptop computer, tablet computer, mobile phone, feature phone, smartphone, phablet computer, wearable computer, or in-vehicle computer) based on or including a descriptor. For example, the report may be a data file (e.g., a spreadsheet file, a word processing file, or a web page) containing the report content, regardless of whether it is structured or unstructured, generic or personalized (e.g., based on a user profile or default settings). For example, the report content may include figures, charts, diagrams, text, images (e.g., photographs, videos), audio, or other appropriate format content. For example, the report may be convertible to a standardized format (e.g., Portable Document Format (PDF)).

[0059] The controller 114 may be connected to sensors (e.g., humidity, CO2, air quality, pressure). The input device 110 and the sensors may be housed in a housing (e.g., a box, enclosure, or case). The input device 110 may be housed in a first housing, and the sensors in a second housing. The sensors can be attached (e.g., fixed, fitted, bonded, or magnetically) to at least one of the frame 102, the filter 104, or a tubular member housing the filter. The sensors can be attached (e.g., fastened, fitted, bonded, or magnetized) to the input device 110 or a mounting section (e.g., L-shaped, T-shaped, C-shaped, U-shaped, or J-shaped), supported, lifted, suspended, or otherwise held. The sensor is powered by at least one of the following, identical or different, a mains power source (e.g., via a power cable, electrical cord, extension cord) or an energy storage device (e.g., a battery, capacitor), which is the same as or different from at least one of the input device 110, the controller 114, the network interface 112, or the geolocator 122. Thus, the sensor can detect at least one of the foreign object aggregate 106, the input-side area of ​​the filter 104, or the gas, and form a detection dataset. The sensor transmits the detection dataset to the network interface 112 (e.g., by instruction from or via the controller 114). The network interface 112 then transmits the detection dataset to the server 118 (e.g., via network 116). The server 118 can (i) receive the detection dataset, (ii) analyze the detection dataset, and perform calculations based on the analyzed detection dataset. For example, this analysis may enable the server 118 to better detect patterns or enable better detection. For example, this analysis allows the server 118 to verify patterns based on sensing datasets acquired from sensors and images acquired from input device 110. For example, this analysis allows the server 118 to extend or complement descriptors or arithmetic processes.

[0060] Figure 3 shows an embodiment of a touchscreen of a smartphone or tablet computer equipped with a camera, which displays a graphical user interface (GUI) presenting a questionnaire for the user to fill out in accordance with this disclosure. Specifically, a GUI 300 is provided to display the questionnaire, which may be self-analysis type, presenting a series of questions as prompts and prompting a series of corresponding user inputs (e.g., binary, alphanumeric). These inputs relate to the medical history of an individual (the user themselves or another). The GUI 300 may be a software wizard. For simplicity, a camera is described, but this configuration is not mandatory, and any input device 110 can be used. Similarly, for simplicity, a smartphone or tablet computer is described, but any computing terminal can be used. For example, a computing terminal can host the input device 110, regardless of whether the terminal is in the same location (e.g., the same place, area, room, floor, building, or vehicle) as at least one of the input device 110 or the filter 104.

[0061] Figure 4 shows an embodiment of the GUI of Figure 3 that presents an image of a filter having an aggregate of foreign matter that can be captured or captured by a camera in accordance with this disclosure. Specifically, when the camera is pointed towards the input side of the filter 104 (either from the front or at an angle), there is a GUI 400 that displays a live view of the aggregate of foreign matter 106 located on the input side of the filter 104 captured by the camera. GUI 400 may be a software wizard. GUI 300 and GUI 400 may be a single GUI and may be included in a single software wizard. If a smartphone or tablet computer equipped with a camera has a flash unit, the flash unit can be operated automatically (e.g., based on input from a light sensor mounted on the smartphone or tablet computer) or manually (e.g., by user input). For simplicity, a camera is described here, but this configuration is not mandatory, and any input device 110 can be used. Similarly, for simplicity, a smartphone or tablet computer is described, but any computing terminal can be used. For example, a computing terminal can host an input device 110, regardless of whether the terminal is located in the same place (e.g., the same location, area, room, floor, building, or vehicle) as one or more of either the input device 110 or the filter 104.

[0062] Figure 5 is a diagram illustrating an embodiment of the GUI of Figure 3, which presents a set of possible messages generated based on a questionnaire completed by the user, and a descriptor of the foreign object aggregate detected in accordance with this disclosure. Specifically, GUI 500 presents a set of possible messages (e.g., text, hyperlinks, illustrations, videos, charts) generated based on a questionnaire completed by the user, as disclosed herein, and a descriptor of the detected foreign object aggregate 106. GUI 500 may be a software wizard. GUI 500 and at least one of GUI 300 or GUI 400 may be a single GUI and be contained within a single software wizard.

[0063] The reports disclosed herein may include any messages as shown in Figure 5. For example, if the aggregate 106 is determined to not meet a predetermined threshold (or vice versa), as disclosed herein, a default message (e.g., "Air quality is normal") may be displayed. However, if the aggregate 106 is determined to meet a predetermined threshold (or vice versa), as disclosed herein, there may be a single message or a series of messages (e.g., progressively or increasing in severity) indicating that the aggregate 106 is suboptimal, undesirable, or related to an emergency (e.g., a forest fire, volcano, hurricane, tornado, pollution event). These messages may be stored locally (e.g., on the computing terminal receiving the message) or shared with another computing terminal (e.g., a medical computing terminal, an emergency service computing terminal) (e.g., by text message, email), which may be local or remote. The messages displayed may be a portion, many, few, or none of the possible messages. The GUI 500 may be a software wizard. GUI300, GUI400, and GUI500 are single GUIs and may be included in a single software wizard. For simplicity, a camera is described, but this configuration is not mandatory, and any input device 110 disclosed herein can be used. Similarly, for simplicity, a smartphone or tablet computer is described, but it should be noted that any computing terminal disclosed herein can be used. For example, a computing terminal may host the input device 110 whether or not it is located in the same place (e.g., the same place, area, room, floor, building, or vehicle) as at least one of the input device 110 or the filter 104.

[0064] Figure 6 shows an embodiment of an input device and filter within the defined domain according to this disclosure. Specifically, there exists a defined domain 600 (at least one of fixed or movable) that includes the input device 110 and the filter 104. Therefore, the directed flow of gas to the filter 104 may be located inside at least one of the following: a land vehicle, an aircraft, a ship, or a spacecraft (these are examples of the defined domain 600). Alternatively, or additionally, the directed flow of gas to the filter 104 may be located inside an object that is an example of the defined domain 600 (e.g., a tubular member, tube, conduit, duct, gas handler, air handler, furnace, air conditioner, area, room, building, tent, warehouse, factory, underground facility, basement). Note that any of the land vehicle, aircraft, ship, or spacecraft (e.g., the defined domain 600) may include the object (e.g., the defined domain 600), or may be the object (e.g., the defined domain 600) itself. Alternatively, the object (e.g., the defined area 600) may include at least one of the following: a land vehicle, an aircraft, a ship, or a spacecraft, or one or more of these.

[0065] Figure 7 shows an embodiment of a filter located outside a definition area including an input device according to the present disclosure. Specifically, a filter 104 and a definition area 700 are shown, the definition area 700 may be materialized as a definition area 600. The definition area 700 includes an input device 110. However, unlike in Figure 6, the filter 104 is located outside the definition area 700. For example, the input device 110 may be in one definition area 700, and the filter 104 may be located in another definition area 700.

[0066] Figure 8 shows an embodiment of a server-free filter imaging system based on this disclosure. Specifically, there exists a system 800 which can be materialized as system 100 shown in Figure 1. However, unlike system 100, system 800 allows the controller 114 to detect patterns in the image locally (e.g., in airplane mode) or at the "edge" without communication with network 116 or the use of server 118, as described in the context of Figure 1. Therefore, in one operating mode, the system 800 enables the input device 100 to capture images (e.g., in JPEG, BMP, GIF, PNG, EPS, WebP, SVG, HEIF, XCF, AVIF, AVI, MPEG, MPEG4, 5GigE, 4K, 8K formats) of aggregates of foreign matter 106 that have accumulated in the input-side sections (e.g., center, lateral, top, bottom) of a filter 104 that is installable (e.g., held by the user after removal from or before insertion into a tubular member) or installed (e.g., inside a tubular member) for filtering a directional flow of gas (e.g., air), the aggregates of foreign matter 106 being formed by the directional flow of gas, and the images show patterns representing the aggregates of foreign matter. The system 800 enables the input device 100 to send an image (or a copy thereof) to a processor (e.g., a single-core processor, a multi-core processor, a PLC, a graphics card, a GPU, a tensor core unit, a TPU, an ASIC, a controller, an edge processor, a SOC, a hardware accelerator, an NN accelerator, an ML accelerator). This allows the processor to (i) detect a pattern (e.g., based on a query to the database 120), (ii) identify a descriptor (e.g., mold) corresponding to the pattern (e.g., based on a query to the database 120), and (iii) perform arithmetic processing (e.g., providing a descriptor for display) based on the descriptor in response to the image capture. The processing device may be a controller 114. The input device 110 and the processor may be located in the same place.For example, the input device 110 and the processor may be hosted by or components of a computing terminal (e.g., a desktop computer, laptop computer, tablet computer, mobile phone, feature phone, smartphone, terrestrial network phone, satellite phone, phablet computer, wearable computer, or in-vehicle computer). Thus, system 800 implements local or edge modes for pattern detection. This is useful when server 800 is down or inaccessible, or when network 116 is down or inaccessible. Originals or copies may be transmitted (e.g., for auditing, redundancy, or historical purposes).

[0067] A hybrid system is conceivable, made possible by systems 100 and 800. For example, system 800 allows controller 114 to detect patterns or identify descriptors locally (e.g., in airplane mode) or at the "edge" without communication with network 116 or the use of server 118, as disclosed herein. Each of these detections or identifications is then verified, confirmed, authenticated, enhanced, or supplemented, where possible or appropriate, by controller 114 communicating with server 118 via network interface 112 through network 116. This is done, where possible or appropriate, based on controller 114 communicating with server 118 via network interface 112 through network 116. Furthermore, or alternatively, database 120 in system 800 may be periodically updated by server 118 via network interface 112 through network 116 to maximize the probability of pattern detection or descriptor identification as needed.

[0068] Figure 9 is a flowchart illustrating an embodiment of a method using the system of Figure 8. Specifically, Method 900 includes a series of blocks 902-908 executed by System 800, which operates as a standalone or hybrid system, as disclosed herein. Method 900 is similar to Method 200 but is adapted and differs based on the differences between Figure 8 and Figure 1 (e.g., local detection or identification versus remote detection or identification), as disclosed herein.

[0069] In block 902, similar to method 200, the input device 100 captures images (e.g., in JPEG, BMP, GIF, PNG, EPS, WebP, SVG, HEIF, XCF, AVIF, AVI, MPEG, MPEG4, 5GigE, 4K, 8K formats) of aggregates of foreign matter 106 that have accumulated in the input-side sections (e.g., center, lateral, top, bottom) of a filter 104 that is installable (e.g., held by the user after removal from or before insertion into a tubular member) or installed (e.g., inside a tubular member) for filtering a directional flow of gas (e.g., air), wherein the aggregates of foreign matter 106 are formed by the directional flow of gas, and the images show patterns representing the aggregates of foreign matter. The input device 100 transmits an image (or a copy thereof) to a processor (e.g., a single-core processor, a multi-core processor, a PLC, a graphics card, a GPU, a tensor core unit, a TPU, an ASIC, a controller, an edge processor, a SOC, a hardware accelerator, a NN accelerator, or an ML accelerator). Note that either the original or a copy (e.g., for auditing, redundancy, or historical purposes) may be transmitted.

[0070] In block 904, similar to method 200, the processor detects a pattern (for example, based on a query to database 120).

[0071] In block 906, similar to method 200, the processor identifies a descriptor (e.g., mold) for a pattern (e.g., based on a query to database 120).

[0072] In block 908, similar to method 200, the processor performs arithmetic operations (e.g., supplying a descriptor to be displayed) based on a descriptor in response to the capture of an image. The processor may be the controller 114. The input device 110 and the processor may be located in the same place. For example, the input device 110 and the processor may be hosted by or components of a computing terminal (e.g., a desktop computer, laptop computer, tablet computer, mobile phone, feature phone, smartphone, phablet computer, wearable computer, or in-vehicle computer). Thus, system 800 enables local or edge modalities to detect useful patterns when the server 800 is down or inaccessible, or when the network 116 is down or inaccessible.

[0073] Figures 1-9 illustrate the concepts in the context of filters (e.g., air filters), but this configuration is not essential and should not be interpreted as limiting the disclosure. Therefore, additionally or alternatively, the disclosure is applicable to objects other than filters, whether or not they are used in the context of directed gas flow. For example, these objects include blades, foils, fan blades or foils, turbine blades or foils, vacuum blades or foils, propellers, various screens, magnetic (metallic) air (or other gas) flow filtration, stroboscopic image recognition synchronized with a camera (or other form of input device 110), pots, siding, drywall, medical devices, or other suitable objects. Thus, the disclosure further enables the identification of the types of foreign matter accumulated on objects (e.g., blades, foils) and various computational processes depending on these. Such identification is technically beneficial in that it does not require expertise or laboratory analysis and enables non-destructive and rapid inspection. At the same time, it enables the detection of early signs of foreign matter presence (e.g., mold growth) and allows for timely intervention as needed. This can be achieved in various ways.

[0074] One example of such a method is a process in which an input device (e.g., a camera) takes an image of an aggregate of foreign matter (e.g., mold growth) gathered on a section of the side of an object, the image showing a pattern representing the aggregate of foreign matter; a process in which the input device transmits this image to a network interface (e.g., a transmitter), the network interface transmits the image to a server; and a process in which the server (i) receives the image, (ii) detects the pattern (e.g., by a computer vision algorithm), (iii) identifies a descriptor (e.g., a name) for the pattern, and (iv) performs a computational process (e.g., instructs a display to show a message) based on the descriptor in response to the image capture. For example, as described in Figures 1-7, this method can be implemented by system 100.

[0075] Another example of such a method is the step of having an input device (e.g., a camera) capture an image of an aggregate of foreign matter (e.g., mold growth) on a section of the side of an object, wherein the image shows a pattern representing the aggregate of foreign matter; and the step of having the input device transmit the image to a processor, wherein the processor (i) detects the pattern (e.g., by a computer vision algorithm), (ii) identifies a descriptor (e.g., a name) for the pattern, and (iii) performs a computational process (e.g., instructs a display device to display a message) based on the descriptor in response to the image capture, wherein the input device and the processor are located in the same place (e.g., within the same locale). For example, this method can be implemented by system 800 as described in Figures 8-9.

[0076] These methods can also be embodied as at least one system or device configured to perform these operations, or as a non-temporary medium (e.g., memory) that stores an instruction set executable by a processing unit (e.g., a single-core processor, a multi-core processor, a PLC, a graphics card, a GPU, a tensor core unit, a TPU, an ASIC, a controller, an edge processor, a SOC, a hardware accelerator, a NN accelerator, a ML accelerator).

[0077] Various embodiments of this disclosure may be implemented in a data processing system suitable for storing and / or executing program code, including at least one processor directly or indirectly connected to memory elements via a system bus. The memory elements include, for example, local memory used during the actual execution of the program code, bulk storage, and cache memory for temporarily storing at least some of the program code to reduce the number of times the code must be retrieved from bulk storage during execution.

[0078] I / O devices (including, but not limited to, keyboards, displays, pointing devices, DASDs, tapes, CDs, DVDs, USB memory, and other storage media) can be connected to the system directly or via an intermediary I / O controller. Network adapters are also connected to the system, enabling the data processing system to connect to other data processing systems, remote printers, or storage devices via private or public networks. Modems, cable modems, and Ethernet cards are just some of the types of network adapters available.

[0079] This disclosure may be embodied as a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions for causing a processor to perform an aspect of this disclosure. A computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. Computer-readable storage mediums include, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof. More specific examples of computer-readable storage mediums (but are not limited to) include portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disks, mechanically encoded devices such as punch cards or raised structures with instructions recorded in grooves, and appropriate combinations thereof.

[0080] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, a neutrino network, an optical network (e.g., Li-Fi, fiber optics), and / or a wireless network). The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives the computer-readable program instructions from the network, transfers the computer-readable program instructions, and stores them in a computer-readable storage medium within each computing / processing device.

[0081] Computer-readable program instructions for performing the operations of the Disclosure may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including smalltalk, object-oriented programming languages ​​such as C++, and conventional procedural programming languages ​​such as the "C" programming language or similar languages. A code segment or machine-executable instruction may represent a procedure, function, subprogram, program, routine, subroutine, module, software package, class, or any combination of instructions, data structures, and program statements. A code segment may be coupled to another code segment or hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, transferred, or transmitted via any suitable means, such as memory sharing, message passing, token passing, or network transmission. Computer-readable program instructions may run entirely on the user's computer, partially on the user's computer, run as a standalone software package, run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including local area networks (LANs) and wide area networks (WANs), or to an external computer (e.g., via the Internet through an Internet service provider).In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) can perform a computer-readable program instruction by individualizing the electronic circuit using state information of the computer-readable program instruction in order to perform an aspect of the computer-readable program instruction.

[0082] Aspects of this disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks within the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions. Various exemplary logic blocks, modules, circuits, and algorithmic steps described in relation to embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly demonstrate this hardware- and software compatibility, various exemplary components, blocks, modules, circuits, and steps have been generally described above with respect to their functionality. Whether such functionality is implemented as hardware or software depends on the design constraints imposed on the particular application and the overall system. A person skilled in the art may implement the described functionality in various ways for each particular application, but such a decision on implementation should not be construed as causing a departure from the scope of this disclosure.

[0083] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or part of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions described in a block may occur in an order different from the order shown in the figure. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or the blocks may be executed in reverse order depending on the functions involved. It should also be noted that each block shown in the block diagrams and / or flowcharts, and combinations of blocks shown in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system or a combination of dedicated hardware and computer instructions that performs a particular function or operation.

[0084] Words such as "next" and "then" are not intended to restrict the order of steps, but are simply used as guides to help the reader understand the explanation of the method. Process flowcharts may describe operations as sequential processes, but many operations can be performed in parallel or simultaneously. Furthermore, the order of operations may be rearranged. A process can correspond to a method, function, procedure, subroutine, subprogram, etc. If a process corresponds to a function, its termination may correspond to the function returning to the calling function or the main function.

[0085] Features or functionalities described in relation to a particular embodiment may be combined or partially combined in various other embodiments, and / or in combination with them. Similarly, different aspects and / or elements of some embodiments disclosed herein may also be combined and subcombined in a similar manner. Furthermore, some embodiments may, individually and / or collectively, constitute components of a larger system. In this case, other procedures may take precedence and / or modify its application. In addition, multiple steps may not be required before, after, and / or simultaneously with the embodiments disclosed herein. It should be noted that at least some of the methods and / or processes disclosed herein may, in any way, be performed by at least one entity or actor.

[0086] While preferred embodiments have been illustrated and described in detail herein, those skilled in the art will understand that various modifications, additions, substitutions, etc., are possible without departing from the spirit of this disclosure. Therefore, these are considered to fall within the scope of the disclosure as defined in the following claims.

Claims

1. A step of causing an input device to capture an image of an aggregate of foreign matter collected in the input-side section of a filter that can be installed or is installed to filter a directional flow of gas, wherein the aggregate of foreign matter is formed from the directional flow of the gas, and the image shows a pattern representing the aggregate of foreign matter. The input device causes the network interface to transmit the image, the network interface then transmits the image to the server. The process involves (i) causing the server to receive the image, (ii) causing it to detect the pattern, (iii) causing it to identify a descriptor for the pattern, and (iv) causing it to perform a calculation process based on the descriptor in response to the image being captured. A method for providing this.

2. The method according to claim 1, characterized in that the input device is a camera.

3. The method according to 2, characterized in that the camera is at least one of the following: an infrared camera, a monochrome camera, a telescope camera, a thermal camera, a dynamic camera, a static camera, a camera with a wide-angle lens, a webcam, a security or surveillance camera, a smartwatch camera, a pen camera, a digisscope camera, a three-dimensional (3D) camera, an ultraviolet (UV) camera, an area scan camera, a line scan camera, an electronic camera, a water-resistant camera, a waterproof camera, a high-sensitivity or low-light camera, a robotic camera, a holographic image sensor, a lapatronic camera, a microscope camera, a strobo-tachometer, or a laser sensor camera.

4. A step of causing the sensor to detect at least one of the aggregate of foreign matter, the section, or the gas, thereby forming a detection data set, The process involves the sensor transmitting the detection dataset to the network interface, and the network interface transmitting the detection dataset to the server. The process involves (i) causing the server to receive the detection dataset, (ii) causing the server to analyze the detection dataset, and the calculation process being based on the analyzed detection dataset. The method according to claim 1, further comprising:

5. The method according to claim 4, characterized in that the input device and the sensor are housed in a housing.

6. The method according to claim 4, characterized in that the input device is housed in a first housing and the sensor is housed in a second housing.

7. The method according to claim 1, characterized in that the input device is an ultrasonic transducer.

8. The method according to claim 1, characterized in that the input device is a radar.

9. The method according to claim 1, characterized in that the filter is mounted on a frame and the input device extends from the frame.

10. The method according to claim 1, characterized in that the input device extends from the filter.

11. The method according to claim 1, characterized in that the filter is attached to a frame and the input device is attached to a mounting portion extending from the frame.

12. The method according to claim 1, characterized in that the input device is attached to a mounting portion extending from the filter.

13. The method according to claim 1, characterized in that the input device is attached to at least one of a tripod, a bracket, or a base.

14. The method according to claim 1, characterized in that the input device is stabilized by a gyro stabilizer.

15. The method according to claim 1, characterized in that the input device is installed inside an unmanned aerial vehicle.

16. The method according to claim 1, characterized in that the input device is installed on the outside of the unmanned aerial vehicle.

17. The method according to claim 1, characterized in that the input device is a smartphone camera.

18. The method according to claim 1, characterized in that the input device is attached to an animal.

19. The method according to claim 1, characterized in that the input device is attached to a container.

20. The method according to claim 1, characterized in that the input device is attached to the articulated arm.

21. The method according to claim 1, characterized in that the input device is attached to at least one of the engine or the frame or chassis supporting the engine.

22. The method according to claim 1, characterized in that the input device is attached to a mounting portion for a diagnostic tool.

23. The method according to claim 1, characterized in that the input device is attached to a mounting portion located inside a protective case.

24. The method according to claim 1, characterized in that the input device is attached to a mounting portion extending from the display.

25. The method according to claim 1, characterized in that the input device is attached to a mounting portion located behind the glass.

26. The method according to claim 1, characterized in that the input device is attached to an extendable mounting portion.

27. The method according to claim 1, characterized in that the input device is attached to a mounting portion extending from a sensor that detects at least one of the aggregate of foreign matter, the section, or the gas.

28. The method according to claim 1, characterized in that the input device is attached to a mounting portion extending from a light source.

29. The method according to claim 1, characterized in that the input device and the filter are located inside a submersible offshore vehicle.

30. The method according to claim 1, characterized in that the section is a partial section.

31. The method according to claim 1, characterized in that the section is a complete section forming the input side.

32. The method according to claim 1, characterized in that the filter can be installed to filter the directional flow of the gas.

33. The method according to claim 1, characterized in that the filter is installed to filter out the directional flow of the gas, and the input device captures the image when the gas is not flowing in a directional manner.

34. The method according to claim 1, characterized in that the gas is air.

35. The method according to 17, characterized in that the directional flow of the gas is generated in a duct that transports the air by at least one blower or fan, and the air is conditioned or can be conditioned.

36. The gas is argon (Ar), methane (CH ８ , ２ , ２ , ２ , ７ , １２ , ２ , ６ , ３ , ４ , ３ , １６ , ６ , １０ , ５ , ６ , ５ , １４ , ２ , ４ , １２ , １４ , ５ ), carbon dioxide (CO ２ ), acetylene (C ２ H ２ ), ethylene (C ２ H ４ ), propane (C ３ H ８ ), hydrogen (H ２ ), parahydrogen (p-H ２ ), ammonia (NH ３ ), nitrogen (N ２ ), oxygen (O ２ ), helium (He), xenon (Xe), krypton (Kr), neon (Ne), carbon monoxide (CO), n-butane (C ４ H １０ ), 1-butene (C ４ H ８ ), 2-cis-butene (C ４ H ８ ), 2-trans-butene (C ４ H ８ ), cyclopropane (C ３ H ６ ), deuterium (D ２ ), dimethyl ether ((CH ３ ) ２ O), ethane (C ２ H ６ ), fluorine (F ２ ), normal heptane (C ７ H １６ ), normal hexane (C ６ H １４ ), hydrogen sulfide (H ２ S), isobutane (C ４ H １０ ), isobutene (C ４ H ８ ), isohexane (C ６ H １４ ), isopentane (C ５ H １２ ), methanol (CH ３ OH), neopentane (C ５ H １２ [[ID=9​​​​​​ １２ ), hydrogen chloride (HCl), nitric oxide (NO), propene (C) ３ H ６ ), propin (C ３ H ４ ), sulfur dioxide (SO ２ ), sulfur hexafluoride (SF ６ ), Toluene (C ７ H ８ ), trifluoroiodomethane (CF ３ I) Water flow or water vapor (H ２ O), Bromochlorodifluoromethane (CBrClF ２ ), methyl bromide (CH ３ Br), 1,1,2-trichloro-1,2,2-trifluoroethane (C ２ F ３ Cl ３ ), 1-chloro-1,1-difluoroethane (C ２ H ３ CLF ２ ), 1-chlorodifluoromethane (HClF ２ ), 1-chlorotrifluoromethane (CCl ２ F ４ ), 1,2-dichloro-1,1,2,2-tetrafluoroethane (C ２ Cl ２ F ４ ), dichlorodifluoromethene (CCl ２ F ２ ), dichlorofluoromethane (CCl １２ F), 2-chloro-1,1,1-trifluoroethane (C ２ H ２ CLF ３ ), chloropentafluoroethane (C ２ CLF ５ ), methyl chloride (CH ３ Cl), trifluoromethane bromide (CBrF ３ ), nitrogen dioxide (NO ２ ), silane (SiH ４ ), trichloromonofluoromethane (CCl ３ F), or ozone (O ３ The method according to claim 1, characterized in that it is at least one of the following.

37. The method according to claim 1, characterized in that the aggregate of foreign matter contains at least one particle of plant components, animal components, or inorganic material.

38. The method according to 37, characterized in that at least one of the plant component, the animal component, or the inorganic material particles are rock, paper, dust, lint, glass fiber, skin, hair, insects, animals, birds, smoke, trees, weeds, grass, pollen, mold, bacteria, viruses, or fungi.

39. The method according to claim 1, characterized in that the network interface transmits the image to the server via at least one of a local area network (LAN), a wide area network (WAN), a personal area network (PAN), a mesh network, a satellite network, a neutrino network, or a cellular network.

40. The method according to claim 1, wherein the aforementioned image is a first image, and the server detects the pattern by accessing a second image showing the section without the pattern, and by identifying the difference between the first image and the second image (the difference being a pattern).

41. The method according to claim 1, characterized in that the server identifies the descriptor for the pattern by detecting at least one of the patterns or by querying a database that stores a group of patterns and a group of pattern descriptors corresponding to the group of patterns.

42. The method according to 41, characterized in that the database is located locally with respect to the server.

43. The method according to 41, characterized in that the database is located remotely from the server.

44. The method according to claim 1, characterized in that the calculation process involves causing the server to send a message to a computing terminal, and the computing terminal outputs content based on the descriptor.

45. The method according to 44, characterized in that the content includes a descriptor.

46. The method according to 44, characterized in that the computing terminal hosts the input device.

47. The method according to 44, characterized in that the computing terminal is located in the same place as at least one of the input device or the filter.

48. The method according to 44, characterized in that the computing terminal is not located in the same place as at least one of the input device or the filter.

49. The method according to 44, characterized in that the content indicates at least one of the following: that the filter should be replaced or cleaned.

50. The method according to 44, characterized in that the content includes recommendations for a user profile associated with at least one of the input devices or the filter.

51. The method according to 50, characterized in that the aforementioned recommendation is a medical health recommendation.

52. The method according to 51, characterized in that the user profile includes a set of medical and health attributes, and the medical and health recommendations are personalized based on the set of personal attributes.

53. The method according to 50, characterized in that the recommendation includes a uniform resource locator (URL) to a web page containing a set of information related to the descriptor.

54. The method according to 53, characterized in that the web page describes a physical product related to the descriptor.

55. The method according to 50, characterized in that the recommendation is customized to a weather forecast associated with at least one of the input device, the filter, or the computing terminal.

56. The method according to 50, characterized in that the recommendation is customized to an environmental dataset collected based on geographic location information associated with at least one of the input device, the filter, or the computing terminal.

57. The method according to claim 1, characterized in that the server detects the pattern based on at least one of the color, wavelength or frequency of the signal, pixel variation, or shape.

58. The method according to claim 1, characterized in that the directional flow of the gas is inside at least one of a land vehicle, aircraft, ship, or spacecraft.

59. The method according to claim 1, wherein the calculation process involves the server forming a dataset, the dataset being based on or including the descriptor, and the dataset being transmitted to at least one of a third-party server, a third-party POS terminal, or a third-party computing terminal.

60. The method according to claim 1, characterized in that the calculation process is based on the descriptor or presents a report containing the descriptor to a computing terminal.

61. A step of causing an input device to capture an image of an aggregate of foreign matter collected in the input section of a filter that can be installed or is installed to filter a directional flow of gas, wherein the aggregate of foreign matter is formed from the directional flow of the gas, and the image shows a pattern representing the aggregate of foreign matter. The process involves the input device transmitting the image to a processor, the processor (i) detecting the pattern, (ii) identifying a descriptor for the pattern, and (iii) performing a calculation process based on the descriptor in response to the image being captured, wherein the input device and the processor are located in the same place. A method for providing this.

62. The method according to 61, characterized in that the input device is a camera.

63. The method according to 62, characterized in that the camera is at least one of the following: an infrared camera, a monochrome camera, a telescope camera, a thermal camera, a dynamic camera, a static camera, a camera with a wide-angle lens, a webcam, a security or surveillance camera, a smartwatch camera, a pen camera, a digisscope camera, a three-dimensional (3D) camera, an ultraviolet (UV) camera, an area scan camera, a line scan camera, an electronic camera, a water-resistant camera, a waterproof camera, a high-sensitivity or low-light camera, a robotic camera, a holographic image sensor, a lapatronic camera, a microscope camera, a strobo-tachometer, or a laser sensor camera.

64. A step of causing the sensor to detect at least one of the aggregate of foreign matter, the section, or the gas, thereby forming a detection data set, The process involves the sensor transmitting the detection dataset to the processor, the processor (i) receiving the detection dataset, (ii) analyzing the detection dataset, and the calculation process being based on the analyzed detection dataset. The method according to claim 61, further comprising:

65. The method according to 64, characterized in that the input device and the sensor are housed in a housing.

66. The method according to 64, characterized in that the input device is housed in a first housing and the sensor is housed in a second housing.

67. The method according to 61, characterized in that the input device is an ultrasonic transducer.

68. The method according to 61, characterized in that the input device is a radar.

69. The method according to 61, characterized in that the filter is mounted on a frame and the input device extends from the frame.

70. The method according to 61, characterized in that the input device extends from the filter.

71. The method according to 61, characterized in that the filter is attached to a frame and the input device is attached to a mounting portion extending from the frame.

72. The method according to 61, characterized in that the input device is attached to a mounting portion extending from the filter.

73. The method according to 61, characterized in that the input device is attached to at least one of a tripod, a bracket, or a base.

74. The method according to 61, characterized in that the input device is stabilized by a gyro stabilizer.

75. The method according to 61, characterized in that the input device is installed inside an unmanned aerial vehicle.

76. The method according to 61, characterized in that the input device is installed outside the unmanned aerial vehicle.

77. The method according to 61, characterized in that the input device is a smartphone camera.

78. The method according to 61, characterized in that the input device is attached to an animal.

79. The method according to 61, characterized in that the input device is attached to the container.

80. The method according to 61, characterized in that the input device is attached to the articulated arm.

81. The method according to 61, characterized in that the input device is attached to at least one of the engine or the frame or chassis supporting the engine.

82. The method according to 61, characterized in that the input device is attached to the mounting portion for the diagnostic tool.

83. The method according to 61, characterized in that the input device is attached to a mounting portion located inside a protective case.

84. The method according to 61, characterized in that the input device is attached to a mounting portion extending from the display.

85. The method according to 61, characterized in that the input device is attached to a mounting portion located behind the glass.

86. The method according to 61, characterized in that the input device is attached to an extendable mounting portion.

87. The method according to 61, characterized in that the input device is attached to a mounting portion extending from a sensor that detects at least one of the aggregate of foreign matter, the section, or the gas.

88. The method according to 61, characterized in that the input device is attached to a mounting portion extending from the light source.

89. The method according to 61, characterized in that the input device and the filter are located inside a submersible offshore vehicle.

90. The method according to 61, characterized in that the section is a partial section.

91. The method according to 61, characterized in that the section is a complete section forming the input side.

92. The method according to 61, characterized in that the filter can be installed to filter the directional flow of the gas.

93. The method according to 61, characterized in that the filter is installed to filter out the directional flow of the gas, and the input device captures the image when the gas is not flowing in a directional manner.

94. The method according to 61, characterized in that the gas is air.

95. The method according to 94, characterized in that the directional flow of the gas is generated in a duct that transports the air by at least one blower or fan, and the air is conditioned or can be conditioned.

96. The gas is argon (Ar), methane (CH ４ ), carbon dioxide (CO ２ ), acetylene (C ２ H ２ ), ethylene (C ２ H ４ ), propane (C[[ID=1十三]] ３ H ８ ), hydrogen (H ２ ), parahydrogen (p-H ２ ), ammonia (NH ３ ), nitrogen (N ２ ), oxygen (O ２ ), helium (He), xenon (Xe), krypton (Kr), neon (Ne), carbon monoxide (CO), n-butane (C ４ H<00001十2>), 1-butene (C ４ [[ID=3十2]]H ８ ), 2-cis-butene (C ４ H ８ ), 2-trans-butene (C ４ H ８ ), cyclopropane (C ３ H ６ ), deuterium (D ２ ), dimethyl ether ((CH ３ ) ２ O), ethane (C ２ H ６ ), fluorine (F ２ ), normal heptane (C ７ H １６ ), normal hexane (C<00001十一9>H １４ ), hydrogen sulfide (H[[ID=六十7]] ２ S), isobutane (C ４ H １０ ), isobutene (C<00^00124>H ８ ), isohexane (C ６ H １４ ), isopentane (C ５ H １２ ), methanol (CH ３ OH), neopentane (C ５ H １２ ), nitrous oxide (N ２ O), nitrogen trifluoride (NF ３ ), n-pentane (C ５ H １２ ), hydrogen chloride (HCl), nitric oxide (NO), propene (C) ３ H ６ ), propin (C ３ H ４ ), sulfur dioxide (SO ２ ), sulfur hexafluoride (SF ６ ), Toluene (C ７ H ８ ), trifluoroiodomethane (CF ３ I) Water flow or water vapor (H ２ O), Bromochlorodifluoromethane (CBrClF ２ ), methyl bromide (CH ３ Br), 1,1,2-trichloro-1,2,2-trifluoroethane (C ２ F ３ Cl ３ ), 1-chloro-1,1-difluoroethane (C ２ H ３ CLF ２ ), 1-chlorodifluoromethane (HClF ２ ), 1-chlorotrifluoromethane (CCl ２ F ４ ), 1,2-dichloro-1,1,2,2-tetrafluoroethane (C ２ Cl ２ F ４ ), dichlorodifluoromethene (CCl ２ F ２ ), dichlorofluoromethane (CCl １２ F), 2-chloro-1,1,1-trifluoroethane (C ２ H ２ CLF ３ ), chloropentafluoroethane (C ２ CLF ５ ), methyl chloride (CH ３ Cl), trifluoromethane bromide (CBrF ３ ), nitrogen dioxide (NO ２ ), silane (SiH ４ ), trichloromonofluoromethane (CCl ３ F), or ozone (O ３ The method according to 61, characterized in that it is at least one of the following.

97. The method according to 61, characterized in that the aggregate of foreign matter contains at least one particle of plant components, animal components, or inorganic material.

98. The method according to 97, characterized in that at least one of the plant component, the animal component, or the inorganic material particles are rock, paper, dust, lint, glass fiber, skin, hair, insects, animals, birds, smoke, trees, weeds, grass, pollen, mold, bacteria, viruses, or fungi.

99. The method according to 61, wherein the image is a first image, and the processor detects the pattern by accessing a second image showing the section without the pattern, and by identifying the difference between the first image and the second image (the difference being the pattern).

100. The method according to 61, wherein the processor identifies the descriptor for the pattern by detecting at least one of the patterns or by querying a database that stores a group of patterns and a group of pattern descriptors corresponding to the group of patterns, and the database is located in the same place as at least one of the input device or the processor.

101. The method according to 61, wherein the calculation process involves instructing the processor to output content based on the descriptor, and the output device is located in the same place as at least one of the input device or the processor.

102. The method according to 101, characterized in that the content includes a descriptor.

103. The method according to 101, characterized in that the input device and the processor are housed in a casing.

104. The method according to 101, characterized in that the content indicates at least one of the following: that the filter should be replaced or cleaned.

105. The method according to 101, characterized in that the content includes recommendations for a user profile associated with at least one of the input devices or the filters.

106. The method according to 105, characterized in that the aforementioned recommendation is a medical health recommendation.

107. The method according to 106, characterized in that the user profile includes a set of medical and health attributes, and the medical and health recommendations are personalized based on the set of personal attributes.

108. The method according to 105, characterized in that the recommendation includes a uniform resource locator (URL) to a web page containing a set of information related to the descriptor.

109. The method according to 108, characterized in that the web page describes a physical product related to the descriptor.

110. The method according to 105, characterized in that the recommendation is customized to a weather forecast associated with at least one of the input device, the filter, or the computing terminal.

111. The method according to 105, characterized in that the recommendation is customized to an environmental dataset collected based on geographic location information associated with at least one of the input device, the filter, or the computing terminal.

112. The method according to 61, characterized in that the processor detects the pattern based on at least one of the color, wavelength or frequency of the signal, the variation in pixels, or the shape.

113. The method according to 61, characterized in that the directional flow of the gas is inside at least one of a land vehicle, aircraft, ship, or spacecraft.

114. The method according to 61, wherein the calculation process involves the processor forming a dataset, the dataset being based on or including the descriptor, and the dataset being transmitted to at least one of a third-party server, a third-party POS terminal, or a third-party computing terminal.

115. The method according to 61, characterized in that the calculation process is based on the descriptor or presents a report containing the descriptor to a computing terminal.

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