Patent Application
20240411837
With large scale agriculture, crops typically are observed, measured, and/or interacted with in a relatively coarse manner. For example, data gathered from sparse sampling may be used to extrapolate crop yields, disease diagnoses, and/or pest presence/population for entire plots of plants. This can lead to less-than-ideal agricultural practices such as over/under application of fertilizer or other chemicals, over/under remediation of weeds and/or pests, and so forth. These agricultural practices may yield less than optical crop yields because healthy plants may be destroyed or damaged, unhealthy plants may be inadequately remediated, etc.
“Precision agriculture” refers to techniques for observing, measuring, and/or interacting with (e.g., harvesting, applying chemicals, pruning, etc.) crops in a highly targeted and granular manner, including at the level of individual, localized plants. Precision agriculture may improve crop yields and increase agricultural efficiency and/or land use overall. As agricultural robots become increasingly available and capable, precision agriculture has become more feasible, technologically and economically, with localization of individual plants being a key feature. However, existing plant localization techniques suffer from various shortcomings, such as being computationally expensive, error-prone, and/or too time-consuming.
Implementations are described herein for localizing individual plants by aligning high-elevation images using invariant anchor points instead of variant feature points, such as deformable plants themselves, which tend to be disproportionately prevalent in agricultural areas. In some implementations, rather than blending or fusing a global image together from multiple high-elevation images-which can be computationally expensive-multiple high-elevation images may be aligned such that invariant anchor points across different high-elevation images can be localized relative to each other. With the invariant anchor points localized, individual high-elevation images can be independently processed to localize individual plants relative to the invariant anchor points.
In some implementations, a method for localizing one or more individual plants of a plurality of plants may be implemented using one or more processors and may include: obtaining high-elevation images that capture the plurality of plants at a resolution at which wind-triggered deformation of individual plants is perceptible between the high-elevation images; classifying a first set of regions of the high-elevation images that depict the plurality of plants as variant features that are unusable as invariant anchor points; classifying a second set of regions of the high-elevation images that are disjoint from the first set of regions as invariant anchor points; aligning the high-elevation images based on one or more of the invariant anchor points that are common among at least some of the high-elevation images; and based on the aligned high-elevation images, localizing one or more individual plants of the plurality of plants within one of the high-elevation images for performance of one or more agricultural tasks.
In various implementations, the localizing may include assigning position coordinates to the one or more individual plants based on position coordinates generated by an airborne vehicle that acquired the high-elevation images. In various implementations, the localizing may include mapping the one or more individual plants to one or more rows of a plurality of rows in which the plurality of plants are arranged. In various implementations, the method may include processing the aligned high-elevation images to generate a map of the plurality of rows relative to the invariant anchor points.
In various implementations, each region of the first and second sets of regions may be pixel-wise classified. In various implementations, the method may include deploying one or more agricultural robots to the one or more localized individual plants to perform one or more of the agricultural tasks. In various implementations, the high-elevation images are acquired by an unmanned aerial vehicle (UAV).
In various implementations, classifying the first set of regions may include processing the high-elevation images based on one or more machine learning models that are trained to recognize one or more plant types. In various implementations, classifying the second set of regions may include detecting one or more lodged plants among the plurality of plants. In various implementations, classifying the second set of regions may include detecting agricultural equipment in spatial proximity with the plurality of plants. In various implementations, classifying the second set of regions may include detecting one or more water features or roads in spatial proximity with the plurality of plants.
In addition, some implementations include one or more processors (e.g., central processing unit(s) (CPU(s)), graphics processing unit(s) (GPU(s), and/or tensor processing unit(s) (TPU(s)) of one or more computing devices, where the one or more processors are operable to execute instructions stored in associated memory, and where the instructions are configured to cause performance of any of the aforementioned methods. Some implementations also include one or more non-transitory computer readable storage media storing computer instructions executable by one or more processors to perform any of the aforementioned methods.
It should be appreciated that all combinations of the foregoing concepts and additional concepts described in greater detail herein are contemplated as being part of the subject matter disclosed herein. For example, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the subject matter disclosed herein.
The ability to localize individual plants may be useful for a variety of purposes. For precision agriculture, it may be beneficial to localize individual plants so that agricultural personnel and/or robots are able to locate and perform fine-grained agricultural tasks on individual plants. As another example, localizing individual plants may allow for tracking of individual plants' growth (or lack thereof), disease progression, fruit development (or lack thereof), and so forth. Tracking these metrics enables other applications, such as distinguishing portions of crop fields that are not meeting growth expectations (e.g., due to disease, insufficient or too much irrigation, insufficient or too much fertilizer, etc.) from other portions that are meeting growth expectations.
While individual plant localization is possible using ground-based equipment such as robots, many crop fields and/or farms are exceedingly large, making land-based localization cumbersome at best and impractical at worst. Localization based on high-elevation imagery captured by airborne devices, such as a balloon or an unmanned aerial vehicle (UAV), may be performed more efficiently than land-based localization. However, air-based localization presents its own challenges.
In many cases, multiple high-elevation images captured by an airborne device such as a UAV are combined into a global image using a stitching or “mosaicking” for purposes of georeferencing individual plants based on GPS coordinates of the airborne device. However, given the typically high resolution of this imagery, stitching techniques that require calibration, registration, and/or blending/fusing may be too computationally expensive for real-time or near-real-time localization. While feature-based stitching techniques are faster than direct stitching techniques (e.g., Fourier analysis), they also present unique challenges in the agricultural context where highly-variant and homogenous-appearing crops tend to dominate other visual features. For instance, deformable plants such as corn, soybean, wheat, oats, etc., may tend to sway and/or otherwise deform with the wind, introducing distortion into the stitching process.
Implementations are described herein for localizing individual plants based on high-elevation imagery. More particularly, but not exclusively, implementations are described herein for localizing individual plants by aligning high-elevation images using invariant anchor points instead of variant feature points, such as deformable plants themselves, which tend to be disproportionately prevalent in agricultural areas. In some implementations, rather than blending or fusing a global image together from multiple high-elevation images-which as noted previously can be computationally expensive-multiple high-elevation images may be aligned such that invariant anchor points across different high-elevation images can be localized relative to each other. Aligning two or more high-elevation images may include, for instance, translating one or both images relative to the other, rotating one or both images relative to the other, morphing one or both images relative to the other, etc. With the invariant anchor points localized, individual high-elevation images can be independently processed to localize individual plants relative to the invariant anchor points.
In various implementations, high-elevation images may be acquired, e.g., by an airborne vehicle such as a UAV, an airplane, or a balloon. These high-elevation images may be acquired at an elevation at which individual plants are perceptible, but also at which deformation of the individual plants is perceptible between the high-elevation images. For example, the images may be acquired at an elevation at which swaying of individual plants caused by the wind would introduce distortion into a traditional image stitching process. This elevation may vary depending on the types and/or sizes of the crops, and may be between ten meters and one hundred meters in some cases, or higher in others.
In some implementations, the high elevation images may be processed to classify regions (e.g., clusters of pixels, bounded areas) depicting deformable plants as variant features. These variant features may then be disregarded when multiple high-elevation images are aligned with each for plant localization purposes. Instead, other regions of the high-elevation images that are disjoint from the regions classified as variant features may be classified as invariant anchor points. These invariant anchor points, and in particular, invariant anchor points that are common across multiple high-elevation images, may be used to align the high-elevation images.
Based on the aligned high-elevation images—which need not be blended or fused into a global image—individual plants may be localized for subsequent (e.g., downstream) performance of agricultural tasks on those individual plants. These agricultural tasks may be performed automatically by agricultural robots who are deployed to the localized plants, or they may be performed by agricultural personnel who are provided with directions, a map, or other data they can use to find their way to the localized plants.
Invariant anchor points may include any visual feature that is not perceptively deformed (e.g., does not sway in the wind) from one high-elevation image to the next. Invariant anchor points may be man-made, natural, or any combination thereof. Man-made invariant anchor points may include, for instance, stationary or idle farm equipment, water features such as ponds, roads, trails, irrigation equipment such as sprinklers, valves, or pipes, flags, electrical units, fences, weather equipment, buildings, and so forth. Natural invariant anchor points may include, for instance, natural creeks or ponds, large and/or non-deformable trees or bushes, rocks, and so forth. Other invariant anchor points may include, for instance, holes in rows of plants where individual plants are missing or lodged (and hence, cannot sway in the breeze).
In contrast to invariant anchor points, variant features may include plants that are susceptible to deformation, e.g., due to wind, that is perceptible across multiple high-elevation images. As noted previously, this deformation may introduce noise into conventional image stitching processes. Thus, in various implementations described herein, these variant features may be disregarded for purposes of aligning multiple high-elevation images with each other based on common invariant anchor points. Moreover, various aspects of conventional image stitching such as calibration and blending and/or fusing that are negatively influenced by variant features are not necessary for localization of individual plants using techniques described herein. Rather than being localized within a single global stitched/mosaicked image, with disclosed techniques, individual plants may be localized within individual high-elevation images, independently from other high-elevation images.
As noted previously, the non-fused and aligned high-elevation images can be analyzed to localize individual plants. This localization may be performed in various ways. In some implementations, the aligned plurality of high-elevation images may be processed to generate a map of a plurality of rows of plants relative to a plurality of invariant anchor points. Individual plants may then be mapped to respective rows of the plurality of rows. Thus, for instance, an individual plant can be localized as being “three rows west and two plots north of invariant anchor point 1001.” An invariant anchor point itself may be localized as being “ten rows west and five plants south from field corner C.”
In some implementations, the localization may include assigning position coordinates, such as Global Positioning System (GPS) coordinates, to individual plants. For example, if GPS coordinates of locations (especially three or more locations) such as invariant anchor points and/or field corners can be determined, then those GPS coordinates can be used to interpolate GPS coordinates of individual plants relative to those locations.
Agricultural area(s) 112 may be used to grow various types of crops that may produce plant parts of economic and/or nutritional interest. Agricultural area(s) 112 may include, for instance, one or more plots of multiple rows of crops, one or more gardens, one or more greenhouses, or any other areas in which there may be an interest or desire to automatically detect, classify, and/or segment particular types of plants. Plants that are captured in digital imagery and analyzed using techniques described herein may take any form depending on the circumstances. Some plants may be considered desirable, and may include, for instance, strawberry plants, raspberries, sugar beets, soybeans, beans, corn, cabbage, lettuce, spinach, wheat, berries, etc. Other plants may be considered undesirable (e.g., weeds), and may include, for instance, dandelions, waterhemp, giant ragweed, marestail (also commonly known as horseweed), common lambsquarters, common groundsel, field pansy, mayweed or dog fennel, pineapple-weed or chamomile, several pigweeds, and white campion, yellow woodsorrel, yellow nutsedge, etc.
An individual (which in the current context may also be referred to as a “user”) may operate one or more client devices 1061-x to interact with other components depicted in
Plant knowledge system 104 is an example of an information system in which the techniques described herein may be implemented. Each of client devices 106 and plant knowledge system 104 may include one or more memories for storage of data and software applications, one or more processors for accessing data and executing applications, and other components that facilitate communication over a network. The operations performed by client device 106 and/or plant knowledge system 104 may be distributed across multiple computer systems.
Each client device 106 may operate a variety of different applications that may be used to perform various agricultural tasks, such as crop yield prediction, plant-part-of-interest inventory, crop disease diagnosis, etc. For example, a first client device 1061 operates agricultural (“AG”) client 107 (e.g., which may be standalone or part of another application, such as part of a web browser). Another client device 106x may take the form of a HMD that is configured to render 2D and/or 3D data to a wearer as part of a VR immersive computing experience. For example, the wearer of client device 106x may be presented with 3D point clouds representing various aspects of objects of interest, such as fruits of crops, weeds, crop yield predictions, etc. The wearer may interact with the presented data, e.g., using HMD input techniques such as gaze directions, blinks, etc.
In some implementations, AG client 107 may be used to communicate to agricultural personnel instructions and/or information that can help them perform various agricultural tasks. For example, a farm task module 113 may generate a report, a map, instructions, and/or any other data that may be presented to an operator of a client device 106 using a graphical user interface, audibly, etc. These data may inform the agricultural personnel where targeted individual plants—e.g., weeds, crops ready to be harvested, diseased crops, underperforming or over-performing crops, pest-infested crops, etc.—are located (or “localized”), what action(s) should be taken on those plants, a timeframe in which those action(s) should be taken, etc.
In some implementations, one or more robots 1081-M may be deployed to perform various agricultural tasks. An individual robot 1081-M may take various forms, such as an unmanned aerial vehicle (UAV) 1081, a robot (not depicted) that is propelled along a wire, track, rail or other similar component that passes over and/or between crops, a wheeled robot 108 M, or any other form of robot capable of being propelled or propelling itself past crops of interest. In some implementations, different robots may have different roles, e.g., depending on their capabilities. For example, in some implementations, one or more of robots 1081-M may be designed to capture data, others may be designed to manipulate plants or perform physical agricultural tasks, and/or others may do both. Robots 108 may include various types of sensors, such as vision sensors (e.g., 2D digital cameras, 3D cameras, 2.5D cameras, infrared cameras), inertial measurement unit (“IMU”) sensors, Global Positioning System (“GPS”) sensors, X-ray sensors, moisture sensors, lasers, barometers (for local weather information), photodiodes (e.g., for sunlight), thermometers, etc.
In various implementations, plant knowledge system 104 may be implemented across one or more computing systems that may or may not be referred to as the “cloud.” Plant knowledge system 104 may receive vision data generated by robots 1081-M and/or any other sources of digital imagery capturing agricultural area 112, and process it using various image processing techniques to perform tasks such as plant localization, detection, classification, and/or segmentation. In various implementations, plant knowledge system 104 may include a vision data module 114 and an inference module 118. In some implementations one or more of modules 114 and 118 may be omitted, combined, and/or implemented in a component that is separate from plant knowledge system 104.
Plant knowledge system 104 may also include one or more databases. For example, plant knowledge system 104 may include, in communication with vision data module 114, an imagery database 116 for storing image data captured by, for instance, agricultural personnel and/or one or more robots 1081-M. Plant knowledge system 104 may also include a machine learning model database 120 that includes one or more machine learning models that may be trained, for instance, to detect plants at various levels of granularity. In this specification, the term “database” and “index” will be used broadly to refer to any collection of data. The data of the database and/or the index does not need to be structured in any particular way and it can be stored on storage devices in one or more geographic locations.
Vision data module 114 may be configured to obtain digital images and/or other imagery data from various sources, such as imagery database 116 purposed as an imagery clearinghouse, as well as from sources such as robots 1081-M (particularly relevant to the present disclosure is high-elevation imagery captured by UAV 1081) and/or imagery captured by agricultural personnel. Vision data module 114 may then provide these imagery data to inference module 118. In other implementations, vision data module 114 may be omitted and the functions described herein as being performed by vision data module 114 may be performed by other components of plant knowledge system 104, such as inference module 118.
Inference module 118 may be configured to process imagery data received from vision data module 114 as input based on various machine learning models stored in machine learning model database 120 to generate output. This output may include classifications and/or annotations, at various levels of granularity, of plants and/or other objects that are detected and/or segmented in imagery data received from vision data module 114. To this end, machine learning models stored in database 120 may be trained to detect, classify, and/or segment plants/plant parts within imagery data, such as high-elevation imagery capturing agricultural area(s) 112 that is acquired by UAV 1081.
Various types of machine learning models may be trained to detect, classify, annotate, and/or segment particular plants and/or other plant parts of interest in imagery data. In some implementations, a convolutional neural network (“CNN”) may be trained (e.g., based on human-annotated ground truth images, based on synthetic training data, etc.) to generate output indicative of one or more types and/or categories of plants detected in digital imagery. In
An alignment module 124 may be configured to align high-elevation images captured by UAV 1081 based on one or more invariant anchor points that are common among at least some of the high-elevation images. For example, alignment module 124 may identify, from annotated images 122, annotated (e.g., pixel-wise annotated, bounding shape annotated) invariant anchor points such as stationary or idle farm equipment, lodged plants, holes in rows of plants, irrigation equipment such as sprinklers or cisterns, wells, water features, large stones or arrangements of smaller stones, roads, buildings, flags, indicia printed on mulch, etc. Alignment module 124 may also match one or more invariant anchor features identified in one high-elevation image with the same one or more invariant anchor features in another high-elevation image.
Alignment module 124 may perform this matching of invariant anchor points using various techniques. For example, alignment module 124 may compare distances between embeddings generated from invariant anchor points in embedding space. Additionally or alternatively, alignment module 124 may apply other techniques, such as appearance-based methods such as edge matching, divide-and-conquer, gradient matching, greyscale matching, histograms, and/or feature-based methods such as interpretation trees, pose clustering, geometric hashing, scale-invariant feature transform (“SIFT”), speeded up robust features (“SURF”), trained machine learning models (e.g., convolutional neural networks), and so forth.
Based on the aligned high-elevation images generated by alignment module 124, localization module 126 may be configured to localize one or more individual plants of a plurality of plants (e.g., a crop field) within any individual high-elevation image of the aligned high-elevation images. For example, localization module 126 may store relative and/or absolute position coordinates of the plant in a mapping/localization database 130. Relative position coordinates may include, for instance, directions to the plant from one or more reference points, such as one or more invariant anchor points, or inertial measurement unit (IMU) coordinates. Absolute position coordinates may include, for instance, GPS coordinates of the localized plant.
The localized plant data (e.g., absolute or relative position coordinates) may be provided to farm task module 113 so that farm task module 113 can cause one or more agricultural tasks to be performed on the localized plant. These agricultural tasks may vary, and may include, but are not limited to, picking the plant, harvesting fruit from the plant, treating the plant with a chemical and/or fertilizer, trimming the plant, destroying the plant, etc. These agricultural tasks may be performed by agricultural personnel (e.g., by providing them with a map including directions to the localized plant) and/or by agricultural robots.
In some implementations, a mapping module 128 may be configured to generate maps of an agricultural area, which in some cases may be based on and/or incorporate localization data generated by localization module 126. For example, mapping module 128 may map one or more individual plants to one or more rows of a plurality of rows in which a plurality of plants are arranged. In some implementations, mapping module 128 may process the aligned high-elevation images, e.g., generated by alignment module 124, to generate a map of the plurality of rows relative to the invariant anchor points. This map may be stored, for instance, in mapping/localization database 130. In various implementations, farm task module 113 may use mapping data and localization data stored in database 130 to deploy humans and/or robots to perform agricultural tasks.
Inference module 118 may classify a second set of regions of the high-elevation images that are disjoint from the first set of regions as invariant anchor points. Various candidate invariant anchor points are depicted in
In some implementations, all regions that are not classified as variant features may be classified as anchor points. Additionally or alternatively, in some implementations, object recognition techniques such as CNNs may be applied to classify particular objects as types that are usable as invariant anchor points. In the latter case, agricultural personnel may be provided with directions that are more intuitive to follow because invariant features may be identified in the directions by type (e.g., “east of sprinkler,” “south of cistern”) rather than simply referring to positions in rows of crops.
In various implementations, alignment module 124 may be configured to align high-elevation images 360A, 360B, which were previously annotated by inference module 118 into regions with variant features and regions with invariant anchor points. In various implementations, this alignment may be based on one or more of the invariant anchor points that are common among the high-elevation images. In
Once the images 360A, 360B are aligned as depicted in
In some implementations, individual plants and/or invariant anchor points may be localized using absolute position coordinates, in addition to or instead of relative position coordinates. For example, GPS coordinates of particular visual features, such as corners of plots 240, 242, may be known and/or ascertainable from a GPS coordinate of UAV 1081. If multiple GPS coordinates are ascertained for multiple invariant anchor points in a given high-elevation image, then it is possible to interpolate GPS coordinates for individual plants relative to those invariant anchor point GPS coordinates.
At block 502, the system may obtain high-elevation images (e.g., 360A, 360B) that capture a plurality of plants at a resolution at which wind-triggered deformation of individual plants is (e.g., would be) perceptible between the high-elevation images. For example, UAV 1081 may be flown over agricultural area 112 or 212 at an elevation such as between ten and one hundred or more meters and may capture high-elevation images (e.g., 360A, 360B) at some predetermined frequency, such as once every two seconds, once every three seconds, etc. In some implementations, these high-elevation images may be obtained for plant knowledge system 104 by vision data module 114.
At block 504, the system, e.g., by way of inference module 118, may classify a first set of regions of the high-elevation images that depict the plurality of plants as variant features that are unusable as invariant anchor points. For example, inference module 118 may use one or more CNNs trained to classify regions as depicting plants of particular types and/or plants in general (e.g., terrain classification) to process the high-elevation digital images obtained at block 502. These region classifications may take various forms, such as pixel-wise and/or bounding shape annotations.
At block 506, the system, e.g., by way of inference module 118, may classify (e.g., via pixel-wise or bounding shape annotation) a second set of regions of the high-elevation images that are disjoint from the first set of regions as invariant anchor points. As noted previously, these regions may simply be those that are excluded from the first set of regions that were classified as invariant features. Additionally or alternatively, inference module 118 may apply various object recognition techniques (e.g., using one or more trained CNNs) to classify, as invariant anchor points, objects that are known to be insufficiently deformable or otherwise sufficiently invariant. As noted previously, these invariant anchor points may include, for instance, farm equipment, man-made structures, natural features that are not purely transient, dead/lodged plants, etc. In some implementations, a CNN may be trained to classify an individual plant into one of multiple categories, such as “pre-harvest,” “healthy,” “harvest-ready,” “infested,” “overwatered,” “dead,” and/or “lodged,” to name a few. In some such implementations, plants classified into the “dead” or “lodged” categories may be treated as invariant anchor points, whereas plants classified into the other categories may be treated as variant feature points.
At block 508, the system, e.g., by way of alignment module 124, may align the high-elevation images (e.g., 360A, 360B) based on one or more of the invariant anchor points that are common among at least some of the high-elevation images. An example of this was depicted in
In some implementations, at block 510, the system, e.g., by way of mapping module 128, may process the aligned high-elevation images to generate a map of a plurality of rows relative to the invariant anchor points. For example, object recognition processing may be applied to identify rows of plants in the digital images. These identified rows may be used by mapping module 128 to generate a map of an agricultural area that also includes invariant anchor points. The mapping of block 510 may be performed by the system at any point relative to the other operations of method 500.
Based on the aligned high-elevation images, and if applicable, the mapping at block 510, at block 512, the system, e.g., by way of localization module 126, may localize one or more individual plants of the plurality of plants within one of the high-elevation images for performance of one or more agricultural tasks. In some implementations, object recognition processing may be applied to assign individual plants relative position coordinates. For example, a number of rows between a target plant and an invariant anchor point may be counted, e.g., via object recognition or using a map generated at block 510, to assign the target plant a position such as “three rows south and two plants [or columns] west of cistern.”
Unlike with conventional stitching-based localization techniques, with the alignment techniques described herein, it is not necessary to use a stitched and blended together composite image for localization. Rather, because individual invariant anchor points are localized relative to each other (and with GPS coordinates in some implementations), one or more individual (e.g., unstitched) high-elevation images can be used to localize an individual plant.
In some implementations, at block 514, the system, e.g., by way of farm task module 113, may deploy one or more agricultural robots to the one or more of the localized individual plants to perform one or more agricultural tasks. For example, in some implementations, in addition to localizing individual plants, other image processing techniques (e.g., trained machine learning models such as CNNs) may be applied to classify individual plants in the high-elevation images as being in need of some agricultural task. Weeds may be classified and targeted for remediation, e.g., via mechanical destruction (e.g., pulling) or via chemical application. Crops may be classified as ready for harvest or as being infested with pests. Based on these additional classifications, and based on the localizations determined using techniques described herein, agricultural robots (or agricultural personnel) may be deployed to perform various agricultural tasks.
User interface input devices 622 may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and/or other types of input devices. In some implementations in which computing device 610 takes the form of a HMD or smart glasses, a pose of a user's eyes may be tracked for use, e.g., alone or in combination with other stimuli (e.g., blinking, pressing a button, etc.), as user input. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information into computing device 610 or onto a communication network.
User interface output devices 620 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem may include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, one or more displays forming part of a HMD, or some other mechanism for creating a visible image. The display subsystem may also provide non-visual display such as via audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computing device 610 to the user or to another machine or computing device.
Storage subsystem 624 stores programming and data constructs that provide the functionality of some or all of the modules described herein. For example, the storage subsystem 624 may include the logic to perform selected aspects of method 500 described herein, as well as to implement various components depicted in
These software modules are generally executed by processor 614 alone or in combination with other processors. Memory 625 used in the storage subsystem 624 can include a number of memories including a main random access memory (RAM) 630 for storage of instructions and data during program execution and a read only memory (ROM) 632 in which fixed instructions are stored. A file storage subsystem 626 can provide persistent storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges. The modules implementing the functionality of certain implementations may be stored by file storage subsystem 626 in the storage subsystem 624, or in other machines accessible by the processor(s) 614.
Bus subsystem 612 provides a mechanism for letting the various components and subsystems of computing device 610 communicate with each other as intended. Although bus subsystem 612 is shown schematically as a single bus, alternative implementations of the bus subsystem may use multiple busses.
Computing device 610 can be of varying types including a workstation, server, computing cluster, blade server, server farm, or any other data processing system or computing device. Due to the ever-changing nature of computers and networks, the description of computing device 610 depicted in
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
This patent arises from a continuation of U.S. patent application Ser. No. 17/344,328 (now U.S. Patent ______), which was filed on Jun. 10, 2021. U.S. patent application Ser. No. 17/344,328 is hereby incorporated herein by reference in its entirety. Priority to U.S. patent application Ser. No. 17/344,328 is hereby claimed.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17344328 | Jun 2021 | US |
| Child | 18813324 | US |
