U.S. patent application number 17/583060 was filed with the patent office on 2022-05-12 for system and method for automated grain inspection during harvest.
The applicant listed for this patent is Vibe Imaging Analytics Ltd.. Invention is credited to Ron Hadar.
Application Number | 20220142049 17/583060 |
Document ID | / |
Family ID | 1000006098290 |
Filed Date | 2022-05-12 |
United States Patent
Application |
20220142049 |
Kind Code |
A1 |
Hadar; Ron |
May 12, 2022 |
SYSTEM AND METHOD FOR AUTOMATED GRAIN INSPECTION DURING HARVEST
Abstract
A system and method for automated grain inspection and analysis
of results during harvest, using an inspection system mounted on a
combine harvester with geolocation tracking, allowing for real time
analysis during harvest and tracking of grain quality by location
of harvest.
Inventors: |
Hadar; Ron; (Capitola,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Vibe Imaging Analytics Ltd. |
Bnei-Brak |
|
IL |
|
|
Family ID: |
1000006098290 |
Appl. No.: |
17/583060 |
Filed: |
January 24, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17522161 |
Nov 9, 2021 |
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17583060 |
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16436592 |
Jun 10, 2019 |
11170496 |
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17522161 |
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16434497 |
Jun 7, 2019 |
11074682 |
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16436592 |
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16122853 |
Sep 5, 2018 |
10740893 |
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16434497 |
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62606332 |
Sep 19, 2017 |
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62605957 |
Sep 5, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A01D 41/1277 20130101;
A01B 69/001 20130101; A01D 41/127 20130101; G06T 7/0004 20130101;
A01D 45/30 20130101; G06T 7/90 20170101; G06T 2207/30128 20130101;
G06T 7/0012 20130101; G06T 2207/30242 20130101; G06Q 30/018
20130101; H04W 4/021 20130101; A01D 75/02 20130101; G06V 10/56
20220101; G06T 2207/30252 20130101; G06V 20/56 20220101; H04W 4/40
20180201; G01W 1/02 20130101; G06T 2207/30188 20130101; H04W 4/029
20180201; G06T 7/001 20130101; A01D 41/1278 20130101; G06V 20/188
20220101; G06T 2207/10024 20130101; G06T 7/62 20170101 |
International
Class: |
A01D 41/127 20060101
A01D041/127; A01B 69/00 20060101 A01B069/00; G06T 7/00 20060101
G06T007/00; G06T 7/90 20060101 G06T007/90; G06V 20/56 20060101
G06V020/56; G06V 20/10 20060101 G06V020/10; G06V 10/56 20060101
G06V010/56; H04W 4/021 20060101 H04W004/021; H04W 4/40 20060101
H04W004/40; G01W 1/02 20060101 G01W001/02 |
Claims
1. A system for automated grain inspection during harvest,
comprising: a sample inlet, mounted in or on a harvester, and
configured to divert samples of grain being harvested to an imaging
system; and the imaging system, mounted in or on the harvester, and
configured to take digital images or video of the samples of grain
diverted by the sample inlet; a humidity sensor, configured to
capture moisture data from air surrounding the samples of grain;
and a sample outlet, mounted in or on the harvester, and configured
to either return the samples of grain to the grain being harvested
after imaging and moisture data capture or discard the samples
after imaging and moisture data capture; and a computing device
comprising a memory and a processor, and configured to receive the
digital images or video from the imaging system and the moisture
data from the humidity sensor; an image processor comprising a
first plurality of programming instructions stored in the memory
of, and operating on the processor of, the computing device,
wherein the first plurality of programming instructions, when
operating on the processor, causes the computing device to: receive
the images or video of the samples of grain; identify in each image
or video a plurality of grains of the samples of grain; for each of
the plurality of grains identified, determine a color value of each
pixel representing that grain; and create a histogram of the color
values of the plurality of grains identified from the determined
color values; and a parametric evaluator comprising a second
plurality of programming instructions stored in the memory of, and
operating on the processor of, the computing device, wherein the
second plurality of programming instructions, when operating on the
processor, causes the computing device to: receive the histogram
from the image processor; determine whether the histogram falls
within an expected histogram parameter; receive the moisture data
from the humidity sensor; determine whether the moisture data falls
within an expected moisture parameter; and if the histogram falls
within the expected histogram parameter and the moisture data falls
within the expected moisture parameter, indicate acceptability of
the sample of grain; and a hierarchical histogram evaluator
comprising a third plurality of programming instructions stored in
the memory of, and operating on the processor of, the computing
device, wherein the third plurality of programming instructions,
when operating on the processor, cause the computing device to: if
the parametric evaluator has not indicated acceptability of the
samples of grain, receive the histogram from the parametric
evaluator; and compare the histogram to a hierarchy of histograms
to identify an abnormality in the samples of grain that is a cause
of unacceptability; and a geolocation device configured to track a
location of the harvester; and a wireless communication device
configured to: receive either the indication of acceptability of
the samples of grain from the parametric evaluator or the
identification of the abnormality in the samples of grain; receive
the location of the harvester from the geolocation device; and
transmit the indication of acceptability of the samples of grain
from the parametric evaluator or the identification of the
abnormality in the samples of grain, along with the location of the
harvester, wirelessly to a computer or network of computers located
remotely from the harvester.
2. The system of claim 1, further comprising the computer or
network of computers located remotely from the harvester,
configured to: receive the digital images, video, or analyses from
the wireless communication device on a plurality of harvesters;
receive location data from the wireless communication device on the
plurality of harvesters; and track variations in grain quality by
location of harvest.
3. The system of claim 1, wherein the samples of grain diverted for
inspection are held until at least one analysis is performed by the
computing device, and then either returned to the grain being
harvested or discarded, depending on a result of the at least one
analysis.
4. A method for automated grain inspection during harvest,
comprising the steps of: diverting samples of grain being harvested
to an imaging system via a sample inlet, mounted in or on a
harvester; taking digital images or video of the samples of grain
diverted by the sample inlet via the imaging system, mounted in or
on the harvester; capturing moisture data from air surrounding the
samples of grain using a humidity sensor; returning the samples of
grain to the grain being harvested after imaging and moisture data
capture or discarding the samples of grain after imaging and
moisture data capture via a sample outlet, mounted in or on the
harvester; receiving the digital images or video into an image
processor operating on a computing device from the imaging system;
identifying in each image or video a plurality of grains of the
samples of grain; for each of the plurality of grains identified,
determining a color value of each pixel representing that grain;
and creating a histogram of the color values of the plurality of
grains identified from the determined color values; receiving, into
a parametric evaluator operating on the computing device, the
histogram from the image processor; determining whether the
histogram falls within an expected histogram parameter; receiving
the moisture data from the humidity sensor; determining whether the
moisture data falls within an expected moisture parameter; if the
histogram falls within the expected histogram parameter and the
moisture data falls within the expected moisture parameter,
indicating acceptability of the samples of grain; if the parametric
evaluator has not indicated acceptability of the samples of grain,
receiving, into a hierarchical histogram evaluator operating on the
computing device, the histogram from the parametric evaluator, and
comparing the histogram to a hierarchy of histograms to identify an
abnormality in the samples of grain that is a cause of
unacceptability; tracking a location of the harvester via a
geolocation device; receiving either the indication of
acceptability of the samples of grain from the parametric evaluator
or the identification of the abnormality in the samples of grain
into a wireless communication device; receiving the location of the
harvester from the geolocation device into the wireless
communication device; and transmitting the indication of
acceptability of the samples of grain from the parametric evaluator
or the identification of the abnormality in the samples of grain,
along with the location of the harvester, wirelessly from the
wireless communication device to a computer or network of computers
located remotely from the harvester.
5. The method of claim 4, further comprising the steps of:
receiving into the computer or network of computers located
remotely from the harvester the digital images, video, or analyses
from the wireless communication device on a plurality of
harvesters; receiving into the computer or network of computers
located remotely from the harvester location data from the wireless
communication device on a plurality of harvesters; and tracking
variations in grain quality by location of harvest.
6. The method of claim 4, wherein the samples of grain diverted for
inspection are held until at least one analysis is performed by the
computing device, and then either returned to the grain being
harvested or discarded, depending on a result of the at least one
analysis.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Priority is claimed in the application data sheet to the
following patents or patent applications, the entire written
description of each of which is expressly incorporated herein by
reference in its entirety: [0002] Ser. No. 17/522,161 [0003] Ser.
No. 16/436,592 [0004] 62/605,957 [0005] 62/606,332 [0006] Ser. No.
16/122,853 [0007] Ser. No. 16/434,497
BACKGROUND
Field of the Art
[0008] The disclosure relates to the field of image analysis, and
more particularly to the field of using image analysis to
automatically inspect and analyze grains (seeds and pulses) during
harvest.
Discussion of the State of the Art
[0009] Grains inspections and related applications for quality
control, process control, food safety and grading for commercial
value are based on subjective measures, use human interpretation of
the inspected objects with pictures provided and descriptive
specifications provided by the standards bodies.
[0010] Inspection of various grains (for example, various plant
grains such as wheat or rice, mineral or metallic grains, or
granulated or powdered substances) for various purposes such as
safety or marketability is generally limited by factors such as
subjectivity and speed, due to reliance on manual inspection
methods. These methods also do not scale well and thus inspection
is restricted to a sample group that is assumed to be an accurate
representation of the entire lot, and study has shown visual
inspection to have an error rate of 20-30%. Further, the grains
must usually be sent to a laboratory for inspection, resulting in
delays in inspection, and not allowing for tracking of the location
at which the grains were harvested.
[0011] What is needed is a system and method for automated grain
inspection and analysis of results during harvest, using an
inspection system mounted on a combine harvester with geolocation
tracking, allowing for real time analysis during harvest and
tracking of grain quality by location of harvest.
SUMMARY
[0012] Accordingly, the inventor has conceived and reduced to
practice, a system and method for automated grain inspection and
analysis of results during harvest, using an inspection system
mounted on a combine harvester with geolocation tracking, allowing
for real time analysis during harvest and tracking of grain quality
by location of harvest.
[0013] According to a preferred embodiment, a system for automated
food safety analysis, quality analysis, and grading of grains,
comprising: an image processor, comprising at least a plurality of
programming instructions stored in the memory of, and operating on
at least one processor of, a computing device, wherein the
plurality of programming instructions, when operating on the
processor, cause the computing device to: receive a digital image
of grains; identify and count within the image the areas associated
with individual grains; extract dimension information for each
individual grain identified; create, for each individual grain
identified, a pixel map of the color data for each pixel within the
area of the image associated with that individual grain; and
transmit or store the data comprising individual grain count,
dimension information, and pixel map for each individual grain for
analysis; and a food safety analyzer, comprising at least a
plurality of programming instructions stored in the memory of, and
operating on at least one processor of, a computing device, wherein
the plurality of programming instructions, when operating on the
processor, cause the computing device to: receive or obtain data
from the image processor for a sufficient number of images from a
single lot of grain to constitute a statistically representative
sample for the lot of grain; compare each pixel map in the data
against pixel maps from reference images of infected grains of the
type being inspected; perform a food safety analysis, based on the
pixel map comparisons, comprising at least the type and extent of
infection for each individual grain and the percentage of infected
grains in the data for the statistically representative sample; and
compare the results of the food safety analysis against at least
one pre-defined standard for assessing food safety; and provide a
certificate of analysis for the lot of grain detailing the extent
to which the lot of grain meets the at least one pre-defined
standard for assessing food safety; and a quality analyzer,
comprising at least a plurality of programming instructions stored
in the memory of, and operating on at least one processor of, a
computing device, wherein the plurality of programming
instructions, when operating on the processor, cause the computing
device to: device to: receive or obtain data from the image
processor for a sufficient number of images from a single lot of
grain to constitute a statistically representative sample for the
lot of grain; compare each pixel map in the data against pixel maps
from reference images of damaged grains of the type being
inspected; perform a quality analysis, based on the pixel map
comparisons, comprising at least the type and extent of damage for
each individual grain and the percentage of damaged grains in the
data for the statistically representative sample; and compare the
results of the quality analysis against at least one industry
standard for assessing grain quality; and provide a certificate of
analysis for the lot of grain detailing the extent to which the lot
of grain meets the at least one industry standard for assessing
grain quality, is disclosed.
[0014] According to another preferred embodiment, a method for
automated food safety analysis, quality analysis, and grading of
grains, comprising the steps of: receiving, at an image processor,
comprising at least a plurality of programming instructions stored
in the memory of, and operating on at least one processor of, a
computing device, a digital image of grains; identifying and
counting within the image the areas associated with individual
grains; extracting dimension information for each individual grain
identified; creating, for each individual grain identified, a pixel
map of the color data for each pixel within the area of the image
associated with that individual grain; transmitting or store the
data comprising individual grain count, dimension information, and
pixel map for each individual grain for analysis; receiving or
obtaining, at a food safety analyzer, comprising at least a
plurality of programming instructions stored in the memory of, and
operating on at least one processor of, a computing device, data
from the image processor for a sufficient number of images from a
single lot of grain to constitute a statistically representative
sample for the lot of grain; comparing each pixel map in the data
against pixel maps from reference images of infected grains of the
type being inspected; performing a food safety analysis, based on
the pixel map comparisons, comprising at least the type and extent
of infection for each individual grain and the percentage of
infected grains in the data for the statistically representative
sample; comparing the results of the food safety analysis against
at least one pre-defined standard for assessing food safety;
providing a certificate of analysis for the lot of grain detailing
the extent to which the lot of grain meets the at least one
pre-defined standard for assessing food safety; receiving or
obtaining, at a quality analyzer, comprising at least a plurality
of programming instructions stored in the memory of, and operating
on at least one processor of, a computing device, data from the
image processor for a sufficient number of images from a single lot
of grain to constitute a statistically representative sample for
the lot of grain; comparing each pixel map in the data against
pixel maps from reference images of damaged grains of the type
being inspected; performing a quality analysis, based on the pixel
map comparisons, comprising at least the type and extent of damage
for each individual grain and the percentage of damaged grains in
the data for the statistically representative sample; comparing the
results of the quality analysis against at least one industry
standard for assessing grain quality; and providing a certificate
of analysis for the lot of grain detailing the extent to which the
lot of grain meets the at least one industry standard for assessing
grain quality, is disclosed.
BRIEF DESCRIPTION OF THE DRAWING FIGURES
[0015] The accompanying drawings illustrate several aspects and,
together with the description, serve to explain the principles of
the invention according to the aspects. It will be appreciated by
one skilled in the art that the particular arrangements illustrated
in the drawings are merely exemplary, and are not to be considered
as limiting of the scope of the invention or the claims herein in
any way.
[0016] FIG. 1 is a diagram illustrating an exemplary system for
automated grain inspection and analysis, according to a preferred
embodiment of the invention.
[0017] FIG. 2 is a diagram illustrating an exemplary alternative
system architecture for automated grain inspection and analysis,
according to another embodiment of the invention.
[0018] FIG. 3 is a flow diagram illustrating an exemplary logical
architecture for automated grain analysis, according to another
embodiment of the invention.
[0019] FIG. 4 is a diagram illustrating an exemplary color wheel
for use in color calibration and analysis, according to an
embodiment of the invention.
[0020] FIG. 5 is a diagram illustrating an exemplary color analysis
result, according to an embodiment of the invention.
[0021] FIG. 6 is a diagram illustrating an exemplary reporting
interface window, presenting report results of automated grain
inspection and analysis, according to an embodiment of the
invention.
[0022] FIG. 7 shows an exemplary system for grain analysis,
according to an embodiment.
[0023] FIG. 8 shows details of an exemplary operation of the system
and method disclosed herein
[0024] FIG. 9 shows the analysis of the area and color of a grain,
according to an aspect of the invention.
[0025] FIG. 10 is a block diagram illustrating an exemplary
hardware architecture of a computing device.
[0026] FIG. 11 is a block diagram illustrating an exemplary logical
architecture for a client device.
[0027] FIG. 12 is a block diagram showing an exemplary
architectural arrangement of clients, servers, and external
services.
[0028] FIG. 13 is another block diagram illustrating an exemplary
hardware architecture of a computing device.
[0029] FIG. 14 shows an exemplary representation of the bin
structures of AB chromaticity space of grain samples.
[0030] FIG. 15 shows the process by which grains are analyzed.
[0031] FIG. 16 shows a combine harvester, as currently used for
harvesting crops.
[0032] FIG. 17 shows an example of a system with camera, light and
light control, computer and cloud application.
[0033] FIG. 18 shows the workflow of the system of FIG. 17.
[0034] FIG. 19 shows a process for milling yield management.
[0035] FIG. 20 shows a software interface for milling yield
management.
[0036] FIG. 21 shows an exemplary histogram of the Lab values of
tabulated data.
[0037] FIG. 22 shows data for a grain.
[0038] FIG. 23 shows an exemplary certificate of analysis.
[0039] FIG. 24 shows the workflow of the software wizard of FIG.
18.
[0040] FIG. 25 shows the workflow of the user program of FIG.
18.
[0041] FIG. 26 shows the chalky distribution graph and values.
DETAILED DESCRIPTION
[0042] The inventor has conceived, and reduced to practice, a
system and method for automated grain inspection and analysis of
results during harvest, using an inspection system mounted on a
combine harvester with geolocation tracking, allowing for real time
analysis during harvest and tracking of grain quality by location
of harvest.
[0043] What is needed is a system and method that measures, counts,
calculates, classifies, and reports size, shape, color, color
distribution, damages, unsafe properties, quality score and other
important properties that replaces the manual and subjective
interpretation methods based on description and pictures as
reference, with accurate, measurable, repeatable, and empirical
values. The ability to quantify good grains, damages, health risks,
commercial grading and quality score using an absolute and
objective process and the results of such a process are critical to
the entire agriculture ecosystem and humanity in general. The
results include safer food, higher quality and desired taste and
texture for the consumer. In addition, accurate data measured by a
robust system can be used for further analysis, process control,
yield improvement, event prediction, alerts, and other uses thus
enabling collaboration among the industry ecosystem to help address
issues in a faster and more reliable way.
[0044] The system includes cameras to capture the image of each
grain in a sample, and illumination units to combine light sources
with visible and invisible wavelengths (for example, infrared or
ultraviolet). Both the cameras and light controls are connected to
a computer that runs operating software and application programs.
In addition, the results of an inspection may be uploaded
automatically to cloud applications that store and later use the
data and images to run additional applications where big data is
required from one or more instruments in a facility or multiple
facilities, or in different steps in the processing, or steps in
the supply chain. For example, quality trends and comparisons,
alerts on food safety events, issue of certificates of analysis,
visualization of color distribution in grains, or other uses.
[0045] One or more different aspects may be described in the
present application. Further, for one or more of the aspects
described herein, numerous alternative arrangements may be
described; it should be appreciated that these are presented for
illustrative purposes only and are not limiting of the aspects
contained herein or the claims presented herein in any way. One or
more of the arrangements may be widely applicable to numerous
aspects, as may be readily apparent from the disclosure. In
general, arrangements are described in sufficient detail to enable
those skilled in the art to practice one or more of the aspects,
and it should be appreciated that other arrangements may be
utilized and that structural, logical, software, electrical and
other changes may be made without departing from the scope of the
particular aspects. Particular features of one or more of the
aspects described herein may be described with reference to one or
more particular aspects or figures that form a part of the present
disclosure, and in which are shown, by way of illustration,
specific arrangements of one or more of the aspects. It should be
appreciated, however, that such features are not limited to usage
in the one or more particular aspects or figures with reference to
which they are described. The present disclosure is neither a
literal description of all arrangements of one or more of the
aspects nor a listing of features of one or more of the aspects
that must be present in all arrangements.
[0046] Headings of sections provided in this patent application and
the title of this patent application are for convenience only, and
are not to be taken as limiting the disclosure in any way.
[0047] Devices that are in communication with each other need not
be in continuous communication with each other, unless expressly
specified otherwise. In addition, devices that are in communication
with each other may communicate directly or indirectly through one
or more communication means or intermediaries, logical or
physical.
[0048] A description of an aspect with several components in
communication with each other does not imply that all such
components are required. To the contrary, a variety of optional
components may be described to illustrate a wide variety of
possible aspects and in order to more fully illustrate one or more
aspects. Similarly, although process steps, method steps,
algorithms or the like may be described in a sequential order, such
processes, methods and algorithms may generally be configured to
work in alternate orders, unless specifically stated to the
contrary. In other words, any sequence or order of steps that may
be described in this patent application does not, in and of itself,
indicate a requirement that the steps be performed in that order.
The steps of described processes may be performed in any order
practical. Further, some steps may be performed simultaneously
despite being described or implied as occurring non-simultaneously
(e.g., because one step is described after the other step).
Moreover, the illustration of a process by its depiction in a
drawing does not imply that the illustrated process is exclusive of
other variations and modifications thereto, does not imply that the
illustrated process or any of its steps are necessary to one or
more of the aspects, and does not imply that the illustrated
process is preferred. Also, steps are generally described once per
aspect, but this does not mean they must occur once, or that they
may only occur once each time a process, method, or algorithm is
carried out or executed. Some steps may be omitted in some aspects
or some occurrences, or some steps may be executed more than once
in a given aspect or occurrence.
[0049] When a single device or article is described herein, it will
be readily apparent that more than one device or article may be
used in place of a single device or article. Similarly, where more
than one device or article is described herein, it will be readily
apparent that a single device or article may be used in place of
the more than one device or article.
[0050] The functionality or the features of a device may be
alternatively embodied by one or more other devices that are not
explicitly described as having such functionality or features.
Thus, other aspects need not include the device itself
[0051] Techniques and mechanisms described or referenced herein
will sometimes be described in singular form for clarity. However,
it should be appreciated that particular aspects may include
multiple iterations of a technique or multiple instantiations of a
mechanism unless noted otherwise. Process descriptions or blocks in
figures should be understood as representing modules, segments, or
portions of code which include one or more executable instructions
for implementing specific logical functions or steps in the
process. Alternate implementations are included within the scope of
various aspects in which, for example, functions may be executed
out of order from that shown or discussed, including substantially
concurrently or in reverse order, depending on the functionality
involved, as would be understood by those having ordinary skill in
the art.
Definitions
[0052] The term "damage" as used herein means imperfections in
grain caused by physical factors such as heat, cold, flood,
mechanical damage from harvesting, transportation, and processing,
and insect damage from chewing, boring, or tunneling.
[0053] The term "disease" or "infection" as used herein means any
of a number of diseases and infections that affect grains, the most
common of which are various types of fungal infections.
[0054] The term "grain" or "grains" as used herein includes the
grains, seeds, and pulses of plants.
[0055] The terms "lot" or "batch" as used herein mean a quantity of
grain being transported, sold, processed, analyzed, certified, or
otherwise handled or disposed of as a single unit.
[0056] The term "soundness" as used herein means the overall visual
grain quality. The soundness of a particular grain is diminished by
damage and disease. Industry standards such as the Official United
States Standard for Grain set forth the factors for determining
soundness of grain.
Conceptual Architecture
[0057] FIG. 1 is a diagram illustrating an exemplary system 100 for
automated grain inspection and analysis, according to a preferred
embodiment of the invention. According to the embodiment, a system
100 may comprise a feeder 130 configured to direct grains onto a
receptacle 102, for example using a rigid or flexible chute 130
that is angled to control the rate of flow onto a flat or curved
receptacle 102 to maintain a desired grain density on the surface
of receptacle 102. This allows feeder 130 to feed a limited amount
of grain to be inspected onto receptacle 102 that can spread the
grain out for proper inspection, for example using mechanical means
such as a vibratory motor 101 that agitates receptacle 102 to
distribute grains on the surface of receptacle 102. Receptacle 102
may also be manipulated either manually or automatically to improve
spreading of the grains, for example the intensity of a vibrating
motor 101 may be altered automatically if the grain distribution is
not within desired parameters (for example, as may be detected
using an image sensor 110). A plurality of light emitters 120 may
be used to project visible light onto receptacle 102 and illuminate
any grains scattered thereupon, and a plurality of corresponding
image sensors 110 may be used to capture image data by scanning
receptacle 102 while illuminated. Optionally, light emitters 120
and imaging sensors 110 may be tuned to various wavelengths that
may lie within or outside the visible spectrum (for example,
infrared or ultraviolet), as is described below in FIG. 2. System
100 may be connected to a computing device or a system of computing
devices, such as a network or local arrangement of computers and
computing hardware, that may be configured to capture and analyze
grains using the components of system 100, as described below in
FIG. 3.
[0058] FIG. 2 is a diagram illustrating an exemplary alternative
system 200 for automated grain inspection and analysis, according
to another embodiment of the invention. According to the
embodiment, a system 200 may comprise a plurality of light emitters
120, 220, 221 of different wavelengths chosen from the spectrum of
light, for example ranging from infrared through ultraviolet
positioned above and/or below receptacle 102 (in this embodiment, a
concave receptacle is shown to illustrate an additional possible
arrangement), to illuminate grains on the surface of receptacle 102
with various wavelengths of light from above and below. A plurality
of corresponding images sensors may be used to scan the illuminated
grains, for example sensors tuned to capture visible light
frequencies 110 as described above in FIG. 1, as well as sensors
configured to capture infrared (IR) 210 and ultraviolet (UV) 211.
In this manner, multispectral analysis may be performed on grains
to count and analyze them, providing detailed quantitative and
qualitative results that may be presented in various visualizations
and reports, as described below in FIGS. 4-6.
[0059] In some cases, one of the color properties assessed in
particular may be a degree of chalkiness of some or all of the
grains dispersed on the surface of receptacle 102, and more in
particular afterglow effects of such chalkiness (for example,
multispectral illumination of the grains may reveal certain
spectral behaviors associated with chalkiness that may otherwise be
difficult to observe, revealing details otherwise obscured in any
one particular spectral band). Light emitters 120, 220, 221 may use
one or more, or a combination, of LEDs of different color, or by
specialized uni-or multi-spectral halide or xenon or similar
discharge lamps, or other light-emitting sources, and may be
configured as specialized uni- or multi-spectral lamps, and may
optionally be used with any or a combination of filters to further
alter the emission spectra. During sampling of grain, lights may be
sequenced as needed to achieve optimal image quality or to tune for
specific image or grain features, such as to highlight blemishes or
examine for disease (either in general, or to examine for specific
diseases or pathogens) or grain damage, or to classify grain type
or variety, or to correlate with information regarding the
location, methods, or other conditions of the grain's growth,
harvest, storage, transport, or processing.
[0060] In addition to cameras, other sensor types may include
humidity sensors, temperature sensors, light sensors, scanners,
scales, or other sensor types, and the data from the sensors and
cameras may be used to measure all the details of blemishes,
diseases or any other damage to each grain, so the system can
identify the grain type, its variety, and its diseases and damages.
For each grain, a pixel count may be calculated and then organized
in a histogram for color and size. These histograms may be
hierarchical and may be used to identify and help quickly
categorize grains, diseases, qualities, or any measurable
metric.
[0061] All this information may be sent over a network to a server
or a cloud, and compared to a reference database. Changes over time
may be tracked by region, enabling companies, governments and NGOs
to assess the safety and sufficiency of the food supply and to
recognize supply problems stemming from new diseases quickly and
early on.
[0062] System 100, 200 may be implemented in a combine harvester or
other harvesting or farming equipment, for example diverting a
sample from a harvest stream according to a configured time,
location, or other schedule or pattern. This may be used to enable
real-time (or near real-time) analysis of a harvest, for example to
produce a harvest quality map that may be used to optimize field
preparation (such as to direct the use of fertilizers or
pesticides, for example) or for storage or transmission. Grain may
also be classified and tracked based on harvest time, location,
methods, or other such metrics, which may optionally be presented
alongside analysis results in reporting.
[0063] FIG. 3 is a flow diagram illustrating an exemplary logical
architecture 300 for automated grain analysis, according to another
embodiment of the invention. According to the embodiment, an image
acquisition engine 301 may collect data from a plurality of image
sensors 120, 220, 221 in a system for automated grain examination.
Image sensor data may then be provided to an image processor 302
that may perform any or a combination of image processing tasks on
the data, such as (for example, including but not limited to)
normalization, amplification, color balancing, colorization,
desaturation, edge-finding or erosion, or any other image
processing task that may be used to improve the suitability of
image information for a particular desired analysis result or
operational configuration (for example, processing an image to
expedite analysis or to reduce system load during parallelized
operation). Processed image data may then be provided to a
parametric evaluator 303 that may compare the processed data
against configured parameters for analysis, for example to verify
that the image data is within desired parameters for optimum
results. A color analyzer 304 may then be used to analyze the color
of grains within the image data, for example according to a
configuration file as described below with reference to FIGS. 4-5.
Color analyzer 304 may be used to calculate a pixel count that may
then be organized in a histogram for color and size, and pixel
counts and histograms may be arranged hierarchically and used in
grain classification or visualization as described below. A grain
classifier 305 may be used to classify specific grains, or a sample
group as a whole, for example to apply specific labels such as size
or texture identifiers or to accept/reject grains based on analysis
results. For example, if a sufficient quantity of grains in a
sample are below a configured quality threshold (as determined
using parameter information from parametric evaluator 303 and grain
color information from color analyzer 304), then a sample may be
marked as unsatisfactory. A variety of outputs 306 may be produced
for further review, storage, or transmission, according to various
aspects and implementations. Visualizations 306a may be used to
surface analysis data for human review in a readily-understood
fashion, for example using color wheels (as shown below in FIGS.
4-5). A summary 306b may be provided for a concise statement of
analysis results, for example for publication or quick viewing when
a large number of analyses must be checked or compared quickly. CSV
reports 306c may be produced for storage and import into other
software applications, such as for further analysis or for storage
in a database with other information (such as to maintain a
repository of historical analysis results). A grain window 306d may
present a view of individual grains or groups of grains for manual
inspection, for example if an ambiguous result requires human
intervention to validate, or to enable an additional layer of
quality control by including human verification for some or all
analysis operations. A report screen 306e may be produced to
consolidate analysis results into a human-readable interface with
various analysis factors represented for viewing, such as the
exemplary interface shown below in FIG. 6. Images 306f may be
produced from image data used during analysis, for example to store
"raw" or original image data alongside the results of analysis of
the same data, or to produce images of grain that was analyzed for
publication or storage.
[0064] FIG. 7 shows an exemplary system 700 for grain analysis,
according to one aspect of the system and method disclosed herein.
Inlet funnel 701 conducts grain samples via pipes 703a-n onto
examination plate 705, which can rotate vertically in a full
circle. Motor 704 shakes plate 705 to distribute the grains so a
full sample can be captured by camera 702 for examination and
analysis, as described in greater detail below. Lights (not shown
here) may be installed in system 700 to illuminate grain on plate
705 during photography by camera 702. A software program on a
computer, (not shown in this figure) controlling all elements, such
as LEDs or other lights, motor to manipulate plate 705, etc. now
analyzes the camera images of the grains, in some cases in
different illuminations from the sources discussed throughout. In
some cases, if the grains are too bunched up, the motor 704 that
can manipulate the 705, for example by having a gear box, that when
run in reverse, vibrates (if square) or turns (if round) the table
horizontally rather than flip it for dumping the grains. Then the
grains can be re-examined, and this may be repeated several times
until a satisfactory view is achieve The grain is then dumped from
plate 705 into outlet hopper 706.
[0065] FIG. 8 shows details of an exemplary operation 800 of the
system and method disclosed herein. Grain enters the system via
pipe 801 past valve 802, which valve is calibrated to admit only a
predefined weight or volume of grain into funnel 803 and thence via
pipes 804a-n onto plate 805.
[0066] FIG. 9 shows the analysis of the area and color of a grain
900, according to an aspect of the invention. According to the
aspect, the total area of grain 900 may be considered to be a value
of 100% of the area, while any damage to grain 900 may be defined
as a percentage thereof Grain 900 may be of a normal color
according to a configured color scale, or it may be of an abnormal
color either according to the color scale or based on the analysis
of the % of the surface area that is an abnormal color (for
example, there may be a defined color threshold above which the
entire grain is deemed to be abnormal). Specific damages 901 may be
identified and considered when determining if a grain is
acceptable, for example by the nature or severity of the damage
(such as a surface imperfection) or based on the portion of total
area that is damaged (such as for color abnormalities, or
cracks).
[0067] FIG. 14 shows an exemplary representation of the bin
structures of AB chromaticity space 1400 of grain samples. For a
sample, each grain is presented in average, mode, and median. For
the grains, each grain is represented by roughly 3000-6000 pixels.
For a lab sample, the L range 1401 is typically set to be 0-100;
the AB (XY) axes 1402a and 1402b may run from -100 to +100 and may
be divided into 16 bins 1403a-n, for example. This number is
somewhat arbitrary and may be changed for various reasons. In some
cases, these numbers may be dynamically changed to maximize the
number of bins (within the quadrants) 1403a-n. To run a sample, the
system typically creates ten groups of L1-10 (L for lightness based
on captured pixels). Each L group, typically, with 16 quadrants
1403a-n in a range of +/-100 to +/-100 AB axis 1402a, 1402b of
chromaticity. For each L (Lxaby) group, a user places the pixel in
the relevant *ab bin 1403a-n. The system then summarizes the number
of pixels per bin, calculates each bin as a percent of area of the
sample grain or object, and calculates the average of L-a-b for
each bin. The system is now able to create a plot showing
LAB-bin-average values, percent of area, and actual color. For more
detail, see FIG. 15 below.
[0068] The chromaticity space is used in conjunction with color
visual references provided by various groups, industries, and
governmental agencies for identifying disease and damage in crops.
For example, the USDA visual reference library contains color
photographs of a variety of grain defects, disease, damage,
contamination, spoilage, infection, and other factors, and USDA
grading tables provide quality categories based on the color of
rice kernel, milling degree, and maximum damages allowed per grade.
Under current methodology, a human inspector needs to visually
compare the actual grain with the provided pictures or descriptions
as reference, and make qualitative judgment calls regarding
disease, damage, and soundness. When using such reference, there is
no qualitative number or specification of the color or the minimum
area of the "heat damaged" spot. USDA visual references and other
sources provide information on a variety of grain defects, disease,
damage, contamination, spoilage, infection, and other factors.
These visual references can be used to input color information into
the system to recognize quantitatively, for example, mold
contamination, which can be a health concern. These quantitative
characterizations using chromaticity space are far more accurate
than human visual inspection, and the results are more repeatable
and reliable. A wide variety of grain-related health issues and
food safety issues can be identified in this manner.
[0069] FIG. 15 shows the process 1420 by which grains are analyzed.
In step 1421 the raw data file is loaded, as shown in chart 1422.
In step 1423, the kernel number is fetched and the kernel ID is
then assigned. Data of the identified kernel is extracted from a
file 1424. Then a new file is created 1425, containing only data
for that selected kernel or object and then the data is loaded into
tables L1-L10 1426a-n. In the next step, additional tables 1427a-n
are made. These tables contain the Lxaby matrices. Finally, table
1427a-n is expanded into a full table 1428a-n with mainly two
values (Lab, Avg) for each pixel that then are output into a
regular file. This file is then processed, for example in FIG.
22.
Detailed Description of Exemplary Aspects
[0070] FIG. 4 is a diagram illustrating an exemplary color wheel
400 for use in color calibration and analysis, illustrating the use
of configured zones 410, 420, 430, according to an embodiment of
the invention. According to various aspects of the invention, color
analyzer 304 may receive or produce a calibration file comprising
configuration information that defines a plurality of color zones
410, 420, 430 on a color wheel 400, that may be used in conjunction
with slices of color wheel 400 to represent color analysis points.
A calibration file may be produce by scanning and analyzing a known
sample of grains and fitting the calibration to the results,
defining calibration values in terms of the results obtained from
the use of a controlled sample that is known to produce specific
values. Color zones are defined as an area covering the full
360.degree. of the color wheel 400 out to a specified distance from
the origin. For example, zone A 410 may be the area out to 1/3r
(where r is the radius of the circle described by color wheel 400),
zone B 420 may comprise the area from 1/3r to 2/3r, and zone C 430
may be the remaining outer area from 2/3r to r, or the outer
boundary of color wheel 400. The specific values for each zone may
be described in a calibration file for ease of storage and use. It
should be appreciated that the specific visual arrangement shown in
FIG. 4 may vary, for example orienting the color wheel with
0.degree. at the bottom, side, or at an angle (rather than at the
top as shown), or increasing the degree scale in a counterclockwise
direction, rather than the clockwise direction shown, or other
variations (which may be defined in a calibration file).
[0071] FIG. 5 is a diagram illustrating an exemplary color wheel
500 for use in color calibration and analysis, illustrating the use
of configured color slices 510, 520, 530, 540, according to an
embodiment of the invention. According to various aspects, color
slices 510, 520, 530, 540 may be defined in a configuration file
(optionally in addition to , with each slice comprising a circular
sector within color wheel 500. In this manner, color analysis
results may be represented as points within color wheel 500, with
each point being placed within the slice and/or zone corresponding
to the analysis results, producing a complete visualization for
easy interpretation and further use of analysis results. This may
be used both to visualize actual color (for example, when using
visible-light analysis of grains, as described above in FIG. 1), as
well as to visualize multidimensional data by assigning color to
other data values. For example, grain size or degree of chalkiness
may be represented as distance from the origin (and thus, placement
within color zones 410, 411, 412) and grain density (or other
metric) may be represented as a point's placement within color
zones 510, 520, 530, 540. This may be particularly suitable for
some visualization types, as a normal color wheel provides for easy
visual indication of color hue and saturation, which may be natural
analogues for certain analysis metrics such as chalkiness, size,
damage, density, purity, or any of a number of metrics that may be
represented on a bounded scale. Additionally, any particular
visualization may have selectable or dynamically-adjustable
granularity, for example to enable a zoom feature to precisely
compare multiple points that may be grouped together, providing for
a high-fidelity representation of information (for example, by
using vectors to store pixel information rather than rounding
values and thus introducing information loss).
[0072] FIG. 6 is a diagram illustrating an exemplary reporting
interface window 600, presenting report results of automated grain
inspection and analysis, according to an embodiment of the
invention. According to the embodiment, analysis results may be
collected and consolidated into a reporting interface 600 for ease
of viewing, so they may be presented for verification or review by
a human user or for use in publication (such as to publish analysis
results of a grain sample for public viewing). Any number and
combination of analysis metrics may be represented for viewing in
appropriate formats, for example including (but not limited to) a
list of grain types 610 that were analyzed, a list of metricized
analysis results 630, a graph of grain metric distribution 620 such
as color or size distribution or other metrics that may be
represented in graph form, or sortable or filterable lists of grain
attributes 640 such as physical dimensions. These interface views
may optionally be fixed, for example as part of a loaded
configuration or as part of a particular analysis operation (for
example, an analysis focused on specific metrics may restrict the
types of information presented for the sake of clarity), or they
may be user-configurable and interactive, for example enabling
drag-and-drop or other interaction so that a user may adjust the
information or the presentation thereof
[0073] FIG. 16 shows a combine harvester 1600, as currently used
for harvesting crops. In some cases, the system and method
disclosed herein may be implemented in such a combine or other
harvesting system. Implementation may comprise occasionally
diverting, for example based on schedule or location, a sample for
analysis from a harvest stream. Such a system of diversion and
analysis during harvesting operations could enable a user to create
a near real-time harvest quality map, which may then be
communicated to any desired destinations, thus permitting farm
managers to optimize field preparation for the next season. Such
preparation may for example include, but is not limited to, the use
of fertilizers or pesticides where applicable.
[0074] Kernel analyzer system 1604 is integrated into combine
harvest 1600 (typically near or on a driver cabin 1601) by having a
sample pull 1603 that allows periodic pulling of samples from a
main kernel feed 1602, a sample return 1606, and a link 1605 that
either connect to the combine's own network or connects via a
wireless uplink directly to the Internet, or both.
[0075] Additionally, kernel analyzer system 1604 analyzes and
calculates the sorter "rejected grains bin" and provides
information related to return on investment (ROI) to recover "good"
grains from the rejected bin. In addition, it provides feedback to
the sorter machine on its performance, how many good kernels are
rejected on each bad kernel, and by inspecting the grains before
the sorter station as an input to the sorter for optimization of
speed vs. performance. This method can recover good grains that can
be sold at a premium compared to damaged grains.
[0076] FIG. 17 shows an example of a system with camera 1701, light
and light control 1702, computer 1703 and cloud application 1704.
This system can measure grains of any kind from one grain to any
amount that can be placed on a working area, for example 1500-2000
grains of rice (approximately 25 grams), or several thousand grains
of Quinoa (approximately 10 grams), or other such uses.
[0077] FIG. 18 shows an exemplary workflow of the system of FIG.
17. This flow includes system operating software, graphic user
interface, inputs, and outputs of application software. Wizard 1801
is a software program that uses a template file 1802 as input for
the measurement basic setup and configuration of default
parameters. A sample of grains (not shown) may be placed on a
working area and the user progresses through all the steps of
wizard 1801 to set all the parameters of the measurement, and at
the end of the process a new calibration file is created.
[0078] The calibration file can be for rice variety (for example,
basmati or jasmine), for size (long, medium or short grain), for
type (rough or paddy, brown, milled), or for special rice such as
Wild or Arborio. In additional it may be for a specific quality
standard (for example, Japan, Korea, USA) or by customer
requirement either for tighter specifications such as rice grains
for sake preparation or loose quality requirement when grains are
to be used in food processing such as for rice crackers or pet
food, or because of specific weather conditions when further
adjustments are required. Wizard 1801 steps cover all grain
properties and definitions to classify each grain as "good", "bad"
(damaged), or "questionable", by color and dimensions (length,
width, area, and calculations such as length/width ratio). All
calibrations may be stored in a folder 1803 and used for testing
matching relevant samples.
[0079] User program 1804 performs the actual measurements,
classification and reporting. It receives as input a grain sample
placed on a working area, and a calibration file that matches the
grain type and test requirements. The outputs are summary results,
visual classification of all the grains (for example, "broken",
"chalky", "red", etc.) and provides tools to further review the
results, such as a window where a user may click on a specific
grain to view all the grain properties.
[0080] A report page 1805 provides additional information such as
quantity of grain, distribution, types of grains in the sample,
classification of all damaged grains, and statistics such as (for
example), number of grains, number of broken grains, average
length, or average width. In addition, a test results folder 1806
may be generated and saved, with the folder name including date and
time plus optional text if entered by the user as a sample name.
Files provide data on the sample level, grain level, and pixel
level for each grain. Images of the sample provide information on
the visuals, classifications, and results. Images may be used to
upload an image of the sample for re-testing in case of dispute or
investigation, and a daily results file may be updated to maintain
daily status. Web-based or local cloud page 1807 includes a set of
software applications including (for example) alerts,
visualization, reporting, trends, dashboard, and other productivity
tools provided to improve quality, yield, and food safety. Wizard
1801 enables a user to set a file name and the method to define
grain type, such as by length or length-to-width ratio, and uses
USDA or other standards as defaults with the option to modify
values. The wizard 1801 provides a method to set color value and
limits (for example, maximum area to classify "red damages" in a
grain), and a summary results screen with classification of all
detected damages. In addition to the system's capability to
measure, classify, and report grain status, it also provides a set
of applications that bring new methods to address the grain
industry resulting in better quality, safer food, higher yield, and
greater profit.
[0081] FIG. 19 shows an exemplary process for milling yield
management. Yield management is based on measurement of dimensions,
color and calculation of the milling process endpoint, which means
a desired milling level and optimized yield that eliminates over-
or under-milling. 1901 is a color index presentation of all the
samples 1910a-n. What is missing in the current art is ensuring
that the milling process reached its desired yield level, not over-
or under-milled; therefore, the system uses the combination of
color with rice grain dimensions.
[0082] FIG. 20 shows a software interface for milling yield
management. Shown is a distribution of length 2001 and width 2002,
while graph 2003 presents the combination of calculated area with
color. In addition, the graph 2003 shows that there are two groups
of milled rice 2004a-b; both groups look good visually but one
2004b is over-milled as determined by the system.
[0083] FIG. 21 shows an exemplary histogram 2100 of the Lab values
of tabulated data for several grains 2110a-n.
[0084] FIG. 22 shows exemplary color data for a single grain,
showing a pie chart 2200 indicating portions of color variance
2201a-n in the grain surface.
[0085] FIG. 23 shows an exemplary certificate of analysis 2300.
Most of the transactions in the supply chain include a quality test
before shipment and incoming inspection by the receiving party.
Currently, the reports are based on collective inputs including
manual inspection results. The system will generate a certificate
with images and digital absolute values measured by instruments.
This will ensure accurate and objective reporting and reduce
arguments, conflicts, and shipment returns based on subjective
interpretation. The application uses the cloud databased with the
test results, and uses templates to extract the desired text and
images to include in the report.
[0086] FIG. 24 shows an exemplary workflow of the software wizard
of FIG. 18. Wizard 1801 loads a template file 1802 and grain sample
2401, calibrates using the loaded data 2402, and then stores the
calibration 1804.
[0087] FIG. 25 shows an exemplary workflow of the user program of
FIG. 18. User program 1804 retrieves a calibration file 2501 from a
calibration store 1803, and uses the calibration when measuring and
analyzing 2502 a grain sample 2510 or loaded historical grain
sample imagery 2520. Calibrated measurement results 2503 may then
be stored 2504 for future use, such as for loading and using in
future operations 2520.
[0088] FIG. 26 shows an exemplary chalky distribution graph and
values. The current graph 2600 shows that the distribution is not
linear, and addresses the binary method provided by USDA and other
standards. Chalk is an opaque area in the rice grain that occurs
most commonly when grains are exposed to high temperatures during
development. Chalky rice decreases the value of rice because of its
undesirable appearance and quality Chalky rice grains are defined
by USDA (for example) if more than 50% of the area of the grain is
opaque in color. This method provides a subjective and biased
presentation of the actual grain quality. For example, a grain can
be 45% chalky and yet defined as "not chalky", or between 51-100%
and defined as "chalky". It is a binary classification with no
additional information that provides accurate status of the entire
sample The wizard defines a level of color range that is defined as
"chalky" using absolute color values and definitions of "light
chalky" and "chalky" area of the total area of the grain and later
in the user program with the measurement, classification,
visualization, and reporting of the two values, in addition to the
presentation of the entire sample distribution chart.
[0089] An alert system (not shown) will detect and notify selected
users and will perform actions when triggered. User screen sets
alert thresholds, for example if the amount of broken grains is
higher than 5% or if the level of chalkiness is higher than 20%.
Alerts may be via email, voice notification, SMS, or any other
electronic communication method with relevant people or systems. A
list of alerts is configured by the user and the cloud application
monitor it periodically and compares with the test results of the
sample. The alert system may notify in different locations of
activity, incoming inspection to alert on bad grains in a shipment,
processing for quality results, after storage, or before packing.
This tool is valuable to improving overall food safety, quality,
and operational efficiency.
Hardware Architecture
[0090] Generally, the techniques disclosed herein may be
implemented on hardware or a combination of software and hardware.
For example, they may be implemented in an operating system kernel,
in a separate user process, in a library package bound into network
applications, on a specially constructed machine, on an
application-specific integrated circuit (ASIC), or on a network
interface card.
[0091] Software/hardware hybrid implementations of at least some of
the aspects disclosed herein may be implemented on a programmable
network-resident machine (which should be understood to include
intermittently connected network-aware machines) selectively
activated or reconfigured by a computer program stored in memory.
Such network devices may have multiple network interfaces that may
be configured or designed to utilize different types of network
communication protocols. A general architecture for some of these
machines may be described herein in order to illustrate one or more
exemplary means by which a given unit of functionality may be
implemented. According to specific aspects, at least some of the
features or functionalities of the various aspects disclosed herein
may be implemented on one or more general-purpose computers
associated with one or more networks, such as for example an
end-user computer system, a client computer, a network server or
other server system, a mobile computing device (e.g., tablet
computing device, mobile phone, smartphone, laptop, or other
appropriate computing device), a consumer electronic device, a
music player, or any other suitable electronic device, router,
switch, or other suitable device, or any combination thereof In at
least some aspects, at least some of the features or
functionalities of the various aspects disclosed herein may be
implemented in one or more virtualized computing environments
(e.g., network computing clouds, virtual machines hosted on one or
more physical computing machines, or other appropriate virtual
environments).
[0092] Referring now to FIG. 10, there is shown a block diagram
depicting an exemplary computing device 10 suitable for
implementing at least a portion of the features or functionalities
disclosed herein. Computing device 10 may be, for example, any one
of the computing machines listed in the previous paragraph, or
indeed any other electronic device capable of executing software-
or hardware-based instructions according to one or more programs
stored in memory. Computing device 10 may be configured to
communicate with a plurality of other computing devices, such as
clients or servers, over communications networks such as a wide
area network a metropolitan area network, a local area network, a
wireless network, the Internet, or any other network, using known
protocols for such communication, whether wireless or wired.
[0093] In one aspect, computing device 10 includes one or more
central processing units (CPU) 12, one or more interfaces 15, and
one or more busses 14 (such as a peripheral component interconnect
(PCI) bus). When acting under the control of appropriate software
or firmware, CPU 12 may be responsible for implementing specific
functions associated with the functions of a specifically
configured computing device or machine. For example, in at least
one aspect, a computing device 10 may be configured or designed to
function as a server system utilizing CPU 12, local memory 11
and/or remote memory 16, and interface(s) 15. In at least one
aspect, CPU 12 may be caused to perform one or more of the
different types of functions and/or operations under the control of
software modules or components, which for example, may include an
operating system and any appropriate applications software,
drivers, and the like.
[0094] CPU 12 may include one or more processors 13 such as, for
example, a processor from one of the Intel, ARM, Qualcomm, and AMD
families of microprocessors. In some aspects, processors 13 may
include specially designed hardware such as application-specific
integrated circuits (ASICs), electrically erasable programmable
read-only memories (EEPROMs), field-programmable gate arrays
(FPGAs), and so forth, for controlling operations of computing
device 10. In a particular aspect, a local memory 11 (such as
non-volatile random access memory (RAM) and/or read-only memory
(ROM), including for example one or more levels of cached memory)
may also form part of CPU 12. However, there are many different
ways in which memory may be coupled to system 10. Memory 11 may be
used for a variety of purposes such as, for example, caching and/or
storing data, programming instructions, and the like. It should be
further appreciated that CPU 12 may be one of a variety of
system-on-a-chip (SOC) type hardware that may include additional
hardware such as memory or graphics processing chips, such as a
QUALCOMM SNAPDRAGON.TM. or SAMSUNG EXYNOS.TM. CPU as are becoming
increasingly common in the art, such as for use in mobile devices
or integrated devices.
[0095] As used herein, the term "processor" is not limited merely
to those integrated circuits referred to in the art as a processor,
a mobile processor, or a microprocessor, but broadly refers to a
microcontroller, a microcomputer, a programmable logic controller,
an application-specific integrated circuit, and any other
programmable circuit.
[0096] In one aspect, interfaces 15 are provided as network
interface cards (NICs). Generally, NICs control the sending and
receiving of data packets over a computer network; other types of
interfaces 15 may for example support other peripherals used with
computing device 10. Among the interfaces that may be provided are
Ethernet interfaces, frame relay interfaces, cable interfaces, DSL
interfaces, token ring interfaces, graphics interfaces, and the
like. In addition, various types of interfaces may be provided such
as, for example, universal serial bus (USB), Serial, Ethernet,
FIREWIRE.TM., THUNDERBOLT.TM., PCI, parallel, radio frequency (RF),
BLUETOOTH.TM., near-field communications (e.g., using near-field
magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet
interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or
external SATA (ESATA) interfaces, high-definition multimedia
interface (HDMI), digital visual interface (DVI), analog or digital
audio interfaces, asynchronous transfer mode (ATM) interfaces,
high-speed serial interface (HSSI) interfaces, Point of Sale (POS)
interfaces, fiber data distributed interfaces (FDDIs), and the
like. Generally, such interfaces 15 may include physical ports
appropriate for communication with appropriate media. In some
cases, they may also include an independent processor (such as a
dedicated audio or video processor, as is common in the art for
high-fidelity A/V hardware interfaces) and, in some instances,
volatile and/or non-volatile memory (e.g., RAM).
[0097] Although the system shown in FIG. 10 illustrates one
specific architecture for a computing device 10 for implementing
one or more of the aspects described herein, it is by no means the
only device architecture on which at least a portion of the
features and techniques described herein may be implemented. For
example, architectures having one or any number of processors 13
may be used, and such processors 13 may be present in a single
device or distributed among any number of devices. In one aspect, a
single processor 13 handles communications as well as routing
computations, while in other aspects a separate dedicated
communications processor may be provided. In various aspects,
different types of features or functionalities may be implemented
in a system according to the aspect that includes a client device
(such as a tablet device or smartphone running client software) and
server systems (such as a server system described in more detail
below).
[0098] Regardless of network device configuration, the system of an
aspect may employ one or more memories or memory modules (such as,
for example, remote memory block 16 and local memory 11) configured
to store data, program instructions for the general-purpose network
operations, or other information relating to the functionality of
the aspects described herein (or any combinations of the above).
Program instructions may control execution of or comprise an
operating system and/or one or more applications, for example.
Memory 16 or memories 11, 16 may also be configured to store data
structures, configuration data, encryption data, historical system
operations information, or any other specific or generic
non-program information described herein.
[0099] Because such information and program instructions may be
employed to implement one or more systems or methods described
herein, at least some network device aspects may include
nontransitory machine-readable storage media, which, for example,
may be configured or designed to store program instructions, state
information, and the like for performing various operations
described herein. Examples of such nontransitory machine- readable
storage media include, but are not limited to, magnetic media such
as hard disks, floppy disks, and magnetic tape; optical media such
as CD-ROM disks; magneto-optical media such as optical disks, and
hardware devices that are specially configured to store and perform
program instructions, such as read-only memory devices (ROM), flash
memory (as is common in mobile devices and integrated systems),
solid state drives (SSD) and "hybrid SSD" storage drives that may
combine physical components of solid state and hard disk drives in
a single hardware device (as are becoming increasingly common in
the art with regard to personal computers), memristor memory,
random access memory (RAM), and the like. It should be appreciated
that such storage means may be integral and non-removable (such as
RAM hardware modules that may be soldered onto a motherboard or
otherwise integrated into an electronic device), or they may be
removable such as swappable flash memory modules (such as "thumb
drives" or other removable media designed for rapidly exchanging
physical storage devices), "hot-swappable" hard disk drives or
solid state drives, removable optical storage discs, or other such
removable media, and that such integral and removable storage media
may be utilized interchangeably. Examples of program instructions
include both object code, such as may be produced by a compiler,
machine code, such as may be produced by an assembler or a linker,
byte code, such as may be generated by for example a JAVA.TM.
compiler and may be executed using a Java virtual machine or
equivalent, or files containing higher level code that may be
executed by the computer using an interpreter (for example, scripts
written in Python, Perl, Ruby, Groovy, or any other scripting
language).
[0100] In some aspects, systems may be implemented on a standalone
computing system. Referring now to FIG. 11, there is shown a block
diagram depicting a typical exemplary architecture of one or more
aspects or components thereof on a standalone computing system.
Computing device 20 includes processors 21 that may run software
that carry out one or more functions or applications of aspects,
such as for example a client application 24. Processors 21 may
carry out computing instructions under control of an operating
system 22 such as, for example, a version of MICROSOFT WINDOWS.TM.
operating system, APPLE macOS.TM. or iOS.TM. operating systems,
some variety of the Linux operating system, ANDROID.TM. operating
system, or the like. In many cases, one or more shared services 23
may be operable in system 20, and may be useful for providing
common services to client applications 24. Services 23 may for
example be WINDOWS.TM. services, user-space common services in a
Linux environment, or any other type of common service architecture
used with operating system 21. Input devices 28 may be of any type
suitable for receiving user input, including for example a
keyboard, touchscreen, microphone (for example, for voice input),
mouse, touchpad, trackball, or any combination thereof Output
devices 27 may be of any type suitable for providing output to one
or more users, whether remote or local to system 20, and may
include for example one or more screens for visual output,
speakers, printers, or any combination thereof Memory 25 may be
random-access memory having any structure and architecture known in
the art, for use by processors 21, for example to run software.
Storage devices 26 may be any magnetic, optical, mechanical,
memristor, or electrical storage device for storage of data in
digital form (such as those described above, referring to FIG. 10).
Examples of storage devices 26 include flash memory, magnetic hard
drive, CD-ROM, and/or the like.
[0101] In some aspects, systems may be implemented on a distributed
computing network, such as one having any number of clients and/or
servers. Referring now to FIG. 12, there is shown a block diagram
depicting an exemplary architecture 30 for implementing at least a
portion of a system according to one aspect on a distributed
computing network. According to the aspect, any number of clients
33 may be provided. Each client 33 may run software for
implementing client-side portions of a system; clients may comprise
a system 20 such as that illustrated in FIG. 11. In addition, any
number of servers 32 may be provided for handling requests received
from one or more clients 33. Clients 33 and servers 32 may
communicate with one another via one or more electronic networks
31, which may be in various aspects any of the Internet, a wide
area network, a mobile telephony network (such as CDMA or GSM
cellular networks), a wireless network (such as WiFi, WiMAX, LTE,
and so forth), or a local area network (or indeed any network
topology known in the art; the aspect does not prefer any one
network topology over any other). Networks 31 may be implemented
using any known network protocols, including for example wired
and/or wireless protocols.
[0102] In addition, in some aspects, servers 32 may call external
services 37 when needed to obtain additional information, or to
refer to additional data concerning a particular call.
Communications with external services 37 may take place, for
example, via one or more networks 31. In various aspects, external
services 37 may comprise web-enabled services or functionality
related to or installed on the hardware device itself. For example,
in one aspect where client applications 24 are implemented on a
smartphone or other electronic device, client applications 24 may
obtain information stored in a server system 32 in the cloud or on
an external service 37 deployed on one or more of a particular
enterprise's or user's premises.
[0103] In some aspects, clients 33 or servers 32 (or both) may make
use of one or more specialized services or appliances that may be
deployed locally or remotely across one or more networks 31. For
example, one or more databases 34 may be used or referred to by one
or more aspects. It should be understood by one having ordinary
skill in the art that databases 34 may be arranged in a wide
variety of architectures and using a wide variety of data access
and manipulation means. For example, in various aspects one or more
databases 34 may comprise a relational database system using a
structured query language (SQL), while others may comprise an
alternative data storage technology such as those referred to in
the art as "NoSQL" (for example, HADOOP CASSANDRA.TM., GOOGLE
BIGTABLE.TM., and so forth). In some aspects, variant database
architectures such as column-oriented databases, in-memory
databases, clustered databases, distributed databases, or even flat
file data repositories may be used according to the aspect. It will
be appreciated by one having ordinary skill in the art that any
combination of known or future database technologies may be used as
appropriate, unless a specific database technology or a specific
arrangement of components is specified for a particular aspect
described herein. Moreover, it should be appreciated that the term
"database" as used herein may refer to a physical database machine,
a cluster of machines acting as a single database system, or a
logical database within an overall database management system.
Unless a specific meaning is specified for a given use of the term
"database", it should be construed to mean any of these senses of
the word, all of which are understood as a plain meaning of the
term "database" by those having ordinary skill in the art.
[0104] Similarly, some aspects may make use of one or more security
systems 36 and configuration systems 35. Security and configuration
management are common information technology (IT) and web
functions, and some amount of each are generally associated with
any IT or web systems. It should be understood by one having
ordinary skill in the art that any configuration or security
subsystems known in the art now or in the future may be used in
conjunction with aspects without limitation, unless a specific
security 36 or configuration system 35 or approach is specifically
required by the description of any specific aspect.
[0105] FIG. 13 shows an exemplary overview of a computer system 40
as may be used in any of the various locations throughout the
system. It is exemplary of any computer that may execute code to
process data. Various modifications and changes may be made to
computer system 40 without departing from the broader scope of the
system and method disclosed herein. Central processor unit (CPU) 41
is connected to bus 42, to which bus is also connected memory 43,
nonvolatile memory 44, display 47, input/output (I/O) unit 48, and
network interface card (NIC) 53. I/O unit 48 may, typically, be
connected to keyboard 49, pointing device 50, hard disk 52, and
real-time clock 51. NIC 53 connects to network 54, which may be the
Internet or a local network, which local network may or may not
have connections to the Internet. Also shown as part of system 40
is power supply unit 45 connected, in this example, to a main
alternating current (AC) supply 46. Not shown are batteries that
could be present, and many other devices and modifications that are
well known but are not applicable to the specific novel functions
of the current system and method disclosed herein. It should be
appreciated that some or all components illustrated may be
combined, such as in various integrated applications, for example
Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it
may be appropriate to combine multiple capabilities or functions
into a single hardware device (for instance, in mobile devices such
as smartphones, video game consoles, in-vehicle computer systems
such as navigation or multimedia systems in automobiles, or other
integrated hardware devices).
[0106] In various aspects, functionality for implementing systems
or methods of various aspects may be distributed among any number
of client and/or server components. For example, various software
modules may be implemented for performing various functions in
connection with the system of any particular aspect, and such
modules may be variously implemented to run on server and/or client
components.
[0107] The skilled person will be aware of a range of possible
modifications of the various aspects described above. Accordingly,
the present invention is defined by the claims and their
equivalents.
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