Note: Descriptions are shown in the official language in which they were submitted.
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1
Description
DEVICES, SYSTEMS AND METHODS FOR AUTOMATICALLY
PREDICTING MAINTENANCE EVENTS
Technical Field
5 The
present disclosure relates generally to devices, systems, and
methods for automatically predicting maintenance events.
Background
Machines often include components with parts that can gradually
fail over time. Generally, machine maintenance should be performed before a
part
10 or
component of the machine fails, so as to avoid increased maintenance costs
and/or machine downtime. However, a lack of access to data regarding a
machine's
repair history can limit the ability to accurately predict what maintenance a
machine should undergo and when that maintenance should be performed.
Brief Description Of The Drawings
15 Figure 1A
is a schematic illustration of a machine in accordance
with embodiments of the present technology.
Figure 1B is a perspective view of one example of the machine of
Figure 1A, in accordance with embodiments of the present technology.
Figure 2 is a flow diagram illustrating a method for predicting
20
maintenance events for machines in accordance with embodiments of the present
technology.
Figure 3 is a flow diagram illustrating a method for predicting
maintenance events for machines in accordance with embodiments of the present
technology.
25 Figure 4
is a block diagram illustrating an overview of devices on
which some implementations can operate, in accordance with embodiments of the
present technology.
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Figure 5 is a block diagram illustrating an overview of an
environment in which some implementations can operate, in accordance with
embodiments of the present technology.
Figure 6 is a block diagram illustrating elements which, in some
5 implementations, can be used in a system employing the disclosed
technology, in
accordance with embodiments of the present technology.
Summary
In some embodiments the present technology includes a method for
providing future maintenance event predictions for an industrial machine based
on
10 partial maintenance history data for the industrial machine can include
receiving
information associated with a past maintenance event of the industrial
machine;
identifying maintenance information associated with the past maintenance event
based at least partially on the received information, and predicting a time
until a
future maintenance event for the industrial machine. The identified
maintenance
15 information can include at least one of (i) a type of the industrial
machine, (ii) a
component of the industrial machine that was serviced during the past
maintenance
event, and/or (iii) a category of the past maintenance event. The predicted
time
until the future maintenance event can be based at least partially on the
identified
maintenance information, such as at least one of the type of the industrial
machine,
20 the component that was serviced during the past maintenance event,
and/or the
category of the past maintenance event.
Additionally, or alternatively, the present technology includes a
maintenance event prediction system for predicting future maintenance events
for
industrial machines based on partial maintenance history data can include one
or
25 more processors, and a non-transitory computer readable memory having
instructions that, when executed by the one or more processors, cause the
maintenance event prediction system to collect information associated with a
past
maintenance event of an industrial machine; identify maintenance information
associated with the past maintenance event; and/or predict a time until a
future
30 maintenance event for the industrial machine. The identified maintenance
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information can be based at least partially on the collected receive
information, and
can include one or more of (i) a type of the industrial machine, (ii) a
component of
the industrial machine that was serviced during the past maintenance event,
and/or
(iii) a category of the past maintenance event. The predicted time until the
future
5 maintenance event can be based at least partially on the identified
maintenance
information, such as at least one of the type of the industrial machine, the
component that was serviced during the past maintenance event, and/or the
category of the past maintenance event.
In these and other embodiments, the present technology includes a
10 non-transitory computer-readable media storing computer-executable
instructions
for predicting future maintenance events for industrial machines based on
partial
maintenance history data that, when executed by one or more processors of a
maintenance event prediction system, cause the maintenance event prediction
system to performing one or more operations. The operations can include
receiving
15 information associated with a past maintenance event of an industrial
machine,
identifying maintenance information associated with the past maintenance
event;
and predicting a time until a future maintenance event for the industrial
machine.
The identified maintenance information can be based at least partially on the
received information, and can include at least one of (i) a type of the
industrial
20 machine, (ii) a component of the industrial machine that was serviced
during the
past maintenance event, and (iii) a category of the past maintenance event.
The
predicted time until the future maintenance event be based at least partially
on the
identified maintenance information, such as at least one of the type of the
industrial
machine, the component that was serviced during the past maintenance event,
25 and/or the category of the past maintenance event.
Detailed Description
The present technology is generally directed to maintenance event
prediction systems, and associated devices and methods. In some embodiments, a
maintenance event prediction system configured in accordance with embodiments
30 of the present technology can (i) collect information associated with
one or more
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past maintenance events for a machine, (ii) use the collected information to
identify
additional information about the machine, and/or (iii) predict a future
maintenance
event and/or a time until the future maintenance event based at least
partially on
the received information and/or the additional information. For example, the
5 maintenance event prediction system can receive invoices, work orders,
parts
orders, and the like, and analyze these received documents to identify the
parts of
a machine being replaced during a past maintenance event. Generally, machine
parts are not purchased randomly and are instead purchased in response to a
repair
or maintenance need. Accordingly, in some aspects of the present technology,
10 analyzing invoices and other parts purchase data is expected to provide
an accurate,
reliable source of data for identifying information related to a machine's
maintenance history, predicting future maintenance events, and/or predicting
times
until the future maintenance events.
In at least some embodiments, the maintenance event prediction
15 system can identify one or more indicator parts from the received
information (e.g.,
parts purchase data). The indicator parts can be associated with specific
components and/or specific maintenance events. As such, the indicator parts
can
be used to identify the components being serviced and/or the category of the
past
maintenance event. The maintenance event prediction system can also use the
20 received information to identify (i) a type or class of the machine
being serviced
during the past maintenance event, (ii) one or more components of the machine
that the identified part(s) were used to service, and/or (iii) a type or
category of the
past maintenance event In some aspects, this can allow the maintenance event
prediction system to predict, supplement, or "gap fill" a given machine's
25 maintenance history when such data is not readily available.
Accordingly, the maintenance event prediction system can identify
information associated with the machine, predict future maintenance events,
and/or
predict a time until a future maintenance event based at least partially on
the parts
purchase data and other received information. In some aspects of the present
30 technology, the parts purchase data can be used to identify information
about a
given machine's maintenance history (e.g., the component that was serviced,
the
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type or category of the past maintenance event, and the like) based on
incomplete
or partial information about the machine's maintenance history, such as when
the
machine's maintenance history information is not recorded, not reported, or
otherwise inaccessible; this is expected to increase the availability of
machine
5
maintenance history information for further processing and/or analysis, for
example, to predict future maintenance events and/or times until the future
maintenance events.
A person skilled in the art will understand (i) that the technology
may have additional embodiments than those illustrated and described herein
with
10 reference
to Figures 1-6, and (ii) that the technology may be practiced without
several of the details of the embodiments described herein with reference to
Figures
1-6.
A
Select Embodiments of Devices, Systems and Methods for Automatically
Predicting Maintenance Events
15 Figure IA
is a schematic diagram illustrating a machine 100 in
accordance with embodiments of the present technology. In some embodiments,
the machine 100 can be an industrial machine, a vehicle, a truck, a tracked-
type
tractor, an excavator, a wheel loader, a front-end loader, and/or a motor
grader. In
other embodiments, the machine 100 can be any other suitable machine.
20 The
machine 100 can include one or more components 102. In the
illustrated embodiment, for example, the machine 100 includes a first
component
102a, a second component 102b, and a third component 102c. In other
embodiments, the machine 100 can include more or fewer components, such as
less than three, more than three, or any other suitable number of components.
Each
25 of the
machine's components 102 can be associated with the operation (e.g.,
structure, function, and the like) of the machine 100. For example, at least
one of
the components 102 can function independently or in combination with one or
more of the other components to perform activities associated with the
machine.
The components 102 of the machine can include one or more engines,
30 transmissions, torque converters, final drives, differentials, axles,
brakes,
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cylinders, pump and motors, undercarriages, hydraulic systems, work tools,
traction motors, injectors, and/or any other suitable component.
Each of the machine's components 102 can include one or more
parts 104. In the illustrated embodiment, for example, the first component
102a
5 includes a first part 104al, a second part 104a2, and a third part 104a3;
the second
component 102b includes a first part 104bi and a second part 104b2; and the
third
component 102c includes a first part 104ci, a second part 104c2, and a third
part
104c3. In other embodiments, each of the machine's components can include more
or fewer parts, such as at least one, four, five, six, seven, or any other
suitable
10 number of parts. Each of the machine's parts 104 can be associated with
the
operation (e.g., structure, function, and the like) of one or more of the
machine' s
components 104. For example, the parts 104 of the machine 100 can include one
or more gears, pistons, liners, rods, cylinders, valves, valve caps,
actuators,
solenoids, pins, bolts, fasteners, ring and pinions, arms, brackets, plates,
wires,
15 processors, microcomputers, controllers, sensors, springs, and/or any
other suitable
parts.
Over time, the machine 100, at least one of the components 102,
and/or at least one of the parts 104 may undergo maintenance or repair events.
For
example, the machine 100 may be subject to regularly scheduled maintenance
20 based on a selected number of hours of operation or units of distance
driven. The
machine 100 may also be subject to unscheduled maintenance events, such as
repair or replacement of one or more of the components 102 and/or one or more
of
the parts 104 thereof that wear or break at variable rates due to, for
example,
environmental conditions, usage patterns, maintenance history, and/or other
25 variable factors. The maintenance events can be specific to individual
components
104. For example, an engine of the machine can undergo top-end maintenance,
bottom-end maintenance, in-frame repair, and/or a full rebuild; during each of
these engine maintenance events.
In some embodiments, at least one of the parts 104 can be an
30 indicator part (which can also be referred to as a predictive part, an
identifying part,
a key part, a marker part, and the like). The indicator part can be used to
identify
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one or more of the components 102 that was subject to a past maintenance
event,
such that the indicator part can be used to identify information about a past
maintenance event for the machine. For example, an invoice including the
indicator
part can be used to identify at least one of (i) a type or classification of
the machine
5 100, (ii)
a component 104 of the machine 100 that was serviced during the past
maintenance event, and (iii) a type or category of the past maintenance event;
this
is described in detail below regarding Figures 2-6.
The machine 100 can be associated with an operator (e.g., an owner,
a driver, a purchaser, and the like). Although a single machine is shown in
Figure
10 1A, the
operator may be associated with multiple machines (e.g., a fleet of
machines), and each of the machine can include at least some aspects that are
generally similar or identical in structure and/or function to the machine
100.
Figure 1B is a perspective view of one example of the machine 100,
in accordance with embodiments of the present technology. In the illustrated
15
embodiment, the machine 100 includes a first component or work tool 102a. The
work tool 102a can include a first part or bucket 104at, a second part or
actuator
104a2, and a third part or arm 104a3. In the illustrated embodiment, the
bucket 104at is an indicator part for the work tool 102a. Accordingly, a
document
(e.g., an invoice) indicating the purchase of a replacement bucket can be used
to
20 identify
that (i) a given operator owns a machine including the bucket 104at (e.g.,
a front-end loader), (ii) the bucket was serviced during a past maintenance
event,
and (iii) that servicing the front-end loader included replacing the bucket
104at.
Although not labeled in Figure 1B, it will be appreciated that the machine 100
can
include additional components, such as an engine, a transmission, and the
like.
25 Figure 2
is a flow diagram illustrating a method 200 for predicting
maintenance events for machines in accordance with embodiments of the present
technology. The method 200 is illustrated as a set of blocks, steps,
operations, or
processes 201-204. Several of the blocks 201-204 are described with reference
to
Figure 1. All or a subset of the blocks 201-204 can be executed at least in
part by
30 various
components of a device and/or a system, such as the device 400 of Figure
4, and/or any other suitable device and/or system. Additionally, or
alternatively, all
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or a subset of the blocks 201-204 can be executed at least in part by an
operator
(e.g., a remote operator, a human operator, an institutional operator, a
corporate
operator, and the like) of the device and/or system. Furthermore, any one or
more
of the blocks 201-204 can be executed in accordance with the discussion
herein.
5 The
method 200 can begin at block 201 by receiving information
associated with a past repair event for a machine. Aspects of the machine can
be
generally similar or identical in structure and/or function to aspects of the
machine
100 of Figure 1. In at least some embodiments, for example, receiving
information
includes receiving the information for an industrial machine, and/or one or
more
10 components and/or parts thereof.
The received information can be any information related to the
machine. In at least some embodiments, for example, receiving the information
can
include receiving one or more parts identifiers. Each of the parts identifiers
can be
associated with an identity of a part of the machine and can include a name, a
15 number, a
serial number, a code, a price, a manufacturing cost, a quantity
purchased, and/or any other suitable parts identifier. The received
information can
be in any suitable form. In at least some embodiments, for example, receiving
the
information can include receiving the information from one or more invoices,
component order forms, work orders, transactions, ecommerce transaction, price
20 quotes,
records of incomplete transaction (e.g., items left in or removed from an
online shopping cart and not purchased), aftermarket transaction records,
manual
data entries, and/or any other suitable information sources. In these and
other
embodiments, receiving the information can include receiving information for
all
or a subset of past repair events for the machine.
25 At block
202, the method 200 continues by identifying, based at
least partially on the received information (block 201), information
associated with
the past maintenance event, including at least one of (i) a type or class of
the
machine, (ii) a component of the machine that was serviced during the past
maintenance event, and/or (iii) a type or category of the past maintenance
event.
30
Identifying the type or class of the machine can include identifying a machine
identifier. The machine identifier can be associated with the type or class of
the
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machine, and can include a name, a number, a serial number, a code, a price, a
manufacturing cost, a quantity purchased, and/or any other suitable machine
identifier. Identifying the component that was serviced can include
identifying a
component identifier. The component identifier can be associated with an
identity
5 of the
component that was, and can include a name, a number, a serial number, a
code, a price, a manufacturing cost, a quantity purchased, and/or any other
suitable
component identifier. Identifying the type or category of the past maintenance
event can include identifying a maintenance event identifier. The maintenance
event identifier can be associated with a type or category of the past
maintenance
10 event,
and can include a name, a number, a serial number, a code, a price, a cost to
perform, a time to complete, a quantity performed, and/or any other suitable
maintenance event identifier. In at least some embodiments, the maintenance
event
can be associated with and/or specific to one or more of the machine's
components,
as described
15 In some
embodiments, block 202 can further include identifying at
least one indicator part and using the at least one indicator part to identify
at least
one of (i) the type or class of the machine when combined with the customer
fleet
and engineering data, (ii) the component that was serviced, (iii) the type or
category
of the past maintenance event, and/or (iv) a date and/or time associated with
the
20
maintenance event (i.e., a time and/or a time range in which the past
maintenance
event was performed). As described in detail above, the machine can include
one
or more indicator parts. At least some of the indicator parts can be
associated with
(e g , specific to) one or more of the machine's components and/or one or more
maintenance events associated with the machine's components. As a specific
25 example,
a piston, a cylinder head, and a crankshaft (and/or one or more of their
serviceable and/or constituent parts) can each be an indicator part specific
to the
machine's engine. These indicator parts can be identified in the received
information (block 201) and used to identify information associated with the
past
maintenance event. For example, if the received information includes an
invoice
30 including
one or more parts numbers associated with a cylinder head assembly,
block 202 can include identifying that the maintenance event includes an
engine
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top-end overhaul maintenance event. Continuing with this example, if the
received
information includes an invoice for the crankshaft, block 202 can include
identifying that the past maintenance event includes a bottom-end engine
maintenance event. Continuing still with this example, if the received
information
5 includes
an invoice for the cylinder head and the piston, block 202 can include
identifying that the past maintenance event includes an in-frame engine
maintenance event. Continuing further with the example, if the received
information includes an invoice for the cylinder head, the piston, and the
crankshaft, block 202 can include identifying that the past maintenance event
10 includes
a full engine rebuild and/or another full engine maintenance event. It will
be appreciated that the piston, the cylinder head, and the crankshaft are
examples
of indicator parts and that, in practice, various machines may include these
and/or
other indicator parts. In some embodiments, identifying the indicator parts
can
include automatically identifying one or more of the indicator parts, for
example,
15 using a
machine learning algorithm, a rules-based algorithm, one or more computer
models, and/or a graphical database that identifies relationships between
parts and
maintenance events. In these and other embodiments, identifying the indicator
parts can include any other suitable process or technique for identifying
and/or
defining indicator parts.
20 In some
aspects of the present technology, the indicator parts can
be used to identify/determine information about a given machine's maintenance
history based on incomplete or partial information about the machine's
maintenance history, such as when the machine maintenance history information
is not recorded, not reported, or otherwise inaccessible; this is expected to
increase
25 the
availability of machine maintenance history information for further processing
and/or analysis. For example, and as described in detail below, the machine
maintenance history information identified in block 202 can be used to make
one
or more predictions and/or recommendations related to the machine.
Accordingly,
increasing the availability of machine maintenance history information can
30
correspondingly increase the number of machines for which predictions and/or
recommendations can be made.
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In some embodiments, at least one of the (i) the type or class of the
machine, (ii) the component that was serviced, and/or (iii) the type or
category of
the past maintenance event can be identifying based at least partially on a
quantity
of parts purchased. For example, the received data (block 201) can include a
5 quantity of parts purchased, and block 202 can include
sorting/classifying parts
based at least partially on the purchased quantity to differentiate between
various
maintenance events, for example, the extensiveness or severity of the
maintenance
event. In some aspects, this can include establishing (e.g., manually,
automatically,
etc.) one or more thresholds to differentiate between various maintenance
events.
10 The thresholds can be established based at least in part on the total
quantity of a
given part that is purchased/ordered for the associated equipment and/or
component. For example, if less than 50% of the parts orders associated with a
given machine include the piston, the cylinder head, and/or the crankshaft,
block
202 can include identifying that the associated engine maintenance event was
15 minor or included parts refresh. Continuing with this example, if
between 50% and
75% of the parts orders associated with a given machine include the piston,
the
cylinder head, and/or the crankshaft, block 202 can include identifying that
the
associated engine maintenance event was moderate or included a parts reset.
Continuing still with the example, if more than 75% of the parts orders
associated
20 with a given machine include the piston, the cylinder head, and/or the
crankshaft,
block 202 can include identifying that the associated engine maintenance event
was
substantial or included a rebuild.
At block 203, the method 200 continues by determining (e g.,
predicting, calculating, modeling, and the like) a time until a future
maintenance
25 event. The prediction in block 203 can be based at least partially on
the information
identified in block 202. For example, if in block 202 it was identified that
an engine
of the machine was serviced, then block 203 can include predicting the time
until
a future maintenance event for the engine. The time can include a time between
the
past maintenance event and the future maintenance event, an amount of time
from
30 the date at which block 203 is performed, and/or any other suitable
time. In some
embodiments, the future maintenance event can be generally similar to or the
same
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as the past maintenance event (block 201), for example, the future maintenance
event can include servicing a same machine, component and/or one or more of
the
same parts. In other embodiments, the future maintenance event can be
different
than the past maintenance event, for example, a different maintenance event
for the
5 same machines, components and/or parts, a same maintenance event for one
or
more different machines, components and/or parts, and/or a different
maintenance
event for one or more different machines, components and/or parts. In at least
some
embodiments, for example, the past maintenance event can be for a first
machine
in an operator's fleet, and the future maintenance event can be for a second
machine
10 in the operator's fleet and of a based at least partially on information
associated
with the first machine. The second machine can be generally similar or
identical in
usage, type, configuration, and/or function to the first machine, and/or the
second
machine may include one or more components and/or parts that are generally
similar or identical to the components/parts of the first machine, such that
the
15 second machine can be expected to undergo generally similar or identical
maintenance events as the first machine, and/or maintenance events at
generally
similar or identical times and/or time intervals. In these and other
embodiments,
the future maintenance event can be associated with one or more parts of
another
component related structurally and/or functionally to the component serviced
20 during the past maintenance event (block 202). For example, if the past
maintenance event included servicing a machine's engine, the future
maintenance
event can include servicing the machine's transmission.
In at least some embodiments, block 203 can further include
determining/predicting a price or cost associated with the future maintenance
25 event. The price associated with the future maintenance can include the
price/cost
of one or more of the components and/or parts associated with the future
maintenance event. Additionally, or alternatively, the price can include one
or more
service fees and/or labor costs associated with the future maintenance event.
In
these and other embodiments, the price can include an estimated increase in
the
30 price or cost associated with the future maintenance event if the future
maintenance
event is not performed by/before the predicted time.
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In some embodiments, block 203 can include determining a time
until individual ones of a plurality of future maintenance events. In some
embodiments, one or more of the plurality of future maintenance events can be
generally similar to or the same as the past maintenance event (block 201),
for
5 example, servicing a same machine, component, and/or one or more of the
same
parts. In other embodiments, one or more of the future maintenances event can
be
different than the past maintenance event, for example, a different
maintenance
event for the same machine, components and/or parts; a same maintenance event
for one or more different machines, components and/or parts, and/or a
different
10 maintenance event for one or more different machines, components and/or
parts.
In these and other embodiments, the time can include a time between individual
ones of the maintenance events, a frequency or probability over time of the
maintenance events, and/or any other suitable time.
At block 204, the method 200 continues by sending the time until
15 the future maintenance event (block 203) to an operator of the machine
(block 201).
In some embodiments, sending the time until the future maintenance event to an
operator can include sending the time until the future maintenance event to
the
operator of the machine 100 described in detail with reference to Figures 1A-
1B, a
product support representative associated with the machine's operator, an
owner
20 of the machine, repair personal associated with performing maintenance
events on
the machine, and/or any other suitable operator or other individual associated
with
the operation/maintenance of the machine. Sending the time until the future
maintenance event can include sending the time until the future maintenance
event
via wired and/or a wireless communication In at least some embodiments, for
25 example, sending the time until the future maintenance event to the
operator can
include wirelessly sending the time until the future maintenance event to a
computing device or system (e.g., a sales management tool, a customer
relationship
management tool, and the like) associated with the operator.
Additionally, or alternatively, at block 204 the method 200 can
30 include generating one or more recommendations and/or predictions. In
some
embodiments, one or more of the recommendations/predictions can be sent to the
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operator. Individual ones of the one or more recommendations can be based at
least
partially on the time until the future maintenance event (block 203), and can
be
related to the future maintenance event. In at least some embodiments, for
example,
sending a recommendation can include sending a recommendation to perform the
5 future maintenance event before the predicted time. Additionally, or
alternatively,
the recommendation can include a recommended time or time window in which
the future maintenance event should be performed. In some aspects,
recommending
that the future maintenance event be performed before the predicted time can
inhibit or prevent the associated parts(s), component(s), and/or machine from
10 failing, and/or can reduce the costs of repairing the associated
machine, for
example, by reducing the likelihood that failure/wear of one part/component
leads
to the failure/wear of other related part(s)/component(s) and a more expensive
maintenance event. In some embodiments, block 204 can include determining one
or more of the predictions automatically, for example, based at least
partially on a
15 rules-based model, a machine learning model, one or more computer
models,
and/or any other suitable process or technique.
In some embodiments, the recommendation can include a
recommendation to purchase one or more parts, components, tools, and/or the
like
associated with the future maintenance event. The recommendation can include,
20 for example, a recommendation to purchase one or more parts and/or
components
for replacement/maintenance during the future maintenance event. In at least
some
embodiments, the recommendation can include recommending one or more parts
and/or components associated with the future maintenance event to the operator
in
response to an indication/input (e.g., a database search query, a text input
into a
25 search field, a price quote request, and the like) that the operator is
searching for
parts/components associated with the future maintenance event. For example, if
an
operator searches a parts database for "piston," the recommendation can
include
recommending other parts associated with an engine rebuild (e.g., the
crankshaft).
Additionally, or alternatively, the recommendation can include a
recommendation
30 that the operator purchase one or more previously unpurchased parts and/or
components (e.g., parts/ components left in or removed from an online shopping
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cart, and the like) associated with the future maintenance event. In these and
other
embodiments the recommendation can include a recommendation to purchase one
or more parts and/or components that are related to (e.g., alternative
parts/components, equivalent parts/components, structurally and/or
functionally
5 similar
parts/components, and the like) the parts/components associated with the
future maintenance event.
In some embodiments, the time until the future maintenance event
(block 203) and/or the recommendation (block 204) can be based at least
partially
on a geographic location or region in which the machine has operated. For
example,
10 machines
in some geographic locations may benefit from more frequent
maintenance events than machines in other geographic locations, and the time
until
the future maintenance event (block 203) and/or the recommendation (block 204)
can vary based at least partially on the differences between these geographic
locations. As another example, the cost of a maintenance event in some
geographic
15 locations
may be greater than the cost of the maintenance event in other locations,
and/or a wear rate for a part may vary based on location, and the time until
the
future maintenance event (block 203) and/or the recommendation (block 204) can
vary based at least partially on the differences in cost and/or wear rate
between
these geographic locations.
20 In some
embodiments, the recommendation can include a change to
one or more supply chains. For example, one or more parts associated with the
future maintenance event can be recommended for increased production. In some
embodiments, the recommendation can include a research and/or development
focus. For example, one or more parts associated with the future maintenance
event
25 can be
recommended for redesign, e.g., to at least partially reduce the likelihood of
future repair events. Additionally, or alternatively, the recommendation can
include a recommendation to increase production of one or more parts and/or
components associated with the future maintenance event. In some aspects,
increasing production of parts and/or components that are subj ect to
relatively
30 frequent
maintenance can at least partially mitigate or prevent future supply chain
shortages.
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16
In some embodiments, block 204 can include predicting/identifying
information associated with the operator of the machine (block 201). In at
least
some embodiments, for example, block 203 can include classifying the operator'
s
machine maintenance history (e.g., does not perform rebuilds, frequently
performs
5 early maintenance, performs maintenance only when machine fails, and the
like)
and/or the operator's parts purchase history (e.g., only purchases parts in
bulk from
over-the-counter dealers, shops around for competitively-priced parts, and the
like).
In some embodiments, generating the one or more
10 recommendations and/or predictions can include testing one or more of
the
recommendations and/or predictions. In at least some embodiments, testing one
or
more of the recommendations and/or predictions can include testing one or more
of the recommendations and/or predictions based at least partially on
historical
maintenance data associated with a machine. For example, a predicted time
until a
15 future maintenance event for a given machine (block 203) can be compared
against
that machine's known maintenance history (e.g., at some time hours, days,
weeks,
years, etc., after the predicted time for that machine), for example, to
determine
whether the future maintenance event actually occurred, whether the future
maintenance event occurred within the predicted time, and/or an error or
difference
20 (e.g., in number of hours run by the machine) between the predicted time
and the
actual time at which the future maintenance event was performed. Additionally,
or
alternatively, testing one or more of the recommendations and/or predictions
can
include testing one or more of the recommendations and/or predictions based at
least partially on feedback from one or more operators associated with the
machine.
25 For example, after receiving the time until the future maintenance event
(block
204), the operators can respond based on their knowledge of the machine' s
maintenance history/record, and provide responses or other feedback
indicating,
for example, that the prediction is not accurate, that the machine has already
been
serviced/rebuilt, and the like. In these and other embodiments, testing one or
more
30 of the recommendations and/or predictions can include testing one or
more of the
recommendations and/or predictions based at least partially on received
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17
information that is associated with the future maintenance event. For example,
if a
recommendation to replace a given part for a given machine is sent to a given
operator, and at some time later (e.g., before, at, or after the predicted
time) the
operator submits an invoice including the part for the machine, the invoice
can
5 indicate that the predicted time and/or the future maintenance event were
correct.
Additionally, or alternatively, testing one or more of the recommendations
and/or
predictions can include testing one or more of the recommendations and/or
predictions based at least partially on data (e.g., sensor data, telematics
data, and
the like) from the machine. For example, the machine can include one or more
10 sensors associated with the machine's operation and configured to sense
data
related to the machine's components/parts, and the sensor data collected for
the
component(s)/part(s) associated the future maintenance event can be analyzed
and/or evaluated, for example, to determine whether the sensor data indicates
that
the component(s)/part(s) are operating normally or abnormally and/or whether
the
15 predicted time and/or the predicted future maintenance event is
accurate.
Although the steps of the method 200 are discussed and illustrated
in a particular order, the method 200 illustrated in Figure 2 is not so
limited. A
person of ordinary skill in the relevant art will recognize that the
illustrated method
200 can be altered and still remain within these and other embodiments of the
20 present technology. For example, one or more steps of the method 200
(e.g., block
204) illustrated in Figure 2 can be omitted and/or repeated in some
embodiments.
Figure 3 is a flow diagram illustrating a method 300 for predicting
maintenance events for machines in accordance with embodiments of the present
technology. The method 300 is illustrated as a set of blocks, steps,
operations, or
25 processes 301-306. Several of the blocks 301-306are described with
reference to
Figure 1 and/or Figure 2. All or a subset of the blocks 301-306 can be
executed at
least in part by various components of a device and/or a system, such as the
device
400 of Figure 4, and/or any other suitable device and/or system. Additionally,
or
alternatively, all or a subset of the blocks 301-306can be executed at least
in part
30 by an operator (e.g., a remote operator, a human operator, and
institutional
operator, a corporate operator, and the like) of the device and/or system.
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18
Furthermore, any one or more of the blocks 301-306 can be executed in
accordance
with the discussion herein.
The method 300 begins at block 301 by receiving information
associated with (i) one or more past maintenance events for a plurality of
industrial
5 machines and/or (ii) a plurality of industrial machine operators. Step
301 can
include some features generally similar or identical to features of step 201
of the
method 200. The plurality of industrial machine operators can be associated
with
one or more of the plurality of the industrial machines, such that the
received
information associated with the past maintenance events can also be
10 associated/correlated with one or more of the industrial machine
operators
At block 302, the method 300 continues by receiving data
associated with usage of one or more of the plurality of industrial machines.
The
received usage data can include one or more identifiers corresponding to one
or
more jobs or tasks, a number of operation hours, fuel burn data, machine
15 inspections, machine status alerts, one or more geographic locations or
regions in
which the industrial machine is operated, environmental conditions (e.g.,
temperature, elevation, terrain, and the like), temporal/seasonal conditions
(e.g.,
spring, summer, winter, fall, etc.) and/or any other suitable data associated
with
usage of one or more of the industrial machines.
20 At block 303, the method 300 continues by identifying, for
individual ones of the plurality of industrial machines and based at least
partially
on the received information, at least one of (i) a type of the machine, (ii) a
component of the machine that was serviced during the past maintenance event,
and/or (iii) a category of the past maintenance event. At least some aspects
of
25 block 303 can include features generally similar or identical to aspects
of block
202 of the method 200. Additionally, in some embodiments block 303 can further
include identifying a fleet of machines owned/operated by a same industrial
machine operator. For example, because the received information (block 301)
can
be associated with specific industrial machines and specific industrial
machine
30 operators, identifying the type of the machine in block 303 can be used
to identify
the types of machines in a fleet of machines owned/operated by one or more of
the
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19
industrial machine operators. In some aspects, this can further supplement or
"gap-
fill" the information known about individual ones of the industrial machine
operators.
In some embodiments, block 303 can include sorting the received
5 information (block 301) and/or identified information based on generally
similar
or identical aspects. In at least some embodiments, for example, block 303 can
further include aggregating the received and/or identified information
associated
with a given category of past maintenance event. As another example, block 303
can include aggregating the received and/or identified information associated
with
10 one or more industrial machines and/or one or more industrial machine
operators.
In these and other embodiments, the received and/or identified information can
be
aggregated/sorted based on any other characteristics described herein and/or
any
other suitable characteristics. The sorted information can be used to make one
or
more predictions and/or recommendations, such as described previously
regarding
15 block 204 of Figure 2 and in block 304 below.
At block 304, the method 300 continues by predicting, for the
individual ones of the plurality of industrial machines and based at least
partially
on the identification in block 303 and/or the received usage data (block 302),
a time
until a future maintenance event. Because the received information (block 301)
and
20 the received usage data (block 302) can be specific to or otherwise
associated with
individual industrial machines, the predicted time and/or the future
maintenance
event can be specific to individual ones of the plurality of the industrial
machines.
In at least some embodiments, for example, block 304 can including predicting
a
first time until a first future maintenance event for a first industrial
machine, and
25 predicting a second time until a second future maintenance event for a
second
industrial machine, and the first time and/or the first future maintenance
event can
be different than the second time and/or the second future maintenance event,
respectively. The difference between the prediction for the first machine and
the
second machine can be based at least partially on the received usage
information
30 (block 302), such as a difference in geographic location, environmental
conditions,
and/or seasonal conditions for the first and second machines, as described
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previously regarding the method 200 of Figure 2 In these and other
embodiments,
block 304 can include predicting a plurality of times for a corresponding
plurality
of future maintenance events for individual ones of the plurality of the
industrial
machines.
5 At block 305, the method 300 continues by identifying one or
more
subsets or segments of the plurality of industrial machine operators. In some
embodiments, identifying a subset of the plurality of industrial machine
operators
can include sorting/segmenting the received information (block 301), the usage
information (block 302), and/or the identified information (block 303) to
identify
10 generally similar industrial machine operators. For example, a
subset/segment of
industrial machine operators may all own a same type of industrial machine,
have
generally similar or identically machine fleets (e.g., size, type of machines,
industry sector, and the like), operate various industrial machines in a same
or
generally similar geographic location, perform maintenance events at a same or
15 generally similar frequency, and/or have performed generally similar or
the same
maintenance events in the past. The subset of the plurality of industrial
machine
operators can be identified manually and/or automatically (e.g., via one or
more
computer models, machine learning models, rules-based models, and the like).
Additionally, or alternatively, the subset of the industrial machine operators
can be
20 based at least partially on an operator hierarchy, including parent-
child business
relationships between the operators (e.g., all operators owned by Company A,
all
operators that own two own companies with specific type(s) of machine(s), and
the
like) In at least some embodiments, block 305 can include identifying a
plurality
of subsets, such substantially all or every industrial machine operator is
25 grouped/sorted into a corresponding subset.
At block 306, the method 300 continues by sending information
associated with individual ones of the plurality of industrial machines to at
least
the subset/segment of the plurality of industrial machine operators (block
305). At
least some aspects of block 306 can be generally similar or identical to
aspects of
30 block 204 of the method 200. The information can include the predicted
time until
the future maintenance event (block 304), one or more recommendations
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21
associated with individual ones of the industrial machines, and/or any other
suitable information. Additionally, in at least some embodiments block 304 can
include generating one or more predictions and/or recommendations without
sending them to the operators, and/or sending one or more of the generated
5 predictions and/or recommendations to all operators. The predictions
and/or
recommendations can include at least some aspects that are generally similar
to or
the same as the recommendations discussed in detail regarding block 204 of the
method 200. In some embodiments, generating one or more of
the
recommendations and/or predictions can include testing one or more of the
10 recommendations and/or predictions for all, a subset, or individual ones
of the
machines, such as described previously regarding Figure 2.
Although the steps of the method 300 are discussed and illustrated
in a particular order, the method 300 illustrated in Figure 3 is not so
limited. A
person of ordinary skill in the relevant art will recognize that the
illustrated method
15 300 can be altered and still remain within these and other embodiments
of the
present technology. For example, one or more steps of the method 300 (e.g.,
block
306) illustrated in Figure 2 can be omitted and/or repeated in some
embodiments.
The techniques disclosed herein can be embodied as special-
purpose hardware (e.g., circuitry), as programmable circuitry appropriately
20 programmed with software and/or firmware, or as a combination of special-
purpose and programmable circuitry. Hence, embodiments may include a machine-
readable medium having stored thereon instructions which may be used to cause
a
computer, a microprocessor, processor, and/or microcontroller (or other
electronic
devices) to perform a process. The machine-readable medium may include, but is
25 not limited to, optical disks, compact disc read-only memories (CD-
ROMs),
magneto-optical disks, ROMs, random access memories (RAN/Is), erasable
programmable read-only memories (EPROMs), electrically erasable
programmable read-only memories (EEPROMs), magnetic or optical cards, flash
memory, or other type of media/machine-readable medium suitable for storing
30 electronic instructions.
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22
Several implementations are discussed below in more detail in
reference to the figures. Figure 4 is a block diagram illustrating an overview
of
devices on which some implementations of the disclosed technology can operate.
The devices can comprise hardware components of a system 400 that predicts
5 maintenance events, for example. System 400 can include one or more input
devices 420 that provide input to the CPU (processor) 410, notifying it of
actions.
The actions are typically mediated by a hardware controller that interprets
the
signals received from the input device and communicates the information to the
CPU 410 using a communication protocol. Input devices 420 include, for
example,
10 a mouse, a keyboard, a touch screen, an infrared sensor, a touchpad, a
wearable
input device, a camera- or image-based input device, a microphone, or other
user
input devices.
CPU 410 can be a single processing unit or multiple processing
units in a device or distributed across multiple devices. CPU 410 can be
coupled
15 to other hardware devices, for example, with the use of a bus, such as a
PCI bus or
SCSI bus. The CPU 410 can communicate with a hardware controller for devices,
such as for a display 430. Display 430 can be used to display text and
graphics. In
some examples, display 430 provides graphical and textual visual feedback to a
user. In some implementations, display 430 includes the one or more of input
20 devices 420 as part of the display 430, such as when one of the input
devices 420
is a touchscreen or is equipped with an eye direction monitoring system. In
some
implementations, the display 430 is separate from the input devices 420.
Examples
of display devices are. an LCD display screen; an LED display screen; a
projected,
holographic, or augmented reality display (such as a heads-up display device
or a
25 head-mounted device); and so on. Other I/O devices 440 can also be
coupled to the
processor, such as a network card, video card, audio card, USB, FireWire or
other
external device, sensor, camera, printer, speakers, CD-ROM drive, DVD drive,
disk drive, or Blu-Ray device.
In some implementations, the system 400 also includes a
30 communication device capable of communicating wirelessly or wire-based
with a
network node. The communication device can communicate with another device
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23
or a server through a network using, for example, TCP/IP protocols. System 400
can utilize the communication device to distribute operations across multiple
network devices.
The CPU 410 can have access to a memory 450. The memory 450
5 includes one or more of various hardware devices for volatile and non-
volatile
storage, and can include both read-only and writable memory. For example, the
memory 450 can comprise random access memory (RAM), CPU registers, read-
only memory (ROM), and writable non-volatile memory, such as flash memory,
hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives,
10 device buffers, and so forth. The memory is not a propagating signal
divorced from
underlying hardware; the memory is thus non-transitory. The memory 450 can
include program memory 460 that stores programs and software, such as an
operating system 462, Predictor 464 (which may include instructions for
carrying
out the methods of maintenance event prediction disclosed herein), and other
15 application programs 466. Memory 450 can also include data memory 470
that can
include database information, etc., which can be provided to the program
memory
460 or any element of the device 400.
Some implementations can be operational with numerous other
general purpose or special purpose computing system environments or
20 configurations. Examples of well-known computing systems, environments,
and/or configurations that may be suitable for use with the technology
include, but
are not limited to, personal computers, server computers, handheld or laptop
devices, cellular telephones, mobile phones, wearable electronics, gaming
consoles, tablet devices, multiprocessor systems, microprocessor-based
systems,
25 set-top boxes, programmable consumer electronics, network PCs,
minicomputers,
mainframe computers, distributed computing environments that include any of
the
above systems or devices, or the like.
Figure 5 is a block diagram illustrating an overview of an
environment 500 in which some implementations of the disclosed technology can
30 operate. Environment 500 can include one or more client computing
devices 505A-
D, examples of which can include the system 400 of Figure 4. Client computing
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24
devices 505 can operate in a networked environment using logical connections
through network 530 to one or more remote computers, such as a server
computing
device 510.
In some implementations, server computing device 510 can be an
5 edge
server that receives client requests and coordinates fulfillment of those
requests through other servers, such as servers 520A-C. Server computing
devices
510 and/or servers 520A-C can comprise computing systems, such as the system
400 of Figure 4. Though the server computing device 510 and servers 520A-C are
each displayed logically as a single server, servers 510 and/or server
computing
10 devices
520A-C can each be a distributed computing environment encompassing
multiple computing devices located at the same or at geographically disparate
physical locations. In some implementations, each server 510 and/or server
computing device 520A-C corresponds to a group of servers
Client computing devices 505, server 510, and/or server computing
15 devices
520A-C can each act as a server or client to other server/client devices.
Server 510 can connect to a database 515. Server computing devices 520A-C can
each connect to a corresponding database 525A-C. As discussed above, each
server
computing device 520A-C can correspond to a group of servers, and each of
these
servers can share a database or can have their own database. Databases 515 and
20 525 can
warehouse (e.g., store) information. Though databases 515 and 525 are
displayed logically as single units, databases 515 and 525 can each be a
distributed
computing environment encompassing multiple computing devices, can be located
within their corresponding server, or can be located at the same or at
geographically
disparate physical locations.
25 Network
530 can be a local area network (LAN) or a wide area
network (WAN), but can also be other wired or wireless networks. Network 530
may be the Internet or some other public or private network. Client computing
devices 505 can be connected to network 530 through a network interface, such
as
by wired or wireless communication. While the connections between server 510
30 and
server computing devices 520A-C are shown as separate connections, these
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connections can be any kind of local, wide area, wired, or wireless network,
including network 530 or a separate public or private network.
Figure 6 is a block diagram illustrating elements 600 which, in some
implementations, can be used in a system employing the disclosed technology.
The
5 elements 600 include hardware 602, general software 620, and specialized
elements 640. As discussed above, a system implementing the disclosed
technology can use various hardware, including processing units 604 (e.g.,
CPUs,
GPUs, APUs, etc.), working memory 606, storage memory 608, and input and
output devices 610. Elements 600 can be implemented in a client computing
device
10 such as client computing devices 505 or on a server computing device,
such as
server 510 and/or server computing devices 520A-C.
General software 620 can include various applications, including an
operating system 622, local programs 624, and a basic input output system
(BIOS)
626. Specialized components 640 can be subcomponents of a general software
15 application 620, such as local programs 624. Specialized elements 640
can include
a Maintenance Event Information Module 644, a Usage Information Module 646,
a Machine Information Identification Module 648, a Maintenance Event Predictor
Module 650, and a Recommendation Module 652, and components that can be
used for transferring data and controlling the specialized components, such as
20 interface 642. In some implementations, elements 600 can be in a
computing
system that is distributed across multiple computing devices or can be an
interface
to a server-based application executing one or more of specialized elements
640.
Those skilled in the art will appreciate that the components
illustrated in Figures 4-6 described above, and in each of the Figures 1-3
discussed
25 above, may be altered in a variety of ways. For example, the order of
the logic may
be rearranged, sub steps may be performed in parallel, illustrated logic may
be
omitted, other logic may be included, etc. In some implementations, one or
more
of the components described above can execute one or more of the processes
described herein.
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26
B. Industrial Applicability
In some embodiments, systems for automatically recommending
repair can include a Maintenance Event Information Module 644, a Usage
Information Module 646, a Machine Information Identification Module 648, a
5 Maintenance Event Predictor Module 650, and a Recommendation Module 652
(Figure 6). In operation, the Maintenance Event Information Module 644 can
receive and/or store maintenance event information (see block 201 of the
method
200 and block 301 of the method 300). The Usage Information Module 646 can
receive and store machine usage information (see block 302 of the method 300).
10 The Machine Information Identification Module 648 can include
instructions for
identifying information associated with a machine, such as (i) a type of the
machine, (ii) a component of the machine that was serviced during the past
maintenance event, and (iii) a category of the past maintenance event (see
block
202 of the method 200, block 303 of the method 300). The Maintenance Event
15 Prediction Module 650 can include instructions for predicting a future
maintenance
event and/or a time until the future maintenance event (see block 203 of the
method
200, block 304 of the method 300). The Recommendation Module 652 can include
instructions, models, and the like for making recommendations (see block 204
of
the method 200, block 306 of the method 300). The Recommendation Module 562
20 can make recommendations based at least partially on the future
maintenance event
and/or the time until the future maintenance event identified by the
Maintenance
Event Prediction Module 650. The disclosed technology, therefore, can
automatically predict a future maintenance event for a machine, predict a time
until
the future maintenance event, and/or make a recommendation, such as a time to
25 complete a future maintenance event, which can be based on impartial or
incomplete information about a given machine and/or the given machine's
maintenance history.
C. Conclusion
From the foregoing, it will be appreciated that specific
30 embodiments of the technology have been described herein for purposes of
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27
illustration, but well-known structures and functions have not been shown or
described in detail to avoid unnecessarily obscuring the description of the
embodiments of the technology. To the extent any materials incorporated herein
by reference conflict with the present disclosure, the present disclosure
controls.
5 Where the context permits, singular or plural terms can also include the
plural or
singular term, respectively. Moreover, unless the word "or" is expressly
limited to
mean only a single item exclusive from the other items in reference to a list
of two
or more items, then the use of "or" in such a list is to be interpreted as
including
(a) any single item in the list, (b) all of the items in the list, or (c) any
combination
10 of the items in the list. As used herein, the phrase "and/or" as in "A
and/or B" refers
to A alone, B alone, and both A and B. Where the context permits, singular or
plural terms can also include the plural or singular term, respectively.
Additionally,
the terms "comprising," "including," "having" and "with" are used throughout
to
mean including at least the recited feature(s) such that any greater number of
the
15 same feature and/or additional types of other features are not
precluded.
Furthermore, as used herein, the term -substantially- refers to the
complete or nearly complete extent or degree of an action, characteristic,
property,
state, structure, item, or result. For example, an object that is
"substantially"
enclosed would mean that the object is either completely enclosed or nearly
20 completely enclosed. The exact allowable degree of deviation from
absolute
completeness may in some cases depend on the specific context. However,
generally speaking the nearness of completion will be so as to have the same
overall result as if absolute and total completion were obtained The use of
"substantially" is equally applicable when used in a negative connotation to
refer
25 to the complete or near complete lack of an action, characteristic,
property, state,
structure, item, or result. Moreover, the terms "connect" and "couple" are
used
interchangeably herein and refer to both direct and indirect connections or
couplings. For example, where the context permits, element A "connected" or
-coupled" to element B can refer (i) to A directly "connected" or directly -
coupled"
30 to B, and/or (ii) to A indirectly "connected" or indirectly "coupled" to
B.
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28
The above detailed descriptions of embodiments of the technology
are not intended to be exhaustive or to limit the technology to the precise
form
disclosed above. Although specific embodiments of, and examples for, the
technology are described above for illustrative purposes, various equivalent
5 modifications are possible within the scope of the technology, as those
skilled in
the relevant art will recognize. For example, while blocks are presented in a
given
order, alternative embodiments can perform blocks in a different order. As
another
example, various components of the technology can be further divided into
subcomponents, and/or various components and/or functions of the technology
can
10 be combined and/or integrated. Furthermore, although advantages
associated with
certain embodiments of the technology have been described in the context of
those
embodiments, other embodiments can also exhibit such advantages, and not all
embodiments need necessarily exhibit such advantages to fall within the scope
of
the technology.
15 The headings provided herein are for convenience only and do
not
necessarily affect the scope of the embodiments. Further, the drawings have
not
necessarily been drawn to scale. For example, the dimensions of some of the
elements in the figures may be expanded or reduced to help improve the
understanding of the embodiments. Moreover, while the disclosed technology is
20 amenable to various modifications and alternative forms, specific
embodiments
have been shown by way of example in the drawings and are described in detail
below. The intention, however, is not to unnecessarily limit the embodiments
described Rather, the embodiments are intended to cover all modifications,
combinations, equivalents, and alternatives falling within the scope of this
25 disclosure.
It should also be noted that other embodiments in addition to those
disclosed herein are within the scope of the present technology. For example,
embodiments of the present technology can have different configurations,
components, and/or procedures in addition to those shown or described herein.
30 Moreover, a person of ordinary skill in the art will understand that
these and other
embodiments can be without several of the configurations, components, and/or
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PCT/US2022/022235
29
procedures shown or described herein without deviating from the present
technology. Accordingly, the disclosure and associated technology can
encompass
other embodiments not expressly shown or described herein.
Reference in this specification to "one embodiment" or "an
5 embodiment" means that a particular feature, structure, or characteristic
described
in connection with the embodiment is included in at least one embodiment of
the
disclosure. The appearances of the phrase "in one embodiment" (or the like) in
various places in the specification are not necessarily all referring to the
same
embodiment, nor are separate or alternative embodiments mutually exclusive of
10 other embodiments. Moreover, various features are described which may be
exhibited by some embodiments and not by others. Similarly, various
requirements
are described which may be requirements for some embodiments but not for other
embodiments.
The terms used in this specification generally have their ordinary
15 meanings in the art, within the context of the disclosure, and in the
specific context
where each term is used. It will be appreciated that the same thing can be
said in
more than one way. Consequently, alternative language and synonyms may be used
for any one or more of the terms discussed herein, and any special
significance is
not to be placed upon whether or not a term is elaborated or discussed herein.
20 Synonyms for some terms are provided. A recital of one or more synonyms
does
not exclude the use of other synonyms. The use of examples anywhere in this
specification, including examples of any term discussed herein, is
illustrative only
and is not intended to further limit the scope and meaning of the disclosure
or of
any exemplified term. Likewise, the disclosure is not limited to various
25 embodiments given in this specification. Unless otherwise defined, all
technical
and scientific terms used herein have the same meaning as commonly understood
by one of ordinary skill in the art to which this disclosure pertains. In the
case of
conflict, the present document, including definitions, will control.
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