The invention generally relates to automated, robotic and other object processing systems such as sortation systems, and relates in particular to automated and robotic systems intended for use in environments requiring, for example, that a variety of objects (e.g., parcels, packages, and articles etc.) be processed and distributed to several output destinations within a confined space.
Many parcel distribution systems receive parcels from a vehicle, such as a trailer of a tractor trailer. The parcels are unloaded and delivered to a processing station in a disorganized stream that may be provided as individual parcels or parcels aggregated in groups such as in bags, and may be provided to any of several different conveyances, such as a conveyor, a pallet, a Gaylord, or a bin. Each parcel must then be distributed to the correct destination container, as determined by identification information associated with the parcel, which is commonly determined by a label printed on the parcel or on a sticker applied to the parcel. The destination container may take many forms, such as a bag or a bin.
The sortation of such parcels from the vehicle has traditionally been done, at least in part, by human workers that scan the parcels, e.g., with a hand-held barcode scanner, and then place the parcels at assigned locations. For example, many order fulfillment operations achieve high efficiency by employing a process called wave picking. In wave picking, orders are picked from warehouse shelves and placed at locations (e.g., into bins) containing multiple orders that are sorted downstream. At the sorting stage individual articles are identified, and multi-article orders are consolidated, for example into a single bin or shelf location, so that they may be packed and then shipped to customers. The process of sorting these objects has traditionally been done by hand. A human sorter picks an object from an incoming bin, finds a barcode on the object, scans the barcode with a handheld barcode scanner, determines from the scanned barcode the appropriate bin or shelf location for the object, and then places the object in the so-determined bin or shelf location where all objects for that order have been defined to belong. Automated systems for order fulfillment have also been proposed. See for example, U.S. Patent Application Publication No. 2014/0244026, which discloses the use of a robotic arm together with an arcuate structure that is movable to within reach of the robotic arm.
Other ways of identifying items by code scanning either require manual processing, or require that the code location be controlled or constrained so that a fixed or robot-held code scanner (e.g., barcode scanner) can reliably detect it. Manually operated barcode scanners are generally either fixed or handheld systems. With fixed systems, such as those used at point-of-sale systems, the operator holds the object and places it in front of the scanner so that the barcode faces the scanning device's sensors, and the scanner, which scans continuously, decodes any barcodes that it can detect. If the object is not immediately detected, the person holding the object typically needs to vary the position or rotation of the object in front of the fixed scanner, so as to make the barcode more visible to the scanner. For handheld systems, the person operating the scanner looks for the barcode on the object, and then holds the scanner so that the object's barcode is visible to the scanner, and then presses a button on the handheld scanner to initiate a scan of the barcode.
Additionally, current distribution center sorting systems generally assume an inflexible sequence of operations whereby a disorganized stream of input objects is first singulated by human workers into a single stream of isolated objects presented one at a time to a human worker with a scanner that identifies the object. The objects are then loaded onto a conveyor, and the conveyor then transports the objects to the desired destination, which may be a bin, a chute, a bag or a destination conveyor.
In conventional parcel sortation systems, human workers typically retrieve parcels in an arrival order, and sort each parcel or object into a collection bin based on a set of given heuristics. For instance, all objects of like type might be routed to a collection bin, or all objects in a single customer order might be routed to a particular collection bin, or all objects destined for the same shipping destination, etc. may be routed to a particular collection bin. The human workers or automated routing systems are required to receive objects and to move each to their assigned collection bin. If the number of different types of input (received) objects is large, a large number of collection bins is required.
Such a system has inherent inefficiencies as well as inflexibilities since the desired goal is to match incoming objects to assigned collection bins. Such systems may require a large number of collection bins (and therefore a large amount of physical space, large capital costs, and large operating costs) in part, because sorting all objects to all destinations at once is not always most efficient.
Current state-of-the-art sortation systems rely on human labor to some extent. Most solutions rely on a worker that is performing sortation, by scanning an object from an induction area (chute, table, etc.) and placing the object in a staging location, conveyor, or collection bin. When a bin is full, another worker empties the bin into a bag, box, or other container, and sends that container on to the next processing step. Such a system has limits on throughput (i.e., how fast can human workers sort to or empty bins in this fashion) and on number of diverts (i.e., for a given bin size, only so many bins may be arranged to be within efficient reach of human workers).
Other partially automated sortation systems involve the use of recirculating conveyors and tilt trays, where the tilt trays receive objects by human sortation, and each tilt tray moves past a scanner. Each object is then scanned and moved to a pre-defined location assigned to the object. The tray then tilts to drop the object into the location. Other systems that include tilt trays may involve scanning an object (e.g., using a tunnel scanner), dropping the object into a tilt tray, associating the object with the specific tilt tray using a known location or position, for example, a using beam breaks, and then causing the tilt tray to drop the object when it is at the desired location.
Further, partially automated systems, such as the bomb-bay style recirculating conveyor, involve having trays open doors on the bottom of each tray at the time that the tray is positioned over a predefined chute, and the object is then dropped from the tray into the chute. Again, the objects are scanned while in the tray, which assumes that any identifying code is visible to the scanner.
Such partially automated systems are lacking in key areas. As noted, these conveyors have discrete trays that can be loaded with an object; the trays then pass through scan tunnels that scan the object and associate it with the tray in which it is riding. When the tray passes the correct bin, a trigger mechanism causes the tray to dump the object into the bin. A drawback with such systems however, is that every divert requires an actuator, which increases the mechanical complexity and the cost per divert can be very high.
An alternative is to use human labor to increase the number of diverts, or collection bins, available in the system. This decreases system installation costs, but increases the operating costs. Multiple cells may then work in parallel, effectively multiplying throughput linearly while keeping the number of expensive automated diverts at a minimum. Such diverts do not ID an object and cannot divert it to a particular spot, but rather they work with beam breaks or other sensors to seek to ensure that indiscriminate bunches of objects get appropriately diverted. The lower cost of such diverts coupled with the low number of diverts keep the overall system divert cost low.
Unfortunately, these systems don't address the limitations to total number of system bins. The system is simply diverting an equal share of the total objects to each parallel manual cell. Thus each parallel sortation cell must have all the same collection bins designations; otherwise an object might be delivered to a cell that does not have a bin to which that object is mapped. There remains a need for a more efficient and more cost effective object sortation system that sorts objects of a variety of sizes and weights into appropriate collection bins or trays of fixed sizes, yet is efficient in handling objects of such varying sizes and weights.
Further, such systems do not adequately account for the overall process in which objects are first delivered to and provided at a processing station by a vehicle such as a trailer of a tractor trailer. Additionally, many processing stations, such as sorting stations for sorting parcels, are at times, at or near full capacity in terms of available floor space and sortation resources.
In accordance with an embodiment, the invention provides an object processing system within a trailer for tractor trailer. The object processing system includes an input area of the trailer at which objects to be processed may be presented, a perception system for providing perception data regarding objects to be processed, and a primary transport system for providing transport of each object in one of at least two primary transport directions within the trailer based on the perception data.
In accordance with another embodiment, the invention provides a system for providing processing of objects within a trailer for a tractor trailer. The system includes an input area within the trailer for receiving objects to be processed, a singulation system within the trailer for providing a singulated stream or objects within the trailer, and a perception system for receiving the singulated stream of objects within the trailer, and for generating perception data for facilitating the processing of the objects within the trailer.
In accordance with another embodiment, the invention provides a method of providing processing of objects within a trailer for a-tractor trailer. The method includes the steps of: providing perception data regarding an object, transporting of the object in one of at least two primary directions based on the perception data, and transporting the object from the one of at least two primary directions into one of at least two secondary directions based on the perception data.
In accordance with a further embodiment, the invention provides a method of providing processing of objects within a trailer of a tractor trailer. The method includes the steps of: providing a singulated stream of objects within the trailer, providing perception data regarding an object, and transporting of the object in one of at least two primary directions within the trailer based on the perception data.
The following description may be further understood with reference to the accompanying drawings in which:
The drawings are shown for illustrative purposes only.
In accordance with an embodiment, the invention provides a processing (e.g., sortation) system within a trailer of a tractor trailer, such that objects may be provided to the processing system, and processed within the trailer. For example, the trailer may include an input system for receiving a wide variety of objects to be sorted, a singulation system for providing a singulated stream of objects for efficient processing of the objects, an identification system, and routing system for delivering the objects to desired destinations. Generally, individual parcels need to be identified and conveyed to desired parcel-specific locations. The described systems reliably automate the identification and conveyance of such parcels, employing in certain embodiments, a set of conveyors and sensors and a scanning system. In short, applicants have discovered that when automating the sortation of objects, there are a few main things to consider: 1) the overall system throughput (parcels sorted per hour), 2) the number of diverts (i.e., number of discrete locations to which an object can be routed), 3) the total area of the sortation system (square feet), 4) sort accuracy, and 5) the capital and annual costs to run the system.
Sorting objects in a shipping distribution center is one application for automatically identifying and sorting parcels. In a shipping distribution center, parcels commonly arrive in trucks, totes, Gaylords or other vessels for delivery, are conveyed to sortation stations where they are sorted according to desired destinations, aggregated in bags, and then loaded back in trucks for transport to the desired destinations. Other applications may include the shipping department of a retail store or order fulfillment center, which may require that parcels be sorted for transport to different shippers, or to different distribution centers of a particular shipper. In a shipping or distribution center, the parcels may take form of plastic bags, boxes, tubes, envelopes, or any other suitable container, and in some cases may also include objects not in a container. In a shipping or distribution center the desired destination is commonly obtained by reading identifying information printed on the parcel or on an attached label. In this scenario the destination corresponding to identifying information is commonly obtained by querying the customer's information system. In other scenarios the destination may be written directly on the parcel, or may be known through other means.
In accordance with various embodiments, therefore, the invention provides a method of taking individual parcels from a disorganized stream of parcels, providing a singulated stream of objects, identifying individual parcels, and sorting them to desired destinations, all within a confined location such as within a trailer of a tractor trailer. The invention further provides methods for conveying parcels from one point to the next, for excluding inappropriate or unidentifiable parcels, for grasping parcels, for determining grasp locations, for determining robot motion trajectories, for transferring parcels from one conveyor to another, for aggregating parcels and transferring to output conveyors, for digital communication within the system and with outside information systems, for communication with human operators and maintenance staff, and for maintaining a safe environment.
Important components of an automated object identification and processing system, in accordance with an embodiment of the present invention, are shown in
The singulated stream of objects is delivered to a drop perception unit 36 (as discussed below) as a singulated stream and without requiring that a robotic system place objects into the drop perception unit. By providing a singulated stream of objects for processing, the system is able to more effectively control the object processing rate, and reducing the incidence of errors that may occur, for example of two objects in close contact with each other are perceived as being one object. The infeed conveyor 16 may also be in communication with a controller 38, and the speed of the infeed conveyor 16 as well as the speed (and even direction) of the primary conveyor 20 may be adjusted to either slow down if moving too fast, or speed up if system determines that more bandwidth exists for a faster input.
Objects then drop through the drop perception unit 36 and fall onto a secondary conveyor 40, and one or more diverters 42, 44 may be employed to divert each object in a desired direction. If an object on the conveyor 40 is not diverted, then the object will fall into an unsorted collection bin 46. When the diverter 42 is engaged to divert an object off of the conveyor 40, the object falls to a carriage 48 that reciprocally runs along a track 50. The contained object in the carriage 48 may then be selectively dumped onto one of a plurality of chutes 52, 54, 56, 58, 60, 62 toward a respective drop container 64, 66, 68, 70, 72, 74, which each include a bomb-bay style bottom drop floor as will be discussed in more detail below. When the diverter 44 is engaged to divert an object off of the conveyor 40, the object falls to a carriage 76 that reciprocally runs along a track 78. The contained object in the carriage 76 may then be selectively dumped onto one of a plurality of chutes 80, 82, 84, 86, 88, 90, 92, 94 toward a respective drop container 96, 98, 100, 102, 104, 106, 108, 110, which each include a bomb-bay style bottom drop floor.
When any of the drop containers 64, 66, 68 is full or otherwise complete and ready for further processing, the bottom of the ready container is dropped onto a conveyor 112 where the contents are moved toward a destination bin 114. Prior to reaching the destination bin 114 however, the contents are passed through an automatic bagging and labeling device 116 as will be discussed below in more detail. When any of the drop containers 70, 72, 74 is full or otherwise complete and ready for further processing, the bottom of the ready container is dropped onto a conveyor 118 where the contents are moved through an automatic bagging and labeling device 120 toward a destination bin 122. Further, when any of the drop containers 96, 98, 100, 102, 104, 106, 108, 110 is full or otherwise complete and ready for further processing, the contents of the ready container is dropped onto a conveyor 124 where the contents are moved through an automatic bagging and labeling device 126 toward a destination bin 128. The destination bin 114 may be accessed through doors 130 in the trailer, and the destination bins 120 (as well as the unsorted collection bin 46) may be accessed through doors 132 in the trailer. The destination bin 128 (as well as the input hopper 14 and the controller 38) may be accessed through doors 134 at the rear of the trailer.
Again, a singulated stream of objects are delivered to the drop perception unit 36 (as discussed below), and by providing a singulated stream of objects for processing, the system is able to more effectively control the object processing rate, and reducing the incidence of errors that may occur, for example of two objects in close contact with each other are perceived as being one object. The infeed conveyor 16 may also be in communication with a controller 38, and speed of the infeed conveyor 16 as well as the speed (and even direction) of the circular conveyor 158 may be adjusted to either slow down if moving too fast, or speed up if system determines that more bandwidth exists for a faster input. The remaining portions of the system 150 having reference numerals from
Portions of the systems 10 and 150 are described below in more detail. The perception unit 36 (which may be mounted to a side wall of the trailer, may be supported by stands or may be suspended from above) includes a structure 170 having a top opening 172 and a bottom opening 174, and the walls may be covered by an enclosing material 176 (e.g., a colored covering such as orange plastic, to protect humans from potentially dangerously bright lights within the perception unit 36) as shown in
An important aspect of systems of certain embodiments of the present invention, is the ability to identify via barcode or other visual markings of objects, unique indicia associated with the object by employing a perception system into which objects may be dropped. Automated scanning systems would be unable to see barcodes on objects that are presented in a way that their barcodes are not exposed or visible. The perception system may be used in certain embodiments, with a robotic system that may include a robotic arm equipped with sensors and computing, that when combined is assumed herein to exhibit the following capabilities: (a) it is able to pick objects up from a specified class of objects, and separate them from a stream of heterogeneous objects, whether they are jumbled in a bin, or are singulated on a motorized or gravity conveyor system; (b) it is able to move the object to arbitrary places within its workspace; (c) it is able to place objects in an outgoing bin or shelf location in its workspace; and, (d) it is able to generate a map of objects that it is able to pick, represented as a candidate set of grasp points in the workcell, and as a list of polytopes enclosing the object in space.
The allowable objects are determined by the capabilities of the robotic system. Their size, weight and geometry are assumed to be such that the robotic system is able to pick, move and place them. These may be any kind of ordered goods, packages, parcels, or other articles that benefit from automated sorting. Each object is associated with unique indicia such as a unique code (e.g., barcode) or a unique destination (e.g., address) of the object.
The manner in which inbound objects arrive may be for example, in one of two configurations: (a) inbound objects arrive piled in bins of heterogeneous objects; or (b) inbound articles arrive by a moving conveyor. The collection of objects includes some that have exposed bar codes and other objects that do not have exposed bar codes. The robotic system is assumed to be able to pick items from the bin or conveyor. The stream of inbound objects is the sequence of objects as they are unloaded either from the bin or the conveyor.
The manner in which outbound objects are organized is such that objects are placed in a bin, shelf location or container, into which all objects corresponding to a given order are consolidated. These outbound destinations may be arranged in vertical arrays, horizontal arrays, grids, or some other regular or irregular manner, but which arrangement is known to the system. The robotic pick and place system is assumed to be able to place objects into all of the outbound destinations, and the correct outbound destination is determined from unique identifying indicia (identify or destination, such as a bar code or a unique address), which identifies the object or it's destination.
It is assumed that the objects are marked in one or more places on their exterior with a visually distinctive mark such as a barcode or radio-frequency identification (RFID) tag so that they may be identified with a scanner. The type of marking depends on the type of scanning system used, but may include 1D or 2D barcode symbologies. Multiple symbologies or labeling approaches may be employed. The types of scanners employed are assumed to be compatible with the marking approach. The marking, either by barcode, RFID tag, or other means, encodes a symbol string, which is typically a string of letters and numbers. The symbol string is uniquely associates the object with unique identifying indicia (identity or destination).
The operations of the systems described herein are coordinated by the central control system 38 as shown in
During operation, the broad flow of work may be generally as follows. First, the system is equipped with a manifest that provides the outbound destination for each inbound object. Next, the system waits for inbound objects to arrive either in a bin or on a conveyor. The robotic system may pick one item at a time from the input bin, and may drop each item into the perception system discussed above. If the perception system successfully recognizes a marking on the object, then the object is then identified and forwarded to a sorting station or other processing station. If the object is not identified, the robotic system may either replace the object back onto the input conveyor and try again, or the conveyor may divert the object to a human sortation bin to be reviewed by a human.
The sequence of locations and orientations of the perception units 36 are chosen so as to minimize the average or maximum amount of time that scanning takes. Again, if the object cannot be identified, the object may be transferred to a special outbound destination for unidentified objects, or it may be returned to the inbound stream. This entire procedure operates in a loop until all of the objects in the inbound set are depleted. The objects in the inbound stream are automatically identified, sorted, and routed to outbound destinations.
In accordance with an embodiment therefore, the invention provides a system for sorting objects that arrive inbound bins and that need to be placed into a shelf of outbound bins, where sorting is to be based on a unique identifier symbol. Key specializations in this embodiment are the specific design of the perception system so as to maximize the probability of a successful scan, while simultaneously minimizing the average scan time. The probability of a successful scan and the average scan time make up key performance characteristics. These key performance characteristics are determined by the configuration and properties of the perception system, as well as the object set and how they are marked.
The two key performance characteristics may be optimized for a given item set and method of barcode labeling. Parameters of the optimization for a barcode system include how many barcode scanners, where and in what orientation to place them, and what sensor resolutions and fields of view for the scanners to use. Optimization can be done through trial and error, or by simulation with models of the object.
Optimization through simulation employs a barcode scanner performance model. A barcode scanner performance model is the range of positions, orientations and barcode element size that a barcode symbol can be detected and decoded by the barcode scanner, where the barcode element size is the size of the smallest feature on the barcode. These are typically rated at a minimum and maximum range, a maximum skew angle, a maximum pitch angle, and a minimum and maximum tilt angle.
Typical performance for camera-based barcode scanners are that they are able to detect barcode symbols within some range of distances as long as both pitch and skew of the plane of the symbol are within the range of plus or minus 45 degrees, while the tilt of the symbol can be arbitrary (between 0 and 360 degrees). The barcode scanner performance model predicts whether a given barcode symbol in a given position and orientation will be detected.
The barcode scanner performance model is coupled with a model of where barcodes would expect to be positioned and oriented. A barcode symbol pose model is the range of all positions and orientations, in other words poses, in which a barcode symbol will expect to be found. For the scanner, the barcode symbol pose model is itself a combination of an article gripping model, which predicts how objects will be held by the robotic system, as well as a barcode-item appearance model, which describes the possible placements of the barcode symbol on the object. For the scanner, the barcode symbol pose model is itself a combination of the barcode-item appearance model, as well as an inbound-object pose model, which models the distribution of poses over which inbound articles are presented to the scanner. These models may be constructed empirically, modeled using an analytical model, or approximate models may be employed using simple sphere models for objects and a uniform distribution over the sphere as a barcode-item appearance model.
As further shown with reference to
With reference to
As shown in
If a next bin is available (and the system may permit any number of bins per station), the system will then assign the object to a next bin (step 316). The system then places the object into the assigned bin (step 318), and updates the number of objects in the bin (step 320). The system them determines whether the bin is full (step 322) and if not, determines whether the bin is unlikely to receive a further object in the near future (step 324). If the answer to either is yes, the system indicates that the bin is ready for further processing (step 326). Otherwise, the system then returns to step 302 until finished.
A process of the overall control system is shown, for example, in
Systems of various embodiments provide numerous advantages because of the inherent dynamic flexibility. The flexible correspondence between sorter outputs and destinations provides that there may be fewer sorter outputs than destinations, so the entire system may require less space. The flexible correspondence between sorter outputs and destinations also provides that the system may choose the most efficient order in which to handle objects, in a way that varies with the particular mix of objects and downstream demand. The system is also easily scalable, by adding sorters, and more robust since the failure of a single sorter might be handled dynamically without even stopping the system. It should be possible for sorters to exercise discretion in the order of objects, favoring objects that need to be handled quickly, or favoring objects for which the given sorter may have a specialized gripper.
While the assignment of objects to destinations is fixed (e.g., each object has an identifier such as a label or barcode that is associated with an assigned destination), systems of certain embodiments may employ carriages or other containers that are not each fixed to assigned destinations, but rather may be dynamically assigned during operation. In other words, the system assigns carriages or containers to certain destination stations responsive to a wide variety of inputs, such as volume of objects being moved to a single destination, the frequency of sortation of the type of object, or even assigning the next available carriage or container to a destination associated with an acquired object.
The system provides in a specific embodiment an input system that interfaces to the customer's conveyors and containers, stores parcels for feeding into the system, and feeds those parcels into the system at a moderate and controllable rate. In one embodiment, the interface to the customer's process takes the form of a Gaylord dumper, but many other embodiments are possible. In one embodiment, feeding into the system is by an inclined cleated conveyor with overhead baffles. A key to the efficient operation of the system is to feed parcels in at a modest controlled rate. Many options are available, including variations in the conveyor slope and speed, the presence, size and structure of cleats and baffles, and the use of sensors to monitor and control the feed rate.
The system includes in a specific embodiment a primary perception system that monitors the stream of parcels on the primary conveyor. Where possible the primary perception system may identify the parcel to speed or simplify subsequent operations. For example, knowledge of the parcels on the primary conveyor may enable the system to make better choices on whether to pick up a parcel rather than let it pass to the exception bin, which parcels to pick up first, or on how to allocate output bins.
Those skilled in the art will appreciate that numerous modifications and variations may be made to the above disclosed embodiments without departing from the spirit and scope of the present invention.
The present application is a divisional of U.S. patent application Ser. No. 15/833,194, filed Dec. 6, 2017, which claims priority to U.S. Provisional Patent Application Ser. No. 62/430,664 filed Dec. 6, 2016, the disclosure disclosures of which are hereby incorporated by reference in its entirety their entireties.
Number | Name | Date | Kind |
---|---|---|---|
3734286 | Simjian | May 1973 | A |
4136780 | Hunter et al. | Jan 1979 | A |
4253791 | Van Drie | Mar 1981 | A |
4722653 | Williams et al. | Feb 1988 | A |
4759439 | Hartlepp | Jul 1988 | A |
4819784 | Sticht | Apr 1989 | A |
4832553 | Grey | May 1989 | A |
4846335 | Hartlepp | Jul 1989 | A |
4895242 | Michel | Jan 1990 | A |
4936735 | Ryan | Jun 1990 | A |
5190162 | Hartlepp | Mar 1993 | A |
5419457 | Ross et al. | May 1995 | A |
5460271 | Kenny et al. | Oct 1995 | A |
5628408 | Planke et al. | May 1997 | A |
5647473 | Miller | Jul 1997 | A |
5685687 | Frye | Nov 1997 | A |
5794788 | Massen | Aug 1998 | A |
5836436 | Fortenbery | Nov 1998 | A |
5839566 | Bonnet | Nov 1998 | A |
6006946 | Williams et al. | Dec 1999 | A |
6087608 | Schlichter et al. | Jul 2000 | A |
6208908 | Boyd et al. | Mar 2001 | B1 |
6246023 | Kugle | Jun 2001 | B1 |
6323452 | Bonnet | Nov 2001 | B1 |
6390756 | Isaacs et al. | May 2002 | B1 |
6401936 | Isaacs et al. | Jun 2002 | B1 |
6505093 | Thatcher et al. | Jan 2003 | B1 |
6554123 | Bonnet | Apr 2003 | B2 |
6579053 | Grams et al. | Jun 2003 | B1 |
6685031 | Taikizawa | Feb 2004 | B2 |
6688459 | Bonham et al. | Feb 2004 | B1 |
6705528 | Good et al. | Mar 2004 | B2 |
6762382 | Danelski | Jul 2004 | B1 |
6897395 | Shilbashi et al. | May 2005 | B2 |
6946612 | Morikawa | Sep 2005 | B2 |
7728244 | De Leo et al. | Jun 2010 | B2 |
8281553 | Kim | Oct 2012 | B2 |
8662314 | Jones et al. | Mar 2014 | B2 |
8776694 | Rosenwinkel et al. | Jul 2014 | B2 |
8811722 | Perez Cortes et al. | Aug 2014 | B2 |
8972045 | Mountz et al. | Mar 2015 | B1 |
8997438 | Fallas | Apr 2015 | B1 |
9038828 | Enenkel | May 2015 | B2 |
9102336 | Rosenwinkel | Aug 2015 | B2 |
9346083 | Stone | May 2016 | B2 |
9364865 | Kim | Jun 2016 | B2 |
9378607 | Wine et al. | Jun 2016 | B1 |
9481518 | Neiser | Nov 2016 | B2 |
9486926 | Kawano | Nov 2016 | B2 |
9492923 | Wellman et al. | Nov 2016 | B2 |
9520012 | Stiernagle | Dec 2016 | B2 |
9555447 | Lykkegaard et al. | Jan 2017 | B2 |
9688471 | Hellenbrand | Jun 2017 | B2 |
9744669 | Wicks et al. | Aug 2017 | B2 |
9751693 | Battles et al. | Sep 2017 | B1 |
9694977 | Vogedes et al. | Oct 2017 | B2 |
9821464 | Stiernagle et al. | Nov 2017 | B2 |
9878349 | Crest et al. | Jan 2018 | B2 |
9911246 | McBride et al. | Mar 2018 | B1 |
9926138 | Brazeau et al. | Mar 2018 | B1 |
9931673 | Nice et al. | Apr 2018 | B2 |
9937532 | Wagner et al. | Apr 2018 | B2 |
9962743 | Bombaugh et al. | May 2018 | B2 |
9975148 | Zhu | May 2018 | B2 |
9999977 | Wagner et al. | Jun 2018 | B2 |
10007827 | Wagner et al. | Jun 2018 | B2 |
10029865 | McCalib, Jr. et al. | Jul 2018 | B1 |
10058896 | Hicham et al. | Aug 2018 | B2 |
10118300 | Wagner et al. | Nov 2018 | B2 |
10127514 | Napoli | Nov 2018 | B2 |
10137566 | Bastian, II et al. | Nov 2018 | B2 |
10538394 | Wagner | Jan 2020 | B2 |
10875057 | Wagner | Dec 2020 | B2 |
10988069 | Vincent | Apr 2021 | B2 |
10988327 | Layne | Apr 2021 | B1 |
11200390 | Wagner | Dec 2021 | B2 |
20020087231 | Lewis et al. | Jul 2002 | A1 |
20020092801 | Dominguez | Jul 2002 | A1 |
20020134056 | Dimario et al. | Sep 2002 | A1 |
20020157919 | Sherwin | Oct 2002 | A1 |
20020179502 | Cerutti et al. | Dec 2002 | A1 |
20030014376 | DeWitt et al. | Jan 2003 | A1 |
20030029946 | Lieber et al. | Feb 2003 | A1 |
20030034281 | Kumar | Feb 2003 | A1 |
20030038065 | Pippin et al. | Feb 2003 | A1 |
20030042112 | Woerner | Mar 2003 | A1 |
20030075051 | Watanabe et al. | Apr 2003 | A1 |
20030135300 | Lewis | Jul 2003 | A1 |
20040065597 | Hanson | Apr 2004 | A1 |
20040112719 | Gilmore et al. | Jun 2004 | A1 |
20040194428 | Close et al. | Oct 2004 | A1 |
20040261366 | Gillet et al. | Dec 2004 | A1 |
20050002772 | Stone | Jan 2005 | A1 |
20050149226 | Stevens et al. | Jul 2005 | A1 |
20050220600 | Baker et al. | Oct 2005 | A1 |
20050268579 | Natterer | Dec 2005 | A1 |
20060070929 | Fry et al. | Apr 2006 | A1 |
20080046116 | Khan et al. | Feb 2008 | A1 |
20080181485 | Beis et al. | Jul 2008 | A1 |
20080181753 | Bastian et al. | Jul 2008 | A1 |
20080269960 | Kostmann | Oct 2008 | A1 |
20100122942 | Harres et al. | May 2010 | A1 |
20100125361 | Mougin et al. | May 2010 | A1 |
20100276248 | Gut | Nov 2010 | A1 |
20100318216 | Faivre et al. | Dec 2010 | A1 |
20110144798 | Freudelsperger | Jun 2011 | A1 |
20110238207 | Bastian, II et al. | Sep 2011 | A1 |
20110243707 | Dumas et al. | Oct 2011 | A1 |
20110320036 | Freudelsperger | Dec 2011 | A1 |
20120118699 | Buchmann et al. | May 2012 | A1 |
20120177465 | Koholka | Jul 2012 | A1 |
20120219397 | Baker | Aug 2012 | A1 |
20120328397 | Yamashita | Dec 2012 | A1 |
20130051696 | Garrett et al. | Feb 2013 | A1 |
20130110280 | Folk | May 2013 | A1 |
20130202195 | Perez Cortes et al. | Aug 2013 | A1 |
20130232919 | Jaconelli | Sep 2013 | A1 |
20140086709 | Kasai | Mar 2014 | A1 |
20140154036 | Matttern et al. | Jun 2014 | A1 |
20140166549 | Ito et al. | Jun 2014 | A1 |
20140244026 | Neiser | Aug 2014 | A1 |
20140277693 | Naylor | Sep 2014 | A1 |
20140291112 | Lyon et al. | Oct 2014 | A1 |
20140305847 | Kudrus | Oct 2014 | A1 |
20140360924 | Smith et al. | Dec 2014 | A1 |
20140364998 | Neiser et al. | Dec 2014 | A1 |
20150057793 | Kawano | Feb 2015 | A1 |
20150081090 | Dong | Mar 2015 | A1 |
20150114799 | Hansl et al. | Apr 2015 | A1 |
20150306634 | Maeda et al. | Oct 2015 | A1 |
20150346708 | Mattern et al. | Dec 2015 | A1 |
20150352717 | Mundt et al. | Dec 2015 | A1 |
20150352721 | Wicks et al. | Dec 2015 | A1 |
20150375880 | Ford et al. | Dec 2015 | A1 |
20160027093 | Creibier | Jan 2016 | A1 |
20160136816 | Pistorino | May 2016 | A1 |
20160199884 | Lykkegaard et al. | Jul 2016 | A1 |
20160221187 | Bradski et al. | Aug 2016 | A1 |
20160221762 | Schroader | Aug 2016 | A1 |
20160221766 | Schroader et al. | Aug 2016 | A1 |
20160228921 | Doublet et al. | Aug 2016 | A1 |
20160244262 | O'Brien et al. | Aug 2016 | A1 |
20160264366 | Heitplatz | Sep 2016 | A1 |
20160280477 | Pippin | Sep 2016 | A1 |
20160347545 | Lindbo et al. | Dec 2016 | A1 |
20170021499 | Wellman et al. | Jan 2017 | A1 |
20170043953 | Battles et al. | Feb 2017 | A1 |
20170057091 | Wagner et al. | Mar 2017 | A1 |
20170066597 | Hiroi | Mar 2017 | A1 |
20170073175 | Wicks | Mar 2017 | A1 |
20170080566 | Stubbs et al. | Mar 2017 | A1 |
20170080571 | Wagner et al. | Mar 2017 | A1 |
20170080579 | Wagner et al. | Mar 2017 | A1 |
20170087718 | Wagner et al. | Mar 2017 | A1 |
20170087731 | Wagner et al. | Mar 2017 | A1 |
20170106532 | Wellman et al. | Apr 2017 | A1 |
20170107056 | Kadaba | Apr 2017 | A1 |
20170120455 | Wagner et al. | May 2017 | A1 |
20170121113 | Wagner et al. | May 2017 | A1 |
20170136632 | Wagner et al. | May 2017 | A1 |
20170157648 | Wagner et al. | Jun 2017 | A1 |
20170157649 | Wagner et al. | Jun 2017 | A1 |
20170173638 | Wagner et al. | Jun 2017 | A1 |
20170197316 | Wagner et al. | Jul 2017 | A1 |
20170225330 | Wagner et al. | Aug 2017 | A1 |
20170305694 | McMurrough | Oct 2017 | A1 |
20170322561 | Stiernagle | Nov 2017 | A1 |
20170349385 | Moroni et al. | Dec 2017 | A1 |
20180085788 | Engel et al. | Mar 2018 | A1 |
20180127219 | Wagner et al. | May 2018 | A1 |
20180134501 | Ge et al. | May 2018 | A1 |
20180148272 | Wagner et al. | May 2018 | A1 |
20180194574 | Wagner et al. | Jul 2018 | A1 |
20180244473 | Mathi et al. | Aug 2018 | A1 |
20180265311 | Wagner et al. | Sep 2018 | A1 |
20180273295 | Wagner et al. | Sep 2018 | A1 |
20180273296 | Wagner et al. | Sep 2018 | A1 |
20180273297 | Wagner et al. | Sep 2018 | A1 |
20180273298 | Wagner et al. | Sep 2018 | A1 |
20180282065 | Wagner et al. | Oct 2018 | A1 |
20180282066 | Wagner et al. | Oct 2018 | A1 |
20180327198 | Wagner et al. | Nov 2018 | A1 |
20180330134 | Wagner et al. | Nov 2018 | A1 |
20180333749 | Wagner et al. | Nov 2018 | A1 |
20190022702 | Vegh et al. | Jan 2019 | A1 |
20190100368 | Zagar et al. | Apr 2019 | A1 |
Number | Date | Country |
---|---|---|
2006204622 | Mar 2007 | AU |
102390701 | Mar 2012 | CN |
104743367 | Jul 2015 | CN |
204714016 | Oct 2015 | CN |
105905019 | Aug 2016 | CN |
205500186 | Aug 2016 | CN |
108602630 | Sep 2018 | CN |
19510392 | Sep 1996 | DE |
102004001181 | Aug 2005 | DE |
102005061309 | Jul 2007 | DE |
102006057658 | Jun 2008 | DE |
102007023909 | Nov 2008 | DE |
102007038834 | Feb 2009 | DE |
102010002317 | Aug 2011 | DE |
102010033115 | Feb 2012 | DE |
102012003160 | Sep 2012 | DE |
102011083095 | Mar 2013 | DE |
102012102333 | Sep 2013 | DE |
102014111396 | Feb 2016 | DE |
1695927 | Aug 2006 | EP |
1995192 | Nov 2008 | EP |
2256703 | Dec 2010 | EP |
2511653 | Oct 2012 | EP |
2745982 | Jun 2014 | EP |
3006379 | Apr 2016 | EP |
3112295 | Jan 2017 | EP |
2832654 | May 2003 | FR |
2084531 | Apr 1982 | GB |
S54131278 | Oct 1979 | JP |
563310406 | Dec 1988 | JP |
H0395001 | Apr 1991 | JP |
H0776404 | Mar 1995 | JP |
2002028577 | Jan 2002 | JP |
2002175543 | Jun 2002 | JP |
2007182286 | Jul 2007 | JP |
2008080300 | Apr 2008 | JP |
101413393 | Jun 2014 | KR |
3074201 | Sep 2003 | WO |
2007009136 | Jan 2007 | WO |
2008091733 | Jul 2008 | WO |
2010017872 | Feb 2010 | WO |
2010034044 | Apr 2010 | WO |
2010099873 | Sep 2010 | WO |
2011038442 | Apr 2011 | WO |
2012024714 | Mar 2012 | WO |
2013178431 | Dec 2013 | WO |
2014111483 | Jul 2014 | WO |
2014166650 | Oct 2014 | WO |
2015118171 | Aug 2015 | WO |
2015162390 | Oct 2015 | WO |
2016067163 | May 2016 | WO |
2016100235 | Jun 2016 | WO |
2017036812 | Mar 2017 | WO |
2017044747 | Mar 2017 | WO |
2018176033 | Mar 2018 | WO |
Entry |
---|
English Translation of CN 105905019B; Inventor: Ma et al.; Pub. Date: Jun. 2018. |
International Search Report and Written Opinion issued by the International Searching Authority dated Feb. 28, 2018 in related International Application No. PCT/US2017/064903, 16 pages. |
International Preliminary Report on Patentability issued by the International Bureau of WIPO dated Jun. 11, 2019 in related International Application No. PCT/US2017/064903, 12 pages. |
Communication pursuant to Rules 161(1) and 162 EPC issued by the European Patent Office dated Jul. 12, 2019, in related European Patent Application No. 17822107.3, 3 pages. |
Non-Final Office Action issued by the U.S. Patent and Trademark Office dated Feb. 1, 2019 in related U.S. Appl. No. 15/833,194, 10 pages. |
First Office Action, and its English Translation, issued by the National Intellectual Property Administration, P.R.C. dated Jun. 3, 2020 in related Chinese Patent Application No. 201780075745.6, 8 pages. |
Non-Final Office Action issued by the U.S. Patent and Trademark Office dated Mar. 12, 2020 in related U.S. Appl. No. 15/833,194, 11 pages. |
First Examiner's Report issued by Innovation, Science and Economic Development Canada in related Canadian Patent Application No. 3,045,522 dated Jul. 3, 2020, 5 pages. |
Second Office Action, and its English Translation, issued by the National Intellectual Property Administration, P.R.C. in related Chinese Patent Application No. 201780075745.6 dated Feb. 19, 2021, 11 pages. |
Third Office Action, and its English Translation, issued by the National Intellectual Property Administration, P.R.C. in related Chinese Patent Application No. 201780075745.6 dated Sep. 16, 2021, 20 pages. |
Examiner's Report issued by Innovation, Science and Economic Development Canada in related Canadian Patent Application No. 3,045,522 dated Feb. 4, 2021, 3 pages. |
Non-Final Office Action issued by the United States Patent and Trademark Office in related U.S. Appl. No. 17/086,645 dated Feb. 4, 2022, 7 pages. |
Decision on Rejection, and its English Translation, issued by the National Intellectual Property Administration, P.R.C. in related Chinese Patent Application No. 201780075745.6 dated Mar. 3, 2022, 22 pages. |
Examiner's Report issued by Innovation, Science and Economic Development Canada (Canadian Intellectual Property Office) in related Canadian Patent Application No. 3,045,522 dated May 27, 2022, 4 pages. |
Number | Date | Country | |
---|---|---|---|
20210053092 A1 | Feb 2021 | US |
Number | Date | Country | |
---|---|---|---|
62430664 | Dec 2016 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 15833194 | Dec 2017 | US |
Child | 17086648 | US |