This disclosure relates generally to the field of data processing systems and more particularly to robotic process automation systems.
Robotic process automation (RPA) is the application of technology that allows workers in an organization to configure computer software, known as a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. The software robots in conventional RPA systems employ the software robots to interpret the user interface of third-party applications and to execute steps identically to a human user. For example, many tasks within organizations require individuals to perform the same repetitive tasks, such as entering data from invoices into an enterprise accounts payable application or entering data from a loan application into a loan processing system. RPA permits the automation of such application level repetitive tasks via software robots that are coded to repeatedly and accurately perform the repetitive task.
The software robots in conventional RPA systems execute on devices, physical or virtual, that are separate from an RPA server and which contain software to permit creation and/or execution of the software robot. The software robots can be of significant size (e.g., several megabytes to over 100 megabytes) and the size of the software robots tends to grow over time as the types of tasks that the software robots are programmed to perform increases in complexity. The software robots are typically provided by the RPA server to a separate device. In a larger deployment, where bots are regularly being requested by devices, the software robot download can consume significant server processing power and network bandwidth. In a RPA system available from Automation Anywhere, Inc. under the trade name Enterprise A2019, a larger number of virtual devices can be deployed to increase scalability of the RPA system. This further increases the above noted load on the RPA server and the network when providing software robot(s) to requesting devices.
Computerized RPA methods and systems that improve downloads of bots and recorders are disclosed herein. In one aspect a robotic process automation system includes data storage upon which are stored a plurality of sets of task processing instructions where each set of task processing instructions is operable to interact at a user level with one or more designated user level application programs. The data storage also contains a plurality of work items, where each work item is stored for subsequent processing by executing a corresponding set of task processing instructions. A server processor is operatively coupled to the data storage and is configured to execute instructions that when executed cause the server processor to respond to a request to perform a first automation task to process a work item from the plurality of work items, on a first computing device that is separate and independent from the server processor. The server processor receives a request from the first computing device to download the first automation task and queries a distribution information file to identify one or more other computing devices that have a copy of the first automation task. The server process provides to the first computing device, an identifier for each of one or more other computing devices that has a copy of the first automation task. If the distribution information file does not contain an identification of any other device that has a copy of the first automation task, then the server processor causes the first automation task to be retrieved from the data storage and to be provided to the first computing device.
The foregoing reduces the load on the server processor by obtaining the automation task from another node within the system, which improves overall system performance. As the automation tasks become stored on multiple nodes the overall availability of the automation tasks is increasingly distributed and hence more available within the RPA system. Even if there is a corrupt segment of the automation task package, only that part is re-downloaded rather than having to download the complete package.
Moreover, even if the server processor is temporarily unavailable due to network down time or the hosting service down time, once the automation task package is available on at least one node, it can be streamed to other nodes using this approach. This reduces dependency on server processor to a large extent.
In other embodiments, distribution of an RPA runtime required by devices may also be provided in a distributed fashion to reduce load on the RPA server. Certain embodiments support multi-tenant environments with each environment operating independently of each other and receiving bots, recorders and RPA runtimes from only devices that are in the same environment.
These and additional aspects related to the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the description or may be learned by practice of the invention. Aspects of the invention may be realized and attained by means of the elements and combinations of various elements and aspects particularly pointed out in the following detailed description and the appended claims.
It is to be understood that both the foregoing and the following descriptions are exemplary and explanatory only and are not intended to limit the claimed invention or application thereof in any manner whatsoever.
The accompanying drawings, which are incorporated in and constitute a part of this specification exemplify the embodiments of the present invention and, together with the description, serve to explain and illustrate principles of the inventive techniques disclosed herein. Specifically:
In the following detailed description, reference will be made to the accompanying drawings, in which identical functional elements are designated with like numerals. Elements designated with reference numbers ending in a suffix such as 0.1, 0.2, 0.3 are referred to collectively by employing the main reference number without the suffix. For example, 100 refers to topics 100.1, 100.2, 100.3 generally and collectively. The aforementioned accompanying drawings show by way of illustration, and not by way of limitation, specific embodiments and implementations consistent with principles of the present invention. These implementations are described in sufficient detail to enable those skilled in the art to practice the invention and it is to be understood that other implementations may be utilized and that structural changes and/or substitutions of various elements may be made without departing from the scope and spirit of present invention. The following detailed description is, therefore, not to be construed in a limited sense.
In
Some or all of the bots 104 may in certain embodiments be located remotely from the control room 108. Moreover, the devices 110 and 111 may also be located remotely from the control room 108. The bots 104 and the tasks 106 are shown in separate containers for purposes of illustration but they may be stored in separate or the same device(s), or across multiple devices. The control room 108 performs user management functions, source control of the bots 104, along with providing a dashboard that provides analytics and results of the bots 104, performs license management of software required by the bots 104 and manages overall execution and management of scripts, clients, roles, credentials, and security etc. The major functions performed by the control room 108 include: (i) a dashboard that provides a summary of registered/active users, tasks status, repository details, number of clients connected, number of scripts passed or failed recently, tasks that are scheduled to be executed and those that are in progress; (ii) user/role management—permits creation of different roles, such as bot creator, bot runner, admin, and custom roles, and activation, deactivation and modification of roles; (iii) repository management—to manage all scripts, tasks, workflows and reports etc.; (iv) operations management—permits checking status of tasks in progress and history of all tasks, and permits the administrator to stop/start execution of bots currently executing; (v) audit trail—logs creation of all actions performed in the control room; (vi) task scheduler—permits scheduling tasks which need to be executed on different clients at any particular time; (vii) credential management—permits password management; and (viii) security: management—permits rights management for all user roles. The control room 108 is shown generally for simplicity of explanation. Multiple instances of the control room 108 may be employed where large numbers of bots are deployed to provide for scalability of the RPA system 10.
The control room 108 provides to the client device 110, software code to implement a node manager 114 that executes on the client device 110 and which provides to a user 112 a visual interface via a browser (not shown) to view progress of and to control execution of the automation task. It should be noted here that the node manager 114 in one embodiment is provided to the client device 110 on demand, when required by the client device 110 to execute a desired automation task. In another embodiment, the node manager 114 may remain on the client device 110 after completion of the requested automation task to avoid the need to download it again. In another embodiment, the node manager 114 may be deleted from the client device 110 after completion of the requested automation task. The node manager 114 also maintains a connection to the control room 108 to inform the control room 108 that device 110 is available for service by the control room 108, irrespective of whether a live user session exists. For simplicity of illustration, the node manager 114 is shown only on device 110.1 but should be understood to be on each device 110.
The control room 108 initiates on the client device 110, a user session to perform the automation task. The control room 108 retrieves the set of task processing instructions 104 that correspond to the work item 106. The task processing instructions 104 that correspond to the work item 106 execute under control of the user session, on the device 110. The node manager 114 provides update data indicative of status of processing of the work item to the control room 108. The control room 108 terminates the user session upon completion of processing of the work item 106.
The hots 104 execute on a player (not shown), via a computing device, to perform the functions encoded by the bot. Additional aspects of operation of bots may be found in the following pending patent application, which refers to bots as automation profiles, System and Method for Compliance Based Automation, filed in the U.S. Patent Office on Jan. 6, 2016, and assigned application Ser. No. 14/988,877, which is hereby incorporated by reference in its entirety. The bot player executes, or plays back, the sequence of instructions encoded in a bot. The sequence of instructions is captured by way of a recorder 105 when a human performs those actions, or alternatively the instructions are explicitly coded into the bot. These instructions enable the bot player, to perform the same actions as a human would do in their absence. The instructions are composed of a command (action) followed by set of parameters, for example: Open Browser is a command, and a URL would be the parameter for it to launch the site.
The user 112 interacts with node manager 114, typically via a conventional browser which employs the node manager 114 to communicate with the control room 108. When for the first time 112 user logs from client device 110 onto the control room 108, they are prompted to download and install the node manager 114 on the device 110, if one is not already present. The node manager 114 establishes a web socket connection to a user session manager (not shown), deployed by the control room 108 that lets the user 112 subsequently create, edit and deploy the bots 104.
The node manager 114 which is provided to the device 110 by the control room 108, in certain embodiments provides a number of functions. First is a discovery service that establishes and maintains a connection to the control room 108 and acts as a resource to the control room 108 for the device 110. Second, the node manager 114 provides an autologin service that provides a vehicle to allow the control room 108 to login or to create a user session by launching a user session manager which works with the control room 108 to serve control room requests. Third, the node manager 212 provides a logging function to provide a single, centralized point for streaming of all logging data back to the control room 108, via the health service, which stores the received log data to a data log.
As seen in
The bots 104 and recorder 105 may be periodically updated from time to time and any device containing a bot 104 or a recorder 105 will need to be updated with a new version when a new version is generated and when the bot or recorder is executed on the device. The RPA system 10 may interact with a large number of devices 110 at any given time, and such devices will download new versions of a bot 104 or recorder 105 when such bots or recorders are required to be executed. These downloads consume bandwidth and processor resources of the control room 108 which can impact performance of the control room 108. The bots 104 can be substantial in size, typically ranging from 2 Mbytes to about 100 Mbytes, and maximum and average size of the bots 104 can be expected to grow as the tasks performed by such bots grows in complexity. The recorder 105 similarly can consume approximately 100 Mbytes as its capability continues to grow.
The RPA system 10 shown in
Control room 108 implements a tracker service 120 to manage the download of bots 104 and recorder 105. Operation of node manager 114 in connection with tracker service 120 may be seen in
Once a first seeder node has been established, the recorder 105 or bots 114 stored in the download cache of the first seeder node may be obtained from the seeder first node. A subsequent node that obtains a recorder 105 or a bot 114 from the seeder node becomes another seeder node with respect to the recorder 105 or bot 114 contained in its download cache. The second, third, etc. seeder node(s) become another source (in addition to the first seeder node) of the recorder 105 and bot(s) 114 in their download cache 118.
In one embodiment, download of the recorder 105 and bots 104 may be performed in accordance with the BitTorrent file sharing protocol, as described for example in The BitTorrent Protocol Specification by B. Cohen, January 2008 available at bittorrent.org. As a user 112 employs recorder 105 to create a new bot 104, the control room 108 stores the bot 104 and creates for the bot 104 a distribution info file 124, details of which are shown in
The tracker 120 may also in one embodiment operate as described by Cohen, supra. As will be appreciated by those skilled in the art in view of the present disclosure, the BitTorrent protocol allows a node seeking to download a file to join a “swarm” of hosts to upload to/download from concurrently from each of the other nodes that may have the file or a portion of the file. The BitTorrent protocol provides an alternative to conventional single source, multiple mirror sources techniques for distributing data, and can work effectively over networks with lower bandwidth. The file being distributed is divided into segments called pieces. As each peer receives a new piece of the file, it becomes a source (of that piece) for other peers, relieving the original seeder node from having to send that piece to every computer or user wishing to receive a copy. Each piece is protected by a cryptographic hash contained in the distribution info file as described above. This ensures that any modification of the piece can be reliably detected, and thus prevents both accidental and malicious modifications of any of the pieces received at other nodes. If a node starts with an authentic copy of the torrent descriptor, it can verify the authenticity of the entire file it receives. The torrent descriptor contains a cryptographic has that protects each piece of the file as it is distributed. In a typical BitTorrent download, pieces of a file are downloaded non-sequentially, and are rearranged into the correct order by the receiving BitTorrent client, which monitors which pieces it needs, and which pieces it has and can upload to other peers. Pieces are of the same size throughout a single download (for example a 10 MB file may be transmitted as ten 1 MB pieces or as forty 256 KB pieces). This approach permits the download of any file to be halted at any time and to be resumed at a later time, without the loss of previously downloaded information. This makes BitTorrent particularly useful in the transfer of larger files. In certain embodiments, the receiving node may seek out readily available pieces and download them immediately, rather than halting the download and waiting for the next (and possibly unavailable) piece in line, which typically reduces the overall time of the download.
Each of the devices 110 with which the RPA system 10 operates is registered with the control room 108 as a seeder node for a particular bot 104 and/or recorder 105, which makes each of the devices 110 a trusted node, thereby avoiding the security issues often faced in BitTorrent implementations, as described by Cohen, supra. In addition to the foregoing information maintained by the control room 108, the control room 108 also maintains for each device 110, a device ID, an IP address for the device and the file name(s) of the files for which the device is a seeder node along with a file hash for each file, and a size of each stored file. In contrast to standard BitTorrent implementations, in the embodiments described herein, the network identifier specified in the distribution info file 124 is fixed to identify only the control room 108. This restricts the nodes from downloading bots, recorders and other RPA required executables from only the control room 108 and devices authorized by the control room 108.
In one embodiment, if a seeder node has an older version of a bot 104, that bot remains on the seeder node as long as it is executed. In such an embodiment, when it is executing a bot, the node manager 114 will check for the latest version of the bot in question on the control room 108. If the version differs, the node manager 114 will obtain a list of seeder nodes for the updated version of the bot 104 in question from the tracker service 120 and download the updated bot 104 in the manner shown in
In another embodiment, the control room 108 may be configured to support an RPA system 10 in multiple organizations. Such an arrangement is referred to herein as a multi-tenant environment. In such an environment, the control room 108 may support an RPA system at for example Company A and an RPA system at for example Company B and another RPA system at for example Company C. This is shown in
The embodiments herein can be implemented in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing system on a target real or virtual processor. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The program modules may be obtained from another computer system, such as via the Internet, by downloading the program modules from the other computer system for execution on one or more different computer systems. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Computer-executable instructions for program modules may be executed within a local or distributed computing system. The computer-executable instructions, which may include data, instructions, and configuration parameters, may be provided via an article of manufacture including a computer readable medium, which provides content that represents instructions that can be executed. A computer readable medium may also include a storage or database from which content can be downloaded. A computer readable medium may also include a device or product having content stored thereon at a time of sale or delivery. Thus, delivering a device with stored content, or offering content for download over a communication medium may be understood as providing an article of manufacture with such content described herein.
Computing system 900 may have additional features such as for example, storage 910, one or more input devices 914, one or more output devices 912, and one or more communication connections 916. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing system 900. Typically, operating system software (not shown) provides an operating system for other software executing in the computing system 900, and coordinates activities of the components of the computing system 900.
The tangible storage 910 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium which can be used to store information in a non-transitory way, and which can be accessed within the computing system 900. The storage 910 stores instructions for the software implementing one or more innovations described herein.
The input device(s) 914 may be a touch input device such as a keyboard, mouse, pen, or trackball, a voice input device, a scanning device, or another device that provides input to the computing system 900. For video encoding, the input device(s) 914 may be a camera, video card, TV tuner card, or similar device that accepts video input in analog or digital form, or a CD-ROM or CD-RW that reads video samples into the computing system 900. The output device(s) 912 may be a display, printer, speaker, CD-writer, or another device that provides output from the computing system 900.
The communication connection(s) 916 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media can use an electrical, optical, RF, or other carrier.
The terms “system” and “computing device” are used interchangeably herein. Unless the context clearly indicates otherwise, neither term implies any limitation on a type of computing system or computing device. In general, a computing system or computing device can be local or distributed and can include any combination of special-purpose hardware and/or general-purpose hardware with software implementing the functionality described herein.
While the invention has been described in connection with a certain embodiment, it is not intended to limit the scope of the invention to the particular form set forth, but on the contrary, it is intended to cover such alternatives, modifications, and equivalents as may be within the spirit and scope of the invention as defined by the appended claims.
This application is a continuation of U.S. patent application Ser. No. 16/779,199, entitled “ROBOTIC PROCESS AUTOMATION SYSTEM WITH DISTRIBUTED DOWNLOAD,” and filed on Jan. 31, 2020, and which is hereby incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
5949999 | Song et al. | Sep 1999 | A |
5983001 | Boughner et al. | Nov 1999 | A |
6133917 | Feigner et al. | Oct 2000 | A |
6389592 | Ayres et al. | May 2002 | B1 |
6427234 | Chambers et al. | Jul 2002 | B1 |
6473794 | Guheen et al. | Oct 2002 | B1 |
6496979 | Chen et al. | Dec 2002 | B1 |
6640244 | Bowman-Amuah | Oct 2003 | B1 |
6704873 | Underwood | Mar 2004 | B1 |
6898764 | Kemp | May 2005 | B2 |
6954747 | Wang et al. | Oct 2005 | B1 |
6957186 | Guheen et al. | Oct 2005 | B1 |
7091898 | Arling et al. | Aug 2006 | B2 |
7246128 | Jordahl | Jul 2007 | B2 |
7398469 | Kisamore et al. | Jul 2008 | B2 |
7441007 | Kirkpatrick et al. | Oct 2008 | B1 |
7533096 | Rice et al. | May 2009 | B2 |
7568109 | Powell et al. | Jul 2009 | B2 |
7571427 | Wang et al. | Aug 2009 | B2 |
7765525 | Davidson et al. | Jul 2010 | B1 |
7805317 | Khan et al. | Sep 2010 | B2 |
7805710 | North | Sep 2010 | B2 |
7810070 | Nasuti et al. | Oct 2010 | B2 |
7846023 | Evans et al. | Dec 2010 | B2 |
8028269 | Bhatia et al. | Sep 2011 | B2 |
8056092 | Allen et al. | Nov 2011 | B2 |
8095910 | Nathan et al. | Jan 2012 | B2 |
8132156 | Malcolm | Mar 2012 | B2 |
8209738 | Nicol et al. | Jun 2012 | B2 |
8234622 | Meijer et al. | Jul 2012 | B2 |
8245215 | Extra | Aug 2012 | B2 |
8352464 | Fotev | Jan 2013 | B2 |
8396890 | Lim | Mar 2013 | B2 |
8438558 | Adams | May 2013 | B1 |
8443291 | Ku et al. | May 2013 | B2 |
8464240 | Fritsch et al. | Jun 2013 | B2 |
8498473 | Chong et al. | Jul 2013 | B2 |
8504803 | Shukla | Aug 2013 | B2 |
8631458 | Banerjee | Jan 2014 | B1 |
8682083 | Kumar et al. | Mar 2014 | B2 |
8713003 | Fotev | Apr 2014 | B2 |
8769482 | Batey et al. | Jul 2014 | B2 |
8819241 | Washburn | Aug 2014 | B1 |
8832048 | Lim | Sep 2014 | B2 |
8874685 | Hollis et al. | Oct 2014 | B1 |
8943493 | Schneider | Jan 2015 | B2 |
8965905 | Ashmore et al. | Feb 2015 | B2 |
9104294 | Forstall et al. | Aug 2015 | B2 |
9213625 | Schrage | Dec 2015 | B1 |
9278284 | Ruppert et al. | Mar 2016 | B2 |
9444844 | Edery et al. | Sep 2016 | B2 |
9462042 | Shukla et al. | Oct 2016 | B2 |
9571332 | Subramaniam et al. | Feb 2017 | B2 |
9621584 | Schmidt et al. | Apr 2017 | B1 |
9946233 | Brun et al. | Apr 2018 | B2 |
10733329 | Ragupathy | Aug 2020 | B1 |
10958689 | Ganesan | Mar 2021 | B1 |
11086614 | Jain et al. | Aug 2021 | B1 |
20030033590 | Leherbauer | Feb 2003 | A1 |
20030101245 | Srinivasan et al. | May 2003 | A1 |
20030159089 | DiJoseph | Aug 2003 | A1 |
20040083472 | Rao et al. | Apr 2004 | A1 |
20040172526 | Fann et al. | Sep 2004 | A1 |
20040210885 | Wang et al. | Oct 2004 | A1 |
20040243994 | Nasu | Dec 2004 | A1 |
20050188357 | Derks et al. | Aug 2005 | A1 |
20050204343 | Kisamore et al. | Sep 2005 | A1 |
20050257214 | Moshir et al. | Nov 2005 | A1 |
20060095276 | Axelrod et al. | May 2006 | A1 |
20060150188 | Roman et al. | Jul 2006 | A1 |
20070101291 | Forstall et al. | May 2007 | A1 |
20070112574 | Greene | May 2007 | A1 |
20070156677 | Szabo | Jul 2007 | A1 |
20080005086 | Moore | Jan 2008 | A1 |
20080027769 | Eder | Jan 2008 | A1 |
20080028392 | Chen et al. | Jan 2008 | A1 |
20080209392 | Able et al. | Aug 2008 | A1 |
20080222454 | Kelso | Sep 2008 | A1 |
20080263024 | Landschaft et al. | Oct 2008 | A1 |
20090037509 | Parekh et al. | Feb 2009 | A1 |
20090103769 | Milov et al. | Apr 2009 | A1 |
20090172814 | Khosravi et al. | Jul 2009 | A1 |
20090199160 | Vaitheeswaran et al. | Aug 2009 | A1 |
20090217309 | Grechanik et al. | Aug 2009 | A1 |
20090249297 | Doshi et al. | Oct 2009 | A1 |
20090313229 | Fellenstein et al. | Dec 2009 | A1 |
20090320002 | Peri-Glass et al. | Dec 2009 | A1 |
20100023602 | Martone | Jan 2010 | A1 |
20100023933 | Bryant et al. | Jan 2010 | A1 |
20100100605 | Allen et al. | Apr 2010 | A1 |
20100106671 | Li et al. | Apr 2010 | A1 |
20100138015 | Colombo et al. | Jun 2010 | A1 |
20100235433 | Ansari et al. | Sep 2010 | A1 |
20110022578 | Fotev | Jan 2011 | A1 |
20110145807 | Molinie et al. | Jun 2011 | A1 |
20110197121 | Kletter | Aug 2011 | A1 |
20110276568 | Fotev | Nov 2011 | A1 |
20110276946 | Pletter | Nov 2011 | A1 |
20110302570 | Kurimilla et al. | Dec 2011 | A1 |
20120042281 | Green | Feb 2012 | A1 |
20120124062 | Macbeth et al. | May 2012 | A1 |
20120330940 | Caire et al. | Dec 2012 | A1 |
20130173648 | Tan et al. | Jul 2013 | A1 |
20130290318 | Shapira et al. | Oct 2013 | A1 |
20140181705 | Hey et al. | Jun 2014 | A1 |
20150082280 | Betak et al. | Mar 2015 | A1 |
20150347284 | Hey et al. | Dec 2015 | A1 |
20160019049 | Kakhandiki et al. | Jan 2016 | A1 |
20160078368 | Kakhandiki et al. | Mar 2016 | A1 |
20190155225 | Kothandaraman | May 2019 | A1 |
Entry |
---|
Torkhani et al., Intelligent Framework for Business Process Automation and Re-engineering, 6 pages (Year: 2018). |
Al Sallami, Load Balancing in Green Cloud Computation, Proceedings of the World Congress on Engineering 2013 vol II, WCE 2013, 2013, pp. 1-5 (Year: 2013). |
B. P. Kasper “Remote: A Means of Remotely Controlling and Storing Data from a HAL Quadrupole Gas Analyzer Jsing an IBM-PC Compatible Computer”, Nov. 15, 1995, Space and Environment Technology Center. |
Bergen et al., RPC automation: making legacy code relevant, May 2013, 6 pages. |
Hu et al., Automating GUI testing for Android applications, May 2011, 7 pages. |
Konstantinou et al., An architecture for virtual solution composition and deployment in infrastructure clouds, 9 pages (Year: 2009). |
Nyulas et al., An Ontology-Driven Framework for Deploying JADE Agent Systems, 5 pages (Year: 2008). |
Tom Yeh, Tsung-Hsiang Chang, and Robert C. Miller, Sikuli: Using GUI Screenshots for Search and Automation, Oct. 4-7, 2009, 10 pages. |
Yu et al., Deploying and managing Web services: issues, solutions, and directions, 36 pages (Year: 2008). |
Zhifang et al., Test automation on mobile device, May 2010, 7 pages. |
Notice of Allowance for U.S. Appl. No. 16/779,199, dated Apr. 14, 2021. |
Roggow et al., “Autonomous Identification of Local Agents in Multi-Agent Robotic Swarms”, 4 pages (Year 2016). |
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20210365260 A1 | Nov 2021 | US |
Number | Date | Country | |
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Parent | 16779199 | Jan 2020 | US |
Child | 17392240 | US |