Patent Grant
10932872
The present disclosure relates to various surgical systems. In the Digital and Information Age, medical systems and facilities are often slower to implement systems or procedures utilizing newer and improved technologies due to patient safety and a general desire for maintaining traditional practices. However, often times medical systems and facilities may lack communication and shared knowledge with other neighboring or similarly situated facilities as a result. To improve patient practices, it would be desirable to find ways to help interconnect medical systems and facilities better.
In one general aspect, a cloud-based analytics medical system is provided. The cloud-based analytics medical system comprises: at least one processor; at least one memory communicatively coupled to the at least one processor; an input/output interface configured for accessing data from a plurality of medical hub communication devices, each communicatively coupled to at least one surgical instrument; and a database residing in at least one memory and configured to store the data. The at least one memory stores instructions executable by the at least one processor to: aggregate patient outcome data from the plurality of medical hubs. The patient outcome data comprises: data pertaining to steps performed and corresponding timings for each step in patient procedures; data pertaining to an outcome of each patient procedure performed; and data pertaining to medical resources used in the patient procedures. The patient outcome data also comprises, for each data item pertaining to the medical resource, location data describing which medical facility the medical resource was allocated to; and, for each data item pertaining to the outcome of the patient procedure, data pertaining to an indication of whether the outcome was a success or failure. The at least one memory further stores instructions executable by the at least one processor to: aggregate medical resource acquisition data from the plurality of medical hubs; determine a correlation between positive outcomes from the patient outcome data and the resource acquisition data; generate a medical recommendation to change a medical resource acquisition practice based on the correlation; and cause display of the medical recommendation to a plurality of medical hubs located at different medical facilities.
In another general aspect, a computer-readable medium is provided. The computer-readable medium is non-transitory and stores computer-readable instructions executable by at least one processor of a cloud-based analytics system to aggregate patient outcome data from the plurality of medical hubs. The patient outcome data comprises: data pertaining to steps performed and corresponding timings for each step in patient procedures; data pertaining to an outcome of each patient procedure performed; data pertaining to medical resources used in the patient procedures; and, for each data item pertaining to the medical resource: location data describing which medical facility said medical resource was allocated to; and, for each data item pertaining to the outcome of the patient procedure: data pertaining to an indication of whether the outcome was a success or failure. The executable instructions also: aggregate medical resource acquisition data from the plurality of medical hubs; determine a correlation between positive outcomes from the patient outcome data and the resource acquisition data; generate a medical recommendation to change a medical resource acquisition practice based on the correlation; and cause display of the medical recommendation to a plurality of medical hubs located at different medical facilities.
In yet another general aspect, another cloud-based analytics medical system is provided. The cloud based analytics medical system comprises: at least one processor; at least one memory communicatively coupled to at least one processor; an input/output interface configured for accessing data from a plurality of medical hub communication devices, each communicatively coupled to at least one surgical instrument; and a database residing in at least one memory and configured to store the data. The at least one memory storing instructions executable by the at least one processor to: aggregate medical instrument data from the plurality of medical hubs. The medical instrument data comprises: data pertaining to physical and performance parameters of medical devices; for each datum pertaining to the medical device: usage data pertaining to medical procedures that utilized the medical device; and, for each medical procedure: an outcome of the medical procedure; and a status of the condition of the medical device during the medical procedure. The at least one memory storing instructions executable by the at least one processor to: determine a correlation between outcomes of the medical procedures and the statuses of the conditions of the medical devices utilized in the respective medical procedures; access live medical procedure data for a live medical procedure, the live medical procedure data comprising a description of the medical devices present in an operating room that is performing the live medical procedure; determine an irregularity in the description of the medical devices present in the live medical procedure, based on the determined correlation between the outcomes and the medical devices utilized; and provide an alert to a medical communication hub that is utilized in the operating room of the live medical procedure.
The features of various aspects are set forth with particularity in the appended claims. The various aspects, however, both as to organization and methods of operation, together with further objects and advantages thereof, may best be understood by reference to the following description, taken in conjunction with the accompanying drawings as follows.
Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Mar. 28, 2018, each of which is herein incorporated by reference in its entirety:
Applicant of the present application owns the following U.S. Patent Applications, filed on Mar. 29, 2018, each of which is herein incorporated by reference in its entirety:
Applicant of the present application owns the following U.S. Patent Applications, filed on Mar. 29, 2018, each of which is herein incorporated by reference in its entirety:
Applicant of the present application owns the following U.S. Patent Applications, filed on Mar. 29, 2018, each of which is herein incorporated by reference in its entirety:
Before explaining various aspects of surgical devices and generators in detail, it should be noted that the illustrative examples are not limited in application or use to the details of construction and arrangement of parts illustrated in the accompanying drawings and description. The illustrative examples may be implemented or incorporated in other aspects, variations and modifications, and may be practiced or carried out in various ways. Further, unless otherwise indicated, the terms and expressions employed herein have been chosen for the purpose of describing the illustrative examples for the convenience of the reader and are not for the purpose of limitation thereof. Also, it will be appreciated that one or more of the following-described aspects, expressions of aspects, and/or examples, can be combined with any one or more of the other following-described aspects, expressions of aspects and/or examples.
Referring to
Other types of robotic systems can be readily adapted for use with the surgical system 102. Various examples of robotic systems and surgical tools that are suitable for use with the present disclosure are described in U.S. Provisional Patent Application Ser. No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety.
Various examples of cloud-based analytics that are performed by the cloud 104, and are suitable for use with the present disclosure, are described in U.S. Provisional Patent Application Ser. No. 62/611,340, titled CLOUD-BASED MEDICAL ANALYTICS, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety.
In various aspects, the imaging device 124 includes at least one image sensor and one or more optical components. Suitable image sensors include, but are not limited to, Charge-Coupled Device (CCD) sensors and Complementary Metal-Oxide Semiconductor (CMOS) sensors.
The optical components of the imaging device 124 may include one or more illumination sources and/or one or more lenses. The one or more illumination sources may be directed to illuminate portions of the surgical field. The one or more image sensors may receive light reflected or refracted from the surgical field, including light reflected or refracted from tissue and/or surgical instruments.
The one or more illumination sources may be configured to radiate electromagnetic energy in the visible spectrum as well as the invisible spectrum. The visible spectrum, sometimes referred to as the optical spectrum or luminous spectrum, is that portion of the electromagnetic spectrum that is visible to (i.e., can be detected by) the human eye and may be referred to as visible light or simply light. A typical human eye will respond to wavelengths in air that are from about 380 nm to about 750 nm.
The invisible spectrum (i.e., the non-luminous spectrum) is that portion of the electromagnetic spectrum that lies below and above the visible spectrum (i.e., wavelengths below about 380 nm and above about 750 nm). The invisible spectrum is not detectable by the human eye. Wavelengths greater than about 750 nm are longer than the red visible spectrum, and they become invisible infrared (IR), microwave, and radio electromagnetic radiation. Wavelengths less than about 380 nm are shorter than the violet spectrum, and they become invisible ultraviolet, x-ray, and gamma ray electromagnetic radiation.
In various aspects, the imaging device 124 is configured for use in a minimally invasive procedure. Examples of imaging devices suitable for use with the present disclosure include, but not limited to, an arthroscope, angioscope, bronchoscope, choledochoscope, colonoscope, cytoscope, duodenoscope, enteroscope, esophagogastro-duodenoscope (gastroscope), endoscope, laryngoscope, nasopharyngo-neproscope, sigmoidoscope, thoracoscope, and ureteroscope.
In one aspect, the imaging device employs multi-spectrum monitoring to discriminate topography and underlying structures. A multi-spectral image is one that captures image data within specific wavelength ranges across the electromagnetic spectrum. The wavelengths may be separated by filters or by the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, e.g., IR and ultraviolet. Spectral imaging can allow extraction of additional information the human eye fails to capture with its receptors for red, green, and blue. The use of multi-spectral imaging is described in greater detail under the heading “Advanced Imaging Acquisition Module” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety. Multi-spectrum monitoring can be a useful tool in relocating a surgical field after a surgical task is completed to perform one or more of the previously described tests on the treated tissue.
It is axiomatic that strict sterilization of the operating room and surgical equipment is required during any surgery. The strict hygiene and sterilization conditions required in a “surgical theater,” i.e., an operating or treatment room, necessitate the highest possible sterility of all medical devices and equipment. Part of that sterilization process is the need to sterilize anything that comes in contact with the patient or penetrates the sterile field, including the imaging device 124 and its attachments and components. It will be appreciated that the sterile field may be considered a specified area, such as within a tray or on a sterile towel, that is considered free of microorganisms, or the sterile field may be considered an area, immediately around a patient, who has been prepared for a surgical procedure. The sterile field may include the scrubbed team members, who are properly attired, and all furniture and fixtures in the area.
In various aspects, the visualization system 108 includes one or more imaging sensors, one or more image processing units, one or more storage arrays, and one or more displays that are strategically arranged with respect to the sterile field, as illustrated in
As illustrated in
In one aspect, the hub 106 is also configured to route a diagnostic input or feedback entered by a non-sterile operator at the visualization tower 111 to the primary display 119 within the sterile field, where it can be viewed by a sterile operator at the operating table. In one example, the input can be in the form of a modification to the snap-shot displayed on the non-sterile display 107 or 109, which can be routed to the primary display 119 by the hub 106.
Referring to
Referring now to
During a surgical procedure, energy application to tissue, for sealing and/or cutting, is generally associated with smoke evacuation, suction of excess fluid, and/or irrigation of the tissue. Fluid, power, and/or data lines from different sources are often entangled during the surgical procedure. Valuable time can be lost addressing this issue during a surgical procedure. Detangling the lines may necessitate disconnecting the lines from their respective modules, which may require resetting the modules. The hub modular enclosure 136 offers a unified environment for managing the power, data, and fluid lines, which reduces the frequency of entanglement between such lines.
Aspects of the present disclosure present a surgical hub for use in a surgical procedure that involves energy application to tissue at a surgical site. The surgical hub includes a hub enclosure and a combo generator module slidably receivable in a docking station of the hub enclosure. The docking station includes data and power contacts. The combo generator module includes two or more of an ultrasonic energy generator component, a bipolar RF energy generator component, and a monopolar RF energy generator component that are housed in a single unit. In one aspect, the combo generator module also includes a smoke evacuation component, at least one energy delivery cable for connecting the combo generator module to a surgical instrument, at least one smoke evacuation component configured to evacuate smoke, fluid, and/or particulates generated by the application of therapeutic energy to the tissue, and a fluid line extending from the remote surgical site to the smoke evacuation component.
In one aspect, the fluid line is a first fluid line and a second fluid line extends from the remote surgical site to a suction and irrigation module slidably received in the hub enclosure. In one aspect, the hub enclosure comprises a fluid interface.
Certain surgical procedures may require the application of more than one energy type to the tissue. One energy type may be more beneficial for cutting the tissue, while another different energy type may be more beneficial for sealing the tissue. For example, a bipolar generator can be used to seal the tissue while an ultrasonic generator can be used to cut the sealed tissue. Aspects of the present disclosure present a solution where a hub modular enclosure 136 is configured to accommodate different generators, and facilitate an interactive communication therebetween. One of the advantages of the hub modular enclosure 136 is enabling the quick removal and/or replacement of various modules.
Aspects of the present disclosure present a modular surgical enclosure for use in a surgical procedure that involves energy application to tissue. The modular surgical enclosure includes a first energy-generator module, configured to generate a first energy for application to the tissue, and a first docking station comprising a first docking port that includes first data and power contacts, wherein the first energy-generator module is slidably movable into an electrical engagement with the power and data contacts and wherein the first energy-generator module is slidably movable out of the electrical engagement with the first power and data contacts.
Further to the above, the modular surgical enclosure also includes a second energy-generator module configured to generate a second energy, different than the first energy, for application to the tissue, and a second docking station comprising a second docking port that includes second data and power contacts, wherein the second energy-generator module is slidably movable into an electrical engagement with the power and data contacts, and wherein the second energy-generator module is slidably movable out of the electrical engagement with the second power and data contacts.
In addition, the modular surgical enclosure also includes a communication bus between the first docking port and the second docking port, configured to facilitate communication between the first energy-generator module and the second energy-generator module.
Referring to
In one aspect, the hub modular enclosure 136 comprises a modular power and communication backplane 149 with external and wireless communication headers to enable the removable attachment of the modules 140, 126, 128 and interactive communication therebetween.
In one aspect, the hub modular enclosure 136 includes docking stations, or drawers, 151, herein also referred to as drawers, which are configured to slidably receive the modules 140, 126, 128.
In various aspects, the smoke evacuation module 126 includes a fluid line 154 that conveys captured/collected smoke and/or fluid away from a surgical site and to, for example, the smoke evacuation module 126. Vacuum suction originating from the smoke evacuation module 126 can draw the smoke into an opening of a utility conduit at the surgical site. The utility conduit, coupled to the fluid line, can be in the form of a flexible tube terminating at the smoke evacuation module 126. The utility conduit and the fluid line define a fluid path extending toward the smoke evacuation module 126 that is received in the hub enclosure 136.
In various aspects, the suction/irrigation module 128 is coupled to a surgical tool comprising an aspiration fluid line and a suction fluid line. In one example, the aspiration and suction fluid lines are in the form of flexible tubes extending from the surgical site toward the suction/irrigation module 128. One or more drive systems can be configured to cause irrigation and aspiration of fluids to and from the surgical site.
In one aspect, the surgical tool includes a shaft having an end effector at a distal end thereof and at least one energy treatment associated with the end effector, an aspiration tube, and an irrigation tube. The aspiration tube can have an inlet port at a distal end thereof and the aspiration tube extends through the shaft. Similarly, an irrigation tube can extend through the shaft and can have an inlet port in proximity to the energy deliver implement. The energy deliver implement is configured to deliver ultrasonic and/or RF energy to the surgical site and is coupled to the generator module 140 by a cable extending initially through the shaft.
The irrigation tube can be in fluid communication with a fluid source, and the aspiration tube can be in fluid communication with a vacuum source. The fluid source and/or the vacuum source can be housed in the suction/irrigation module 128. In one example, the fluid source and/or the vacuum source can be housed in the hub enclosure 136 separately from the suction/irrigation module 128. In such example, a fluid interface can be configured to connect the suction/irrigation module 128 to the fluid source and/or the vacuum source.
In one aspect, the modules 140, 126, 128 and/or their corresponding docking stations on the hub modular enclosure 136 may include alignment features that are configured to align the docking ports of the modules into engagement with their counterparts in the docking stations of the hub modular enclosure 136. For example, as illustrated in
In some aspects, the drawers 151 of the hub modular enclosure 136 are the same, or substantially the same size, and the modules are adjusted in size to be received in the drawers 151. For example, the side brackets 155 and/or 156 can be larger or smaller depending on the size of the module. In other aspects, the drawers 151 are different in size and are each designed to accommodate a particular module.
Furthermore, the contacts of a particular module can be keyed for engagement with the contacts of a particular drawer to avoid inserting a module into a drawer with mismatching contacts.
As illustrated in
In various aspects, the imaging module 138 comprises an integrated video processor and a modular light source and is adapted for use with various imaging devices. In one aspect, the imaging device is comprised of a modular housing that can be assembled with a light source module and a camera module. The housing can be a disposable housing. In at least one example, the disposable housing is removably coupled to a reusable controller, a light source module, and a camera module. The light source module and/or the camera module can be selectively chosen depending on the type of surgical procedure. In one aspect, the camera module comprises a CCD sensor. In another aspect, the camera module comprises a CMOS sensor. In another aspect, the camera module is configured for scanned beam imaging. Likewise, the light source module can be configured to deliver a white light or a different light, depending on the surgical procedure.
During a surgical procedure, removing a surgical device from the surgical field and replacing it with another surgical device that includes a different camera or a different light source can be inefficient. Temporarily losing sight of the surgical field may lead to undesirable consequences. The module imaging device of the present disclosure is configured to permit the replacement of a light source module or a camera module midstream during a surgical procedure, without having to remove the imaging device from the surgical field.
In one aspect, the imaging device comprises a tubular housing that includes a plurality of channels. A first channel is configured to slidably receive the camera module, which can be configured for a snap-fit engagement with the first channel. A second channel is configured to slidably receive the light source module, which can be configured for a snap-fit engagement with the second channel. In another example, the camera module and/or the light source module can be rotated into a final position within their respective channels. A threaded engagement can be employed in lieu of the snap-fit engagement.
In various examples, multiple imaging devices are placed at different positions in the surgical field to provide multiple views. The imaging module 138 can be configured to switch between the imaging devices to provide an optimal view. In various aspects, the imaging module 138 can be configured to integrate the images from the different imaging device.
Various image processors and imaging devices suitable for use with the present disclosure are described in U.S. Pat. No. 7,995,045, titled COMBINED SBI AND CONVENTIONAL IMAGE PROCESSOR, which issued on Aug. 9, 2011, which is herein incorporated by reference in its entirety. In addition, U.S. Pat. No. 7,982,776, titled SBI MOTION ARTIFACT REMOVAL APPARATUS AND METHOD, which issued on Jul. 19, 2011, which is herein incorporated by reference in its entirety, describes various systems for removing motion artifacts from image data. Such systems can be integrated with the imaging module 138. Furthermore, U.S. Patent Application Publication No. 2011/0306840, titled CONTROLLABLE MAGNETIC SOURCE TO FIXTURE INTRACORPOREAL APPARATUS, which published on Dec. 15, 2011, and U.S. Patent Application Publication No. 2014/0243597, titled SYSTEM FOR PERFORMING A MINIMALLY INVASIVE SURGICAL PROCEDURE, which published on Aug. 28, 2014, each of which is herein incorporated by reference in its entirety.
Modular devices 1a-1n located in the operating theater may be coupled to the modular communication hub 203. The network hub 207 and/or the network switch 209 may be coupled to a network router 211 to connect the devices 1a-1n to the cloud 204 or the local computer system 210. Data associated with the devices 1a-1n may be transferred to cloud-based computers via the router for remote data processing and manipulation. Data associated with the devices 1a-1n may also be transferred to the local computer system 210 for local data processing and manipulation. Modular devices 2a-2m located in the same operating theater also may be coupled to a network switch 209. The network switch 209 may be coupled to the network hub 207 and/or the network router 211 to connect to the devices 2a-2m to the cloud 204. Data associated with the devices 2a-2n may be transferred to the cloud 204 via the network router 211 for data processing and manipulation. Data associated with the devices 2a-2m may also be transferred to the local computer system 210 for local data processing and manipulation.
It will be appreciated that the surgical data network 201 may be expanded by interconnecting multiple network hubs 207 and/or multiple network switches 209 with multiple network routers 211. The modular communication hub 203 may be contained in a modular control tower configured to receive multiple devices 1a-1n/2a-2m. The local computer system 210 also may be contained in a modular control tower. The modular communication hub 203 is connected to a display 212 to display images obtained by some of the devices 1a-1n/2a-2m, for example during surgical procedures. In various aspects, the devices 1a-1n/2a-2m may include, for example, various modules such as an imaging module 138 coupled to an endoscope, a generator module 140 coupled to an energy-based surgical device, a smoke evacuation module 126, a suction/irrigation module 128, a communication module 130, a processor module 132, a storage array 134, a surgical device coupled to a display, and/or a non-contact sensor module, among other modular devices that may be connected to the modular communication hub 203 of the surgical data network 201.
In one aspect, the surgical data network 201 may comprise a combination of network hub(s), network switch(es), and network router(s) connecting the devices 1a-1n/2a-2m to the cloud. Any one of or all of the devices 1a-1n/2a-2m coupled to the network hub or network switch may collect data in real time and transfer the data to cloud computers for data processing and manipulation. It will be appreciated that cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle software applications. The word “cloud” may be used as a metaphor for “the Internet,” although the term is not limited as such. Accordingly, the term “cloud computing” may be used herein to refer to “a type of Internet-based computing,” where different services—such as servers, storage, and applications—are delivered to the modular communication hub 203 and/or computer system 210 located in the surgical theater (e.g., a fixed, mobile, temporary, or field operating room or space) and to devices connected to the modular communication hub 203 and/or computer system 210 through the Internet. The cloud infrastructure may be maintained by a cloud service provider. In this context, the cloud service provider may be the entity that coordinates the usage and control of the devices 1a-1n/2a-2m located in one or more operating theaters. The cloud computing services can perform a large number of calculations based on the data gathered by smart surgical instruments, robots, and other computerized devices located in the operating theater. The hub hardware enables multiple devices or connections to be connected to a computer that communicates with the cloud computing resources and storage.
Applying cloud computer data processing techniques on the data collected by the devices 1a-1n/2a-2m, the surgical data network provides improved surgical outcomes, reduced costs, and improved patient satisfaction. At least some of the devices 1a-1n/2a-2m may be employed to view tissue states to assess leaks or perfusion of sealed tissue after a tissue sealing and cutting procedure. At least some of the devices 1a-1n/2a-2m may be employed to identify pathology, such as the effects of diseases, using the cloud-based computing to examine data including images of samples of body tissue for diagnostic purposes. This includes localization and margin confirmation of tissue and phenotypes. At least some of the devices 1a-1n/2a-2m may be employed to identify anatomical structures of the body using a variety of sensors integrated with imaging devices and techniques such as overlaying images captured by multiple imaging devices. The data gathered by the devices 1a-1n/2a-2m, including image data, may be transferred to the cloud 204 or the local computer system 210 or both for data processing and manipulation including image processing and manipulation. The data may be analyzed to improve surgical procedure outcomes by determining if further treatment, such as the application of endoscopic intervention, emerging technologies, a targeted radiation, targeted intervention, and precise robotics to tissue-specific sites and conditions, may be pursued. Such data analysis may further employ outcome analytics processing, and using standardized approaches may provide beneficial feedback to either confirm surgical treatments and the behavior of the surgeon or suggest modifications to surgical treatments and the behavior of the surgeon.
In one implementation, the operating theater devices 1a-1n may be connected to the modular communication hub 203 over a wired channel or a wireless channel depending on the configuration of the devices 1a-1n to a network hub. The network hub 207 may be implemented, in one aspect, as a local network broadcast device that works on the physical layer of the Open System Interconnection (OSI) model. The network hub provides connectivity to the devices 1a-1n located in the same operating theater network. The network hub 207 collects data in the form of packets and sends them to the router in half duplex mode. The network hub 207 does not store any media access control/internet protocol (MAC/IP) to transfer the device data. Only one of the devices 1a-1n can send data at a time through the network hub 207. The network hub 207 has no routing tables or intelligence regarding where to send information and broadcasts all network data across each connection and to a remote server 213 (
In another implementation, the operating theater devices 2a-2m may be connected to a network switch 209 over a wired channel or a wireless channel. The network switch 209 works in the data link layer of the OSI model. The network switch 209 is a multicast device for connecting the devices 2a-2m located in the same operating theater to the network. The network switch 209 sends data in the form of frames to the network router 211 and works in full duplex mode. Multiple devices 2a-2m can send data at the same time through the network switch 209. The network switch 209 stores and uses MAC addresses of the devices 2a-2m to transfer data.
The network hub 207 and/or the network switch 209 are coupled to the network router 211 for connection to the cloud 204. The network router 211 works in the network layer of the OSI model. The network router 211 creates a route for transmitting data packets received from the network hub 207 and/or network switch 211 to cloud-based computer resources for further processing and manipulation of the data collected by any one of or all the devices 1a-1n/2a-2m. The network router 211 may be employed to connect two or more different networks located in different locations, such as, for example, different operating theaters of the same healthcare facility or different networks located in different operating theaters of different healthcare facilities. The network router 211 sends data in the form of packets to the cloud 204 and works in full duplex mode. Multiple devices can send data at the same time. The network router 211 uses IP addresses to transfer data.
In one example, the network hub 207 may be implemented as a USB hub, which allows multiple USB devices to be connected to a host computer. The USB hub may expand a single USB port into several tiers so that there are more ports available to connect devices to the host system computer. The network hub 207 may include wired or wireless capabilities to receive information over a wired channel or a wireless channel. In one aspect, a wireless USB short-range, high-bandwidth wireless radio communication protocol may be employed for communication between the devices 1a-1n and devices 2a-2m located in the operating theater.
In other examples, the operating theater devices 1a-1n/2a-2m may communicate to the modular communication hub 203 via Bluetooth wireless technology standard for exchanging data over short distances (using short-wavelength UHF radio waves in the ISM band from 2.4 to 2.485 GHz) from fixed and mobile devices and building personal area networks (PANs). In other aspects, the operating theater devices 1a-1n/2a-2m may communicate to the modular communication hub 203 via a number of wireless or wired communication standards or protocols, including but not limited to W-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, long-term evolution (LTE), and Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, and Ethernet derivatives thereof, as well as any other wireless and wired protocols that are designated as 3G, 4G, 5G, and beyond. The computing module may include a plurality of communication modules. For instance, a first communication module may be dedicated to shorter-range wireless communications such as Wi-Fi and Bluetooth, and a second communication module may be dedicated to longer-range wireless communications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, and others.
The modular communication hub 203 may serve as a central connection for one or all of the operating theater devices 1a-1n/2a-2m and handles a data type known as frames. Frames carry the data generated by the devices 1a-1n/2a-2m. When a frame is received by the modular communication hub 203, it is amplified and transmitted to the network router 211, which transfers the data to the cloud computing resources by using a number of wireless or wired communication standards or protocols, as described herein.
The modular communication hub 203 can be used as a standalone device or be connected to compatible network hubs and network switches to form a larger network. The modular communication hub 203 is generally easy to install, configure, and maintain, making it a good option for networking the operating theater devices 1a-1n/2a-2m.
The surgical hub 206 employs a non-contact sensor module 242 to measure the dimensions of the operating theater and generate a map of the surgical theater using either ultrasonic or laser-type non-contact measurement devices. An ultrasound-based non-contact sensor module scans the operating theater by transmitting a burst of ultrasound and receiving the echo when it bounces off the perimeter walls of an operating theater as described under the heading “Surgical Hub Spatial Awareness Within an Operating Room” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety, in which the sensor module is configured to determine the size of the operating theater and to adjust Bluetooth-pairing distance limits. A laser-based non-contact sensor module scans the operating theater by transmitting laser light pulses, receiving laser light pulses that bounce off the perimeter walls of the operating theater, and comparing the phase of the transmitted pulse to the received pulse to determine the size of the operating theater and to adjust Bluetooth pairing distance limits, for example.
The computer system 210 comprises a processor 244 and a network interface 245. The processor 244 is coupled to a communication module 247, storage 248, memory 249, non-volatile memory 250, and input/output interface 251 via a system bus. The system bus can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, 9-bit bus, Industrial Standard Architecture (ISA), Micro-Charmel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), USB, Advanced Graphics Port (AGP), Personal Computer Memory Card International Association bus (PCMCIA), Small Computer Systems Interface (SCSI), or any other proprietary bus.
The processor 244 may be any single-core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In one aspect, the processor may be an LM4F230H5QR ARM Cortex-M4F Processor Core, available from Texas Instruments, for example, comprising an on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle serial random access memory (SRAM), an internal read-only memory (ROM) loaded with StellarisWare® software, a 2 KB electrically erasable programmable read-only memory (EEPROM), and/or one or more pulse width modulation (PWM) modules, one or more quadrature encoder inputs (QEI) analogs, one or more 12-bit analog-to-digital converters (ADCs) with 12 analog input channels, details of which are available for the product datasheet.
In one aspect, the processor 244 may comprise a safety controller comprising two controller-based families such as TMS570 and RM4x, known under the trade name Hercules ARM Cortex R4, also by Texas Instruments. The safety controller may be configured specifically for IEC 61508 and ISO 26262 safety critical applications, among others, to provide advanced integrated safety features while delivering scalable performance, connectivity, and memory options.
The system memory includes volatile memory and non-volatile memory. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer system, such as during start-up, is stored in non-volatile memory. For example, the non-volatile memory can include ROM, programmable ROM (PROM), electrically programmable ROM (EPROM), EEPROM, or flash memory. Volatile memory includes random-access memory (RAM), which acts as external cache memory. Moreover, RAM is available in many forms such as SRAM, dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).
The computer system 210 also includes removable/non-removable, volatile/non-volatile computer storage media, such as for example disk storage. The disk storage includes, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-60 drive, flash memory card, or memory stick. In addition, the disk storage can include storage media separately or in combination with other storage media including, but not limited to, an optical disc drive such as a compact disc ROM device (CD-ROM), compact disc recordable drive (CD-R Drive), compact disc rewritable drive (CD-RW Drive), or a digital versatile disc ROM drive (DVD-ROM). To facilitate the connection of the disk storage devices to the system bus, a removable or non-removable interface may be employed.
It is to be appreciated that the computer system 210 includes software that acts as an intermediary between users and the basic computer resources described in a suitable operating environment. Such software includes an operating system. The operating system, which can be stored on the disk storage, acts to control and allocate resources of the computer system. System applications take advantage of the management of resources by the operating system through program modules and program data stored either in the system memory or on the disk storage. It is to be appreciated that various components described herein can be implemented with various operating systems or combinations of operating systems.
A user enters commands or information into the computer system 210 through input device(s) coupled to the I/O interface 251. The input devices include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processor through the system bus via interface port(s). The interface port(s) include, for example, a serial port, a parallel port, a game port, and a USB. The output device(s) use some of the same types of ports as input device(s). Thus, for example, a USB port may be used to provide input to the computer system and to output information from the computer system to an output device. An output adapter is provided to illustrate that there are some output devices like monitors, displays, speakers, and printers, among other output devices that require special adapters. The output adapters include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device and the system bus. It should be noted that other devices and/or systems of devices, such as remote computer(s), provide both input and output capabilities.
The computer system 210 can operate in a networked environment using logical connections to one or more remote computers, such as cloud computer(s), or local computers. The remote cloud computer(s) can be a personal computer, server, router, network PC, workstation, microprocessor-based appliance, peer device, or other common network node, and the like, and typically includes many or all of the elements described relative to the computer system. For purposes of brevity, only a memory storage device is illustrated with the remote computer(s). The remote computer(s) is logically connected to the computer system through a network interface and then physically connected via a communication connection. The network interface encompasses communication networks such as local area networks (LANs) and wide area networks (WANs). LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and the like. WAN technologies include, but are not limited to, point-to-point links, circuit-switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet-switching networks, and Digital Subscriber Lines (DSL).
In various aspects, the computer system 210 of
The communication connection(s) refers to the hardware/software employed to connect the network interface to the bus. While the communication connection is shown for illustrative clarity inside the computer system, it can also be external to the computer system 210. The hardware/software necessary for connection to the network interface includes, for illustrative purposes only, internal and external technologies such as modems, including regular telephone-grade modems, cable modems, and DSL modems, ISDN adapters, and Ethernet cards.
The USB network hub 300 device is implemented with a digital state machine instead of a microcontroller, and no firmware programming is required. Fully compliant USB transceivers are integrated into the circuit for the upstream USB transceiver port 302 and all downstream USB transceiver ports 304, 306, 308. The downstream USB transceiver ports 304, 306, 308 support both full-speed and low-speed devices by automatically setting the slew rate according to the speed of the device attached to the ports. The USB network hub 300 device may be configured either in bus-powered or self-powered mode and includes a hub power logic 312 to manage power.
The USB network hub 300 device includes a serial interface engine 310 (SIE). The SIE 310 is the front end of the USB network hub 300 hardware and handles most of the protocol described in chapter 8 of the USB specification. The SIE 310 typically comprehends signaling up to the transaction level. The functions that it handles could include: packet recognition, transaction sequencing, SOP, EOP, RESET, and RESUME signal detection/generation, clock/data separation, non-return-to-zero invert (NRZI) data encoding/decoding and bit-stuffing, CRC generation and checking (token and data), packet ID (PID) generation and checking/decoding, and/or serial-parallel/parallel-serial conversion. The 310 receives a clock input 314 and is coupled to a suspend/resume logic and frame timer 316 circuit and a hub repeater circuit 318 to control communication between the upstream USB transceiver port 302 and the downstream USB transceiver ports 304, 306, 308 through port logic circuits 320, 322, 324. The SIE 310 is coupled to a command decoder 326 via interface logic to control commands from a serial EEPROM via a serial EEPROM interface 330.
In various aspects, the USB network hub 300 can connect 127 functions configured in up to six logical layers (tiers) to a single computer. Further, the USB network hub 300 can connect to all peripherals using a standardized four-wire cable that provides both communication and power distribution. The power configurations are bus-powered and self-powered modes. The USB network hub 300 may be configured to support four modes of power management: a bus-powered hub, with either individual-port power management or ganged-port power management, and the self-powered hub, with either individual-port power management or ganged-port power management. In one aspect, using a USB cable, the USB network hub 300, the upstream USB transceiver port 302 is plugged into a USB host controller, and the downstream USB transceiver ports 304, 306, 308 are exposed for connecting USB compatible devices, and so forth.
In one aspect, the microcontroller 461 may be any single-core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In one aspect, the main microcontroller 461 may be an LM4F230H5QR ARM Cortex-M4F Processor Core, available from Texas Instruments, for example, comprising an on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle SRAM, and internal ROM loaded with StellarisWare® software, a 2 KB EEPROM, one or more PWM modules, one or more QEI analogs, and/or one or more 12-bit ADCs with 12 analog input channels, details of which are available for the product datasheet.
In one aspect, the microcontroller 461 may comprise a safety controller comprising two controller-based families such as TMS570 and RM4x, known under the trade name Hercules ARM Cortex R4, also by Texas Instruments. The safety controller may be configured specifically for IEC 61508 and ISO 26262 safety critical applications, among others, to provide advanced integrated safety features while delivering scalable performance, connectivity, and memory options.
The microcontroller 461 may be programmed to perform various functions such as precise control over the speed and position of the knife and articulation systems. In one aspect, the microcontroller 461 includes a processor 462 and a memory 468. The electric motor 482 may be a brushed direct current (DC) motor with a gearbox and mechanical links to an articulation or knife system. In one aspect, a motor driver 492 may be an A3941 available from Allegro Microsystems, Inc. Other motor drivers may be readily substituted for use in the tracking system 480 comprising an absolute positioning system. A detailed description of an absolute positioning system is described in U.S. Patent Application Publication No. 2017/0296213, titled SYSTEMS AND METHODS FOR CONTROLLING A SURGICAL STAPLING AND CUTTING INSTRUMENT, which published on Oct. 19, 2017, which is herein incorporated by reference in its entirety.
The microcontroller 461 may be programmed to provide precise control over the speed and position of displacement members and articulation systems. The microcontroller 461 may be configured to compute a response in the software of the microcontroller 461. The computed response is compared to a measured response of the actual system to obtain an “observed” response, which is used for actual feedback decisions. The observed response is a favorable, tuned value that balances the smooth, continuous nature of the simulated response with the measured response, which can detect outside influences on the system.
In one aspect, the motor 482 may be controlled by the motor driver 492 and can be employed by the firing system of the surgical instrument or tool. In various forms, the motor 482 may be a brushed DC driving motor having a maximum rotational speed of approximately 25,000 RPM. In other arrangements, the motor 482 may include a brushless motor, a cordless motor, a synchronous motor, a stepper motor, or any other suitable electric motor. The motor driver 492 may comprise an H-bridge driver comprising field-effect transistors (FETs), for example. The motor 482 can be powered by a power assembly releasably mounted to the handle assembly or tool housing for supplying control power to the surgical instrument or tool. The power assembly may comprise a battery which may include a number of battery cells connected in series that can be used as the power source to power the surgical instrument or tool. In certain circumstances, the battery cells of the power assembly may be replaceable and/or rechargeable. In at least one example, the battery cells can be lithium-ion batteries which can be couplable to and separable from the power assembly.
The motor driver 492 may be an A3941 available from Allegro Microsystems, Inc. The A3941 492 is a full-bridge controller for use with external N-channel power metal-oxide semiconductor field-effect transistors (MOSFETs) specifically designed for inductive loads, such as brush DC motors. The driver 492 comprises a unique charge pump regulator that provides full (>10 V) gate drive for battery voltages down to 7 V and allows the A3941 to operate with a reduced gate drive, down to 5.5 V. A bootstrap capacitor may be employed to provide the above battery supply voltage required for N-channel MOSFETs. An internal charge pump for the high-side drive allows DC (100% duty cycle) operation. The full bridge can be driven in fast or slow decay modes using diode or synchronous rectification. In the slow decay mode, current recirculation can be through the high-side or the lowside FETs. The power FETs are protected from shoot-through by resistor-adjustable dead time. Integrated diagnostics provide indications of undervoltage, overtemperature, and power bridge faults and can be configured to protect the power MOSFETs under most short circuit conditions. Other motor drivers may be readily substituted for use in the tracking system 480 comprising an absolute positioning system.
The tracking system 480 comprises a controlled motor drive circuit arrangement comprising a position sensor 472 according to one aspect of this disclosure. The position sensor 472 for an absolute positioning system provides a unique position signal corresponding to the location of a displacement member. In one aspect, the displacement member represents a longitudinally movable drive member comprising a rack of drive teeth for meshing engagement with a corresponding drive gear of a gear reducer assembly. In other aspects, the displacement member represents the firing member, which could be adapted and configured to include a rack of drive teeth. In yet another aspect, the displacement member represents a firing bar or the !-beam, each of which can be adapted and configured to include a rack of drive teeth. Accordingly, as used herein, the term displacement member is used generically to refer to any movable member of the surgical instrument or tool such as the drive member, the firing member, the firing bar, the I-beam, or any element that can be displaced. In one aspect, the longitudinally movable drive member is coupled to the firing member, the firing bar, and the !-beam. Accordingly, the absolute positioning system can, in effect, track the linear displacement of the I-beam by tracking the linear displacement of the longitudinally movable drive member. In various other aspects, the displacement member may be coupled to any position sensor 472 suitable for measuring linear displacement. Thus, the longitudinally movable drive member, the firing member, the firing bar, or the I-beam, or combinations thereof, may be coupled to any suitable linear displacement sensor. Linear displacement sensors may include contact or non-contact displacement sensors. Linear displacement sensors may comprise linear variable differential transformers (LVDT), differential variable reluctance transducers (DVRT), a slide potentiometer, a magnetic sensing system comprising a movable magnet and a series of linearly arranged Hall effect sensors, a magnetic sensing system comprising a fixed magnet and a series of movable, linearly arranged Hall effect sensors, an optical sensing system comprising a movable light source and a series of linearly arranged photo diodes or photo detectors, an optical sensing system comprising a fixed light source and a series of movable linearly, arranged photo diodes or photo detectors, or any combination thereof.
The electric motor 482 can include a rotatable shaft that operably interfaces with a gear assembly that is mounted in meshing engagement with a set, or rack, of drive teeth on the displacement member. A sensor element may be operably coupled to a gear assembly such that a single revolution of the position sensor 472 element corresponds to some linear longitudinal translation of the displacement member. An arrangement of gearing and sensors can be connected to the linear actuator, via a rack and pinion arrangement, or a rotary actuator, via a spur gear or other connection. A power source supplies power to the absolute positioning system and an output indicator may display the output of the absolute positioning system. The displacement member represents the longitudinally movable drive member comprising a rack of drive teeth formed thereon for meshing engagement with a corresponding drive gear of the gear reducer assembly. The displacement member represents the longitudinally movable firing member, firing bar, I-beam, or combinations thereof.
A single revolution of the sensor element associated with the position sensor 472 is equivalent to a longitudinal linear displacement d1 of the of the displacement member, where d1 is the longitudinal linear distance that the displacement member moves from point “a” to point “b” after a single revolution of the sensor element coupled to the displacement member. The sensor arrangement may be connected via a gear reduction that results in the position sensor 472 completing one or more revolutions for the full stroke of the displacement member. The position sensor 472 may complete multiple revolutions for the full stroke of the displacement member.
A series of switches, where n is an integer greater than one, may be employed alone or in combination with a gear reduction to provide a unique position signal for more than one revolution of the position sensor 472. The state of the switches are fed back to the microcontroller 461 that applies logic to determine a unique position signal corresponding to the longitudinal linear displacement d1+d2+ . . . dn of the displacement member. The output of the position sensor 472 is provided to the microcontroller 461. The position sensor 472 of the sensor arrangement may comprise a magnetic sensor, an analog rotary sensor like a potentiometer, or an array of analog Hall-effect elements, which output a unique combination of position signals or values.
The position sensor 472 may comprise any number of magnetic sensing elements, such as, for example, magnetic sensors classified according to whether they measure the total magnetic field or the vector components of the magnetic field. The techniques used to produce both types of magnetic sensors encompass many aspects of physics and electronics. The technologies used for magnetic field sensing include search coil, fluxgate, optically pumped, nuclear precession, SQUID, Hall-effect, anisotropic magnetoresistance, giant magnetoresistance, magnetic tunnel junctions, giant magnetoimpedance, magnetostrictive/piezoelectric composites, magnetodiode, magnetotransistor, fiber-optic, magneto-optic, and microelectromechanical systems-based magnetic sensors, among others.
In one aspect, the position sensor 472 for the tracking system 480 comprising an absolute positioning system comprises a magnetic rotary absolute positioning system. The position sensor 472 may be implemented as an AS5055EQFT single-chip magnetic rotary position sensor available from Austria Microsystems, AG. The position sensor 472 is interfaced with the microcontroller 461 to provide an absolute positioning system. The position sensor 472 is a low-voltage and low-power component and includes four Hall-effect elements in an area of the position sensor 472 that is located above a magnet. A high-resolution ADC and a smart power management controller are also provided on the chip. A coordinate rotation digital computer (CORDIC) processor, also known as the digit-by-digit method and Volder's algorithm, is provided to implement a simple and efficient algorithm to calculate hyperbolic and trigonometric functions that require only addition, subtraction, bitshift, and table lookup operations. The angle position, alarm bits, and magnetic field information are transmitted over a standard serial communication interface, such as a serial peripheral interface (SPI) interface, to the microcontroller 461. The position sensor 472 provides 12 or 14 bits of resolution. The position sensor 472 may be an AS5055 chip provided in a small QFN 16-pin 4×4×0.85 mm package.
The tracking system 480 comprising an absolute positioning system may comprise and/or be programmed to implement a feedback controller, such as a PID, state feedback, and adaptive controller. A power source converts the signal from the feedback controller into a physical input to the system: in this case the voltage. Other examples include a PWM of the voltage, current, and force. Other sensor(s) may be provided to measure physical parameters of the physical system in addition to the position measured by the position sensor 472. In some aspects, the other sensor(s) can include sensor arrangements such as those described in U.S. Pat. No. 9,345,481, titled STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM, which issued on May 24, 2016, which is herein incorporated by reference in its entirety; U.S. Patent Application Publication No. 2014/0263552, titled STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM, which published on Sep. 18, 2014, which is herein incorporated by reference in its entirety; and U.S. patent application Ser. No. 15/628,175, titled TECHNIQUES FOR ADAPTIVE CONTROL OF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT, filed Jun. 20, 2017, which is herein incorporated by reference in its entirety. In a digital signal processing system, an absolute positioning system is coupled to a digital data acquisition system where the output of the absolute positioning system will have a finite resolution and sampling frequency. The absolute positioning system may comprise a compare-and-combine circuit to combine a computed response with a measured response using algorithms, such as a weighted average and a theoretical control loop, that drive the computed response towards the measured response. The computed response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input.
The absolute positioning system provides an absolute position of the displacement member upon power-up of the instrument, without retracting or advancing the displacement member to a reset (zero or home) position as may be required with conventional rotary encoders that merely count the number of steps forwards or backwards that the motor 482 has taken to infer the position of a device actuator, drive bar, knife, or the like.
A sensor 474, such as, for example, a strain gauge or a micro-strain gauge, is configured to measure one or more parameters of the end effector, such as, for example, the amplitude of the strain exerted on the anvil during a clamping operation, which can be indicative of the closure forces applied to the anvil. The measured strain is converted to a digital signal and provided to the processor 462. Alternatively, or in addition to the sensor 474, a sensor 476, such as, for example, a load sensor, can measure the closure force applied by the closure drive system to the anvil. The sensor 476, such as, for example, a load sensor, can measure the firing force applied to an I-beam in a firing stroke of the surgical instrument or tool. The I-beam is configured to engage a wedge sled, which is configured to upwardly cam staple drivers to force out staples into deforming contact with an anvil. The I-beam also includes a sharpened cutting edge that can be used to sever tissue as the I-beam is advanced distally by the firing bar. Alternatively, a current sensor 478 can be employed to measure the current drawn by the motor 482. The force required to advance the firing member can correspond to the current drawn by the motor 482, for example. The measured force is converted to a digital signal and provided to the processor 462.
In one form, the strain gauge sensor 474 can be used to measure the force applied to the tissue by the end effector. A strain gauge can be coupled to the end effector to measure the force on the tissue being treated by the end effector. A system for measuring forces applied to the tissue grasped by the end effector comprises a strain gauge sensor 474, such as, for example, a micro-strain gauge, that is configured to measure one or more parameters of the end effector, for example. In one aspect, the strain gauge sensor 474 can measure the amplitude or magnitude of the strain exerted on a jaw member of an end effector during a clamping operation, which can be indicative of the tissue compression. The measured strain is converted to a digital signal and provided to a processor 462 of the microcontroller 461. A load sensor 476 can measure the force used to operate the knife element, for example, to cut the tissue captured between the anvil and the staple cartridge. A magnetic field sensor can be employed to measure the thickness of the captured tissue. The measurement of the magnetic field sensor also may be converted to a digital signal and provided to the processor 462.
The measurements of the tissue compression, the tissue thickness, and/or the force required to close the end effector on the tissue, as respectively measured by the sensors 474, 476, can be used by the microcontroller 461 to characterize the selected position of the firing member and/or the corresponding value of the speed of the firing member. In one instance, a memory 468 may store a technique, an equation, and/or a lookup table which can be employed by the microcontroller 461 in the assessment.
The control system 470 of the surgical instrument or tool also may comprise wired or wireless communication circuits to communicate with the modular communication hub as shown in
In certain instances, the surgical instrument system or tool may include a firing motor 602. The firing motor 602 may be operably coupled to a firing motor drive assembly 604 which can be configured to transmit firing motions, generated by the motor 602 to the end effector, in particular to displace the I-beam element. In certain instances, the firing motions generated by the motor 602 may cause the staples to be deployed from the staple cartridge into tissue captured by the end effector and/or the cutting edge of the I-beam element to be advanced to cut the captured tissue, for example. The I-beam element may be retracted by reversing the direction of the motor 602.
In certain instances, the surgical instrument or tool may include a closure motor 603. The closure motor 603 may be operably coupled to a closure motor drive assembly 605 which can be configured to transmit closure motions, generated by the motor 603 to the end effector, in particular to displace a closure tube to close the anvil and compress tissue between the anvil and the staple cartridge. The closure motions may cause the end effector to transition from an open configuration to an approximated configuration to capture tissue, for example. The end effector may be transitioned to an open position by reversing the direction of the motor 603.
In certain instances, the surgical instrument or tool may include one or more articulation motors 606a, 606b, for example. The motors 606a, 606b may be operably coupled to respective articulation motor drive assemblies 608a, 608b, which can be configured to transmit articulation motions generated by the motors 606a, 606b to the end effector. In certain instances, the articulation motions may cause the end effector to articulate relative to the shaft, for example.
As described above, the surgical instrument or tool may include a plurality of motors which may be configured to perform various independent functions. In certain instances, the plurality of motors of the surgical instrument or tool can be individually or separately activated to perform one or more functions while the other motors remain inactive. For example, the articulation motors 606a, 606b can be activated to cause the end effector to be articulated while the firing motor 602 remains inactive. Alternatively, the firing motor 602 can be activated to fire the plurality of staples, and/or to advance the cutting edge, while the articulation motor 606 remains inactive. Furthermore the closure motor 603 may be activated simultaneously with the firing motor 602 to cause the closure tube and the I-beam element to advance distally as described in more detail hereinbelow.
In certain instances, the surgical instrument or tool may include a common control module 610 which can be employed with a plurality of motors of the surgical instrument or tool. In certain instances, the common control module 610 may accommodate one of the plurality of motors at a time. For example, the common control module 610 can be couplable to and separable from the plurality of motors of the robotic surgical instrument individually. In certain instances, a plurality of the motors of the surgical instrument or tool may share one or more common control modules such as the common control module 610. In certain instances, a plurality of motors of the surgical instrument or tool can be individually and selectively engaged with the common control module 610. In certain instances, the common control module 610 can be selectively switched from interfacing with one of a plurality of motors of the surgical instrument or tool to interfacing with another one of the plurality of motors of the surgical instrument or tool.
In at least one example, the common control module 610 can be selectively switched between operable engagement with the articulation motors 606a, 606b and operable engagement with either the firing motor 602 or the closure motor 603. In at least one example, as illustrated in
Each of the motors 602, 603, 606a, 606b may comprise a torque sensor to measure the output torque on the shaft of the motor. The force on an end effector may be sensed in any conventional manner, such as by force sensors on the outer sides of the jaws or by a torque sensor for the motor actuating the jaws.
In various instances, as illustrated in
In certain instances, the microcontroller 620 may include a microprocessor 622 (the “processor”) and one or more non-transitory computer-readable mediums or memory units 624 (the “memory”). In certain instances, the memory 624 may store various program instructions, which when executed may cause the processor 622 to perform a plurality of functions and/or calculations described herein. In certain instances, one or more of the memory units 624 may be coupled to the processor 622, for example.
In certain instances, the power source 628 can be employed to supply power to the microcontroller 620, for example. In certain instances, the power source 628 may comprise a battery (or “battery pack” or “power pack”), such as a lithium-ion battery, for example. In certain instances, the battery pack may be configured to be releasably mounted to a handle for supplying power to the surgical instrument 600. A number of battery cells connected in series may be used as the power source 628. In certain instances, the power source 628 may be replaceable and/or rechargeable, for example.
In various instances, the processor 622 may control the motor driver 626 to control the position, direction of rotation, and/or velocity of a motor that is coupled to the common control module 610. In certain instances, the processor 622 can signal the motor driver 626 to stop and/or disable a motor that is coupled to the common control module 610. It should be understood that the term “processor” as used herein includes any suitable microprocessor, microcontroller, or other basic computing device that incorporates the functions of a computer's central processing unit (CPU) on an integrated circuit or, at most, a few integrated circuits. The processor is a multipurpose, programmable device that accepts digital data as input, processes it according to instructions stored in its memory, and provides results as output. It is an example of sequential digital logic, as it has internal memory. Processors operate on numbers and symbols represented in the binary numeral system.
In one instance, the processor 622 may be any single-core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In certain instances, the microcontroller 620 may be an LM 4F230H5QR, available from Texas Instruments, for example. In at least one example, the Texas Instruments LM4F230H5QR is an ARM Cortex-M4F Processor Core comprising an on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle SRAM, an internal ROM loaded with StellarisWare® software, a 2 KB EEPROM, one or more PWM modules, one or more QEI analogs, one or more 12-bit ADCs with 12 analog input channels, among other features that are readily available for the product datasheet. Other microcontrollers may be readily substituted for use with the module 4410. Accordingly, the present disclosure should not be limited in this context.
In certain instances, the memory 624 may include program instructions for controlling each of the motors of the surgical instrument 600 that are couplable to the common control module 610. For example, the memory 624 may include program instructions for controlling the firing motor 602, the closure motor 603, and the articulation motors 606a, 606b. Such program instructions may cause the processor 622 to control the firing, closure, and articulation functions in accordance with inputs from algorithms or control programs of the surgical instrument or tool.
In certain instances, one or more mechanisms and/or sensors such as, for example, sensors 630 can be employed to alert the processor 622 to the program instructions that should be used in a particular setting. For example, the sensors 630 may alert the processor 622 to use the program instructions associated with firing, closing, and articulating the end effector. In certain instances, the sensors 630 may comprise position sensors which can be employed to sense the position of the switch 614, for example. Accordingly, the processor 622 may use the program instructions associated with firing the I-beam of the end effector upon detecting, through the sensors 630 for example, that the switch 614 is in the first position 616; the processor 622 may use the program instructions associated with closing the anvil upon detecting, through the sensors 630 for example, that the switch 614 is in the second position 617; and the processor 622 may use the program instructions associated with articulating the end effector upon detecting, through the sensors 630 for example, that the switch 614 is in the third or fourth position 618a, 618b.
In one aspect, the robotic surgical instrument 700 comprises a control circuit 710 configured to control an anvil 716 and an I-beam 714 (including a sharp cutting edge) portion of an end effector 702, a removable staple cartridge 718, a shaft 740, and one or more articulation members 742a, 742b via a plurality of motors 704a-704e. A position sensor 734 may be configured to provide position feedback of the I-beam 714 to the control circuit 710. Other sensors 738 may be configured to provide feedback to the control circuit 710. A timer/counter 731 provides timing and counting information to the control circuit 710. An energy source 712 may be provided to operate the motors 704a-704e, and a current sensor 736 provides motor current feedback to the control circuit 710. The motors 704a-704e can be operated individually by the control circuit 710 in a open-loop or closed-loop feedback control.
In one aspect, the control circuit 710 may comprise one or more microcontrollers, microprocessors, or other suitable processors for executing instructions that cause the processor or processors to perform one or more tasks. In one aspect, a timer/counter 731 provides an output signal, such as the elapsed time or a digital count, to the control circuit 710 to correlate the position of the I-beam 714 as determined by the position sensor 734 with the output of the timer/counter 731 such that the control circuit 710 can determine the position of the I-beam 714 at a specific time (t) relative to a starting position or the time (t) when the I-beam 714 is at a specific position relative to a starting position. The timer/counter 731 may be configured to measure elapsed time, count external events, or time external events.
In one aspect, the control circuit 710 may be programmed to control functions of the end effector 702 based on one or more tissue conditions. The control circuit 710 may be programmed to sense tissue conditions, such as thickness, either directly or indirectly, as described herein. The control circuit 710 may be programmed to select a firing control program or closure control program based on tissue conditions. A firing control program may describe the distal motion of the displacement member. Different firing control programs may be selected to better treat different tissue conditions. For example, when thicker tissue is present, the control circuit 710 may be programmed to translate the displacement member at a lower velocity and/or with lower power. When thinner tissue is present, the control circuit 710 may be programmed to translate the displacement member at a higher velocity and/or with higher power. A closure control program may control the closure force applied to the tissue by the anvil 716. Other control programs control the rotation of the shaft 740 and the articulation members 742a, 742b.
In one aspect, the control circuit 710 may generate motor set point signals. The motor set point signals may be provided to various motor controllers 708a-708e. The motor controllers 708a-708e may comprise one or more circuits configured to provide motor drive signals to the motors 704a-704e to drive the motors 704a-704e as described herein. In some examples, the motors 704a-704e may be brushed DC electric motors. For example, the velocity of the motors 704a-704e may be proportional to the respective motor drive signals. In some examples, the motors 704a-704e may be brushless DC electric motors, and the respective motor drive signals may comprise a PWM signal provided to one or more stator windings of the motors 704a-704e. Also, in some examples, the motor controllers 708a-708e may be omitted and the control circuit 710 may generate the motor drive signals directly.
In one aspect, the control circuit 710 may initially operate each of the motors 704a-704e in an open-loop configuration for a first open-loop portion of a stroke of the displacement member. Based on the response of the robotic surgical instrument 700 during the open-loop portion of the stroke, the control circuit 710 may select a firing control program in a closed-loop configuration. The response of the instrument may include a translation distance of the displacement member during the open-loop portion, a time elapsed during the open-loop portion, the energy provided to one of the motors 704a-704e during the open-loop portion, a sum of pulse widths of a motor drive signal, etc. After the open-loop portion, the control circuit 710 may implement the selected firing control program for a second portion of the displacement member stroke. For example, during a closed-loop portion of the stroke, the control circuit 710 may modulate one of the motors 704a-704e based on translation data describing a position of the displacement member in a closed-loop manner to translate the displacement member at a constant velocity.
In one aspect, the motors 704a-704e may receive power from an energy source 712. The energy source 712 may be a DC power supply driven by a main alternating current power source, a battery, a super capacitor, or any other suitable energy source. The motors 704a-704e may be mechanically coupled to individual movable mechanical elements such as the !-beam 714, anvil 716, shaft 740, articulation 742a, and articulation 742b via respective transmissions 706a-706e. The transmissions 706a-706e may include one or more gears or other linkage components to couple the motors 704a-704e to movable mechanical elements. A position sensor 734 may sense a position of the I-beam 714. The position sensor 734 may be or include any type of sensor that is capable of generating position data that indicate a position of the I-beam 714. In some examples, the position sensor 734 may include an encoder configured to provide a series of pulses to the control circuit 710 as the I-beam 714 translates distally and proximally. The control circuit 710 may track the pulses to determine the position of the I-beam 714. Other suitable position sensors may be used, including, for example, a proximity sensor. Other types of position sensors may provide other signals indicating motion of the I-beam 714. Also, in some examples, the position sensor 734 may be omitted. Where any of the motors 704a-704e is a stepper motor, the control circuit 710 may track the position of the I-beam 714 by aggregating the number and direction of steps that the motor 704 has been instructed to execute. The position sensor 734 may be located in the end effector 702 or at any other portion of the instrument. The outputs of each of the motors 704a-704e include a torque sensor 744a-744e to sense force and have an encoder to sense rotation of the drive shaft.
In one aspect, the control circuit 710 is configured to drive a firing member such as the I-beam 714 portion of the end effector 702. The control circuit 710 provides a motor set point to a motor control 708a, which provides a drive signal to the motor 704a. The output shaft of the motor 704a is coupled to a torque sensor 744a. The torque sensor 744a is coupled to a transmission 706a which is coupled to the I-beam 714. The transmission 706a comprises movable mechanical elements such as rotating elements and a firing member to control the movement of the I-beam 714 distally and proximally along a longitudinal axis of the end effector 702. In one aspect, the motor 704a may be coupled to the knife gear assembly, which includes a knife gear reduction set that includes a first knife drive gear and a second knife drive gear. A torque sensor 744a provides a firing force feedback signal to the control circuit 710. The firing force signal represents the force required to fire or displace the I-beam 714. A position sensor 734 may be configured to provide the position of the I-beam 714 along the firing stroke or the position of the firing member as a feedback signal to the control circuit 710. The end effector 702 may include additional sensors 738 configured to provide feedback signals to the control circuit 710. When ready to use, the control circuit 710 may provide a firing signal to the motor control 708a. In response to the firing signal, the motor 704a may drive the firing member distally along the longitudinal axis of the end effector 702 from a proximal stroke start position to a stroke end position distal to the stroke start position. As the firing member translates distally, an I-beam 714, with a cutting element positioned at a distal end, advances distally to cut tissue located between the staple cartridge 718 and the anvil 716.
In one aspect, the control circuit 710 is configured to drive a closure member such as the anvil 716 portion of the end effector 702. The control circuit 710 provides a motor set point to a motor control 708b, which provides a drive signal to the motor 704b. The output shaft of the motor 704b is coupled to a torque sensor 744b. The torque sensor 744b is coupled to a transmission 706b which is coupled to the anvil 716. The transmission 706b comprises movable mechanical elements such as rotating elements and a closure member to control the movement of the anvil 716 from the open and closed positions. In one aspect, the motor 704b is coupled to a closure gear assembly, which includes a closure reduction gear set that is supported in meshing engagement with the closure spur gear. The torque sensor 744b provides a closure force feedback signal to the control circuit 710. The closure force feedback signal represents the closure force applied to the anvil 716. The position sensor 734 may be configured to provide the position of the closure member as a feedback signal to the control circuit 710. Additional sensors 738 in the end effector 702 may provide the closure force feedback signal to the control circuit 710. The pivotable anvil 716 is positioned opposite the staple cartridge 718. When ready to use, the control circuit 710 may provide a closure signal to the motor control 708b. In response to the closure signal, the motor 704b advances a closure member to grasp tissue between the anvil 716 and the staple cartridge 718.
In one aspect, the control circuit 710 is configured to rotate a shaft member such as the shaft 740 to rotate the end effector 702. The control circuit 710 provides a motor set point to a motor control 708c, which provides a drive signal to the motor 704c. The output shaft of the motor 704c is coupled to a torque sensor 744c. The torque sensor 744c is coupled to a transmission 706c which is coupled to the shaft 740. The transmission 706c comprises movable mechanical elements such as rotating elements to control the rotation of the shaft 740 clockwise or counterclockwise up to and over 360°. In one aspect, the motor 704c is coupled to the rotational transmission assembly, which includes a tube gear segment that is formed on (or attached to) the proximal end of the proximal closure tube for operable engagement by a rotational gear assembly that is operably supported on the tool mounting plate. The torque sensor 744c provides a rotation force feedback signal to the control circuit 710. The rotation force feedback signal represents the rotation force applied to the shaft 740. The position sensor 734 may be configured to provide the position of the closure member as a feedback signal to the control circuit 710. Additional sensors 738 such as a shaft encoder may provide the rotational position of the shaft 740 to the control circuit 710.
In one aspect, the control circuit 710 is configured to articulate the end effector 702. The control circuit 710 provides a motor set point to a motor control 708d, which provides a drive signal to the motor 704d. The output shaft of the motor 704d is coupled to a torque sensor 744d. The torque sensor 744d is coupled to a transmission 706d which is coupled to an articulation member 742a. The transmission 706d comprises movable mechanical elements such as articulation elements to control the articulation of the end effector 702 ±65°. In one aspect, the motor 704d is coupled to an articulation nut, which is rotatably journaled on the proximal end portion of the distal spine portion and is rotatably driven thereon by an articulation gear assembly. The torque sensor 744d provides an articulation force feedback signal to the control circuit 710. The articulation force feedback signal represents the articulation force applied to the end effector 702. Sensors 738, such as an articulation encoder, may provide the articulation position of the end effector 702 to the control circuit 710.
In another aspect, the articulation function of the robotic surgical system 700 may comprise two articulation members, or links, 742a, 742b. These articulation members 742a, 742b are driven by separate disks on the robot interface (the rack) which are driven by the two motors 708d, 708e. When the separate firing motor 704a is provided, each of articulation links 742a, 742b can be antagonistically driven with respect to the other link in order to provide a resistive holding motion and a load to the head when it is not moving and to provide an articulation motion as the head is articulated. The articulation members 742a, 742b attach to the head at a fixed radius as the head is rotated. Accordingly, the mechanical advantage of the push-and-pull link changes as the head is rotated. This change in the mechanical advantage may be more pronounced with other articulation link drive systems.
In one aspect, the one or more motors 704a-704e may comprise a brushed DC motor with a gearbox and mechanical links to a firing member, closure member, or articulation member. Another example includes electric motors 704a-704e that operate the movable mechanical elements such as the displacement member, articulation links, closure tube, and shaft. An outside influence is an unmeasured, unpredictable influence of things like tissue, surrounding bodies, and friction on the physical system. Such outside influence can be referred to as drag, which acts in opposition to one of electric motors 704a-704e. The outside influence, such as drag, may cause the operation of the physical system to deviate from a desired operation of the physical system.
In one aspect, the position sensor 734 may be implemented as an absolute positioning system. In one aspect, the position sensor 734 may comprise a magnetic rotary absolute positioning system implemented as an AS5055EQFT single-chip magnetic rotary position sensor available from Austria Microsystems, AG. The position sensor 734 may interface with the control circuit 710 to provide an absolute positioning system. The position may include multiple Hall-effect elements located above a magnet and coupled to a CORDIC processor, also known as the digit-by-digit method and Volder's algorithm, that is provided to implement a simple and efficient algorithm to calculate hyperbolic and trigonometric functions that require only addition, subtraction, bitshift, and table lookup operations.
In one aspect, the control circuit 710 may be in communication with one or more sensors 738. The sensors 738 may be positioned on the end effector 702 and adapted to operate with the robotic surgical instrument 700 to measure the various derived parameters such as the gap distance versus time, tissue compression versus time, and anvil strain versus time. The sensors 738 may comprise a magnetic sensor, a magnetic field sensor, a strain gauge, a load cell, a pressure sensor, a force sensor, a torque sensor, an inductive sensor such as an eddy current sensor, a resistive sensor, a capacitive sensor, an optical sensor, and/or any other suitable sensor for measuring one or more parameters of the end effector 702. The sensors 738 may include one or more sensors. The sensors 738 may be located on the staple cartridge 718 deck to determine tissue location using segmented electrodes. The torque sensors 744a-744e may be configured to sense force such as firing force, closure force, and/or articulation force, among others. Accordingly, the control circuit 710 can sense (1) the closure load experienced by the distal closure tube and its position, (2) the firing member at the rack and its position, (3) what portion of the staple cartridge 718 has tissue on it, and (4) the load and position on both articulation rods.
In one aspect, the one or more sensors 738 may comprise a strain gauge, such as a micro-strain gauge, configured to measure the magnitude of the strain in the anvil 716 during a clamped condition. The strain gauge provides an electrical signal whose amplitude varies with the magnitude of the strain. The sensors 738 may comprise a pressure sensor configured to detect a pressure generated by the presence of compressed tissue between the anvil 716 and the staple cartridge 718. The sensors 738 may be configured to detect impedance of a tissue section located between the anvil 716 and the staple cartridge 718 that is indicative of the thickness and/or fullness of tissue located therebetween.
In one aspect, the sensors 738 may be implemented as one or more limit switches, electromechanical devices, solid-state switches, Hall-effect devices, magneto-resistive (MR) devices, giant magneto-resistive (GMR) devices, magnetometers, among others. In other implementations, the sensors 738 may be implemented as solid-state switches that operate under the influence of light, such as optical sensors, IR sensors, ultraviolet sensors, among others. Still, the switches may be solid-state devices such as transistors (e.g., FET, junction FET, MOSFET, bipolar, and the like). In other implementations, the sensors 738 may include electrical conductorless switches, ultrasonic switches, accelerometers, and inertial sensors, among others.
In one aspect, the sensors 738 may be configured to measure forces exerted on the anvil 716 by the closure drive system. For example, one or more sensors 738 can be at an interaction point between the closure tube and the anvil 716 to detect the closure forces applied by the closure tube to the anvil 716. The forces exerted on the anvil 716 can be representative of the tissue compression experienced by the tissue section captured between the anvil 716 and the staple cartridge 718. The one or more sensors 738 can be positioned at various interaction points along the closure drive system to detect the closure forces applied to the anvil 716 by the closure drive system. The one or more sensors 738 may be sampled in real time during a clamping operation by the processor of the control circuit 710. The control circuit 710 receives real-time sample measurements to provide and analyze time-based information and assess, in real time, closure forces applied to the anvil 716.
In one aspect, a current sensor 736 can be employed to measure the current drawn by each of the motors 704a-704e. The force required to advance any of the movable mechanical elements such as the I-beam 714 corresponds to the current drawn by one of the motors 704a-704e. The force is converted to a digital signal and provided to the control circuit 710. The control circuit 710 can be configured to simulate the response of the actual system of the instrument in the software of the controller. A displacement member can be actuated to move an I-beam 714 in the end effector 702 at or near a target velocity. The robotic surgical instrument 700 can include a feedback controller, which can be one of any feedback controllers, including, but not limited to a PID, a state feedback, a linear-quadratic (LQR), and/or an adaptive controller, for example. The robotic surgical instrument 700 can include a power source to convert the signal from the feedback controller into a physical input such as case voltage, PWM voltage, frequency modulated voltage, current, torque, and/or force, for example. Additional details are disclosed in U.S. patent application Ser. No. 15/636,829, titled CLOSED LOOP VELOCITY CONTROL TECHNIQUES FOR ROBOTIC SURGICAL INSTRUMENT, filed Jun. 29, 2017, which is herein incorporated by reference in its entirety.
The position, movement, displacement, and/or translation of a linear displacement member, such as the I-beam 764, can be measured by an absolute positioning system, sensor arrangement, and position sensor 784. Because the I-beam 764 is coupled to a longitudinally movable drive member, the position of the I-beam 764 can be determined by measuring the position of the longitudinally movable drive member employing the position sensor 784. Accordingly, in the following description, the position, displacement, and/or translation of the !-beam 764 can be achieved by the position sensor 784 as described herein. A control circuit 760 may be programmed to control the translation of the displacement member, such as the I-beam 764. The control circuit 760, in some examples, may comprise one or more microcontrollers, microprocessors, or other suitable processors for executing instructions that cause the processor or processors to control the displacement member, e.g., the I-beam 764, in the manner described. In one aspect, a timer/counter 781 provides an output signal, such as the elapsed time or a digital count, to the control circuit 760 to correlate the position of the I-beam 764 as determined by the position sensor 784 with the output of the timer/counter 781 such that the control circuit 760 can determine the position of the I-beam 764 at a specific time (t) relative to a starting position. The timer/counter 781 may be configured to measure elapsed time, count external events, or time external events.
The control circuit 760 may generate a motor set point signal 772. The motor set point signal 772 may be provided to a motor controller 758. The motor controller 758 may comprise one or more circuits configured to provide a motor drive signal 774 to the motor 754 to drive the motor 754 as described herein. In some examples, the motor 754 may be a brushed DC electric motor. For example, the velocity of the motor 754 may be proportional to the motor drive signal 774. In some examples, the motor 754 may be a brushless DC electric motor and the motor drive signal 774 may comprise a PWM signal provided to one or more stator windings of the motor 754. Also, in some examples, the motor controller 758 may be omitted, and the control circuit 760 may generate the motor drive signal 774 directly.
The motor 754 may receive power from an energy source 762. The energy source 762 may be or include a battery, a super capacitor, or any other suitable energy source. The motor 754 may be mechanically coupled to the I-beam 764 via a transmission 756. The transmission 756 may include one or more gears or other linkage components to couple the motor 754 to the I-beam 764. A position sensor 784 may sense a position of the I-beam 764. The position sensor 784 may be or include any type of sensor that is capable of generating position data that indicate a position of the I-beam 764. In some examples, the position sensor 784 may include an encoder configured to provide a series of pulses to the control circuit 760 as the I-beam 764 translates distally and proximally. The control circuit 760 may track the pulses to determine the position of the I-beam 764. Other suitable position sensors may be used, including, for example, a proximity sensor. Other types of position sensors may provide other signals indicating motion of the I-beam 764. Also, in some examples, the position sensor 784 may be omitted. Where the motor 754 is a stepper motor, the control circuit 760 may track the position of the I-beam 764 by aggregating the number and direction of steps that the motor 754 has been instructed to execute. The position sensor 784 may be located in the end effector 752 or at any other portion of the instrument.
The control circuit 760 may be in communication with one or more sensors 788. The sensors 788 may be positioned on the end effector 752 and adapted to operate with the surgical instrument 750 to measure the various derived parameters such as gap distance versus time, tissue compression versus time, and anvil strain versus time. The sensors 788 may comprise a magnetic sensor, a magnetic field sensor, a strain gauge, a pressure sensor, a force sensor, an inductive sensor such as an eddy current sensor, a resistive sensor, a capacitive sensor, an optical sensor, and/or any other suitable sensor for measuring one or more parameters of the end effector 752. The sensors 788 may include one or more sensors.
The one or more sensors 788 may comprise a strain gauge, such as a micro-strain gauge, configured to measure the magnitude of the strain in the anvil 766 during a clamped condition. The strain gauge provides an electrical signal whose amplitude varies with the magnitude of the strain. The sensors 788 may comprise a pressure sensor configured to detect a pressure generated by the presence of compressed tissue between the anvil 766 and the staple cartridge 768. The sensors 788 may be configured to detect impedance of a tissue section located between the anvil 766 and the staple cartridge 768 that is indicative of the thickness and/or fullness of tissue located therebetween.
The sensors 788 may be is configured to measure forces exerted on the anvil 766 by a closure drive system. For example, one or more sensors 788 can be at an interaction point between a closure tube and the anvil 766 to detect the closure forces applied by a closure tube to the anvil 766. The forces exerted on the anvil 766 can be representative of the tissue compression experienced by the tissue section captured between the anvil 766 and the staple cartridge 768. The one or more sensors 788 can be positioned at various interaction points along the closure drive system to detect the closure forces applied to the anvil 766 by the closure drive system. The one or more sensors 788 may be sampled in real time during a clamping operation by a processor of the control circuit 760. The control circuit 760 receives real-time sample measurements to provide and analyze time-based information and assess, in real time, closure forces applied to the anvil 766.
A current sensor 786 can be employed to measure the current drawn by the motor 754. The force required to advance the I-beam 764 corresponds to the current drawn by the motor 754. The force is converted to a digital signal and provided to the control circuit 760.
The control circuit 760 can be configured to simulate the response of the actual system of the instrument in the software of the controller. A displacement member can be actuated to move an I-beam 764 in the end effector 752 at or near a target velocity. The surgical instrument 750 can include a feedback controller, which can be one of any feedback controllers, including, but not limited to a PID, a state feedback, LQR, and/or an adaptive controller, for example. The surgical instrument 750 can include a power source to convert the signal from the feedback controller into a physical input such as case voltage, PWM voltage, frequency modulated voltage, current, torque, and/or force, for example.
The actual drive system of the surgical instrument 750 is configured to drive the displacement member, cutting member, or I-beam 764, by a brushed DC motor with gearbox and mechanical links to an articulation and/or knife system. Another example is the electric motor 754 that operates the displacement member and the articulation driver, for example, of an interchangeable shaft assembly. An outside influence is an unmeasured, unpredictable influence of things like tissue, surrounding bodies and friction on the physical system. Such outside influence can be referred to as drag which acts in opposition to the electric motor 754. The outside influence, such as drag, may cause the operation of the physical system to deviate from a desired operation of the physical system.
Various example aspects are directed to a surgical instrument 750 comprising an end effector 752 with motor-driven surgical stapling and cutting implements. For example, a motor 754 may drive a displacement member distally and proximally along a longitudinal axis of the end effector 752. The end effector 752 may comprise a pivotable anvil 766 and, when configured for use, a staple cartridge 768 positioned opposite the anvil 766. A clinician may grasp tissue between the anvil 766 and the staple cartridge 768, as described herein. When ready to use the instrument 750, the clinician may provide a firing signal, for example by depressing a trigger of the instrument 750. In response to the firing signal, the motor 754 may drive the displacement member distally along the longitudinal axis of the end effector 752 from a proximal stroke begin position to a stroke end position distal of the stroke begin position. As the displacement member translates distally, an I-beam 764 with a cutting element positioned at a distal end, may cut the tissue between the staple cartridge 768 and the anvil 766.
In various examples, the surgical instrument 750 may comprise a control circuit 760 programmed to control the distal translation of the displacement member, such as the I-beam 764, for example, based on one or more tissue conditions. The control circuit 760 may be programmed to sense tissue conditions, such as thickness, either directly or indirectly, as described herein. The control circuit 760 may be programmed to select a firing control program based on tissue conditions. A firing control program may describe the distal motion of the displacement member. Different firing control programs may be selected to better treat different tissue conditions. For example, when thicker tissue is present, the control circuit 760 may be programmed to translate the displacement member at a lower velocity and/or with lower power. When thinner tissue is present, the control circuit 760 may be programmed to translate the displacement member at a higher velocity and/or with higher power.
In some examples, the control circuit 760 may initially operate the motor 754 in an open loop configuration for a first open loop portion of a stroke of the displacement member. Based on a response of the instrument 750 during the open loop portion of the stroke, the control circuit 760 may select a firing control program. The response of the instrument may include, a translation distance of the displacement member during the open loop portion, a time elapsed during the open loop portion, energy provided to the motor 754 during the open loop portion, a sum of pulse widths of a motor drive signal, etc. After the open loop portion, the control circuit 760 may implement the selected firing control program for a second portion of the displacement member stroke. For example, during the closed loop portion of the stroke, the control circuit 760 may modulate the motor 754 based on translation data describing a position of the displacement member in a closed loop manner to translate the displacement member at a constant velocity. Additional details are disclosed in U.S. patent application Ser. No. 15/720,852, titled SYSTEM AND METHODS FOR CONTROLLING A DISPLAY OF A SURGICAL INSTRUMENT, filed Sep. 29, 2017, which is herein incorporated by reference in its entirety.
In one aspect, sensors 788 may be implemented as a limit switch, electromechanical device, solid-state switches, Hall-effect devices, MR devices, GMR devices, magnetometers, among others. In other implementations, the sensors 638 may be solid-state switches that operate under the influence of light, such as optical sensors, IR sensors, ultraviolet sensors, among others. Still, the switches may be solid-state devices such as transistors (e.g., FET, junction FET, MOSFET, bipolar, and the like). In other implementations, the sensors 788 may include electrical conductorless switches, ultrasonic switches, accelerometers, and inertial sensors, among others.
In one aspect, the position sensor 784 may be implemented as an absolute positioning system comprising a magnetic rotary absolute positioning system implemented as an AS5055EQFT single-chip magnetic rotary position sensor available from Austria Microsystems, AG. The position sensor 784 may interface with the control circuit 760 to provide an absolute positioning system. The position may include multiple Hall-effect elements located above a magnet and coupled to a CORDIC processor, also known as the digit-by-digit method and Volder's algorithm, that is provided to implement a simple and efficient algorithm to calculate hyperbolic and trigonometric functions that require only addition, subtraction, bitshift, and table lookup operations.
In one aspect, the I-beam 764 may be implemented as a knife member comprising a knife body that operably supports a tissue cutting blade thereon and may further include anvil engagement tabs or features and channel engagement features or a foot. In one aspect, the staple cartridge 768 may be implemented as a standard (mechanical) surgical fastener cartridge. In one aspect, the RF cartridge 796 may be implemented as an RF cartridge. These and other sensors arrangements are described in commonly-owned U.S. patent application Ser. No. 15/628,175, titled TECHNIQUES FOR ADAPTIVE CONTROL OF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT, filed Jun. 20, 2017, which is herein incorporated by reference in its entirety.
The position, movement, displacement, and/or translation of a linear displacement member, such as the I-beam 764, can be measured by an absolute positioning system, sensor arrangement, and position sensor represented as position sensor 784. Because the I-beam 764 is coupled to the longitudinally movable drive member, the position of the I-beam 764 can be determined by measuring the position of the longitudinally movable drive member employing the position sensor 784. Accordingly, in the following description, the position, displacement, and/or translation of the I-beam 764 can be achieved by the position sensor 784 as described herein. A control circuit 760 may be programmed to control the translation of the displacement member, such as the I-beam 764, as described herein. The control circuit 760, in some examples, may comprise one or more microcontrollers, microprocessors, or other suitable processors for executing instructions that cause the processor or processors to control the displacement member, e.g., the I-beam 764, in the manner described. In one aspect, a timer/counter 781 provides an output signal, such as the elapsed time or a digital count, to the control circuit 760 to correlate the position of the I-beam 764 as determined by the position sensor 784 with the output of the timer/counter 781 such that the control circuit 760 can determine the position of the I-beam 764 at a specific time (t) relative to a starting position. The timer/counter 781 may be configured to measure elapsed time, count external events, or time external events.
The control circuit 760 may generate a motor set point signal 772. The motor set point signal 772 may be provided to a motor controller 758. The motor controller 758 may comprise one or more circuits configured to provide a motor drive signal 774 to the motor 754 to drive the motor 754 as described herein. In some examples, the motor 754 may be a brushed DC electric motor. For example, the velocity of the motor 754 may be proportional to the motor drive signal 774. In some examples, the motor 754 may be a brushless DC electric motor and the motor drive signal 774 may comprise a PWM signal provided to one or more stator windings of the motor 754. Also, in some examples, the motor controller 758 may be omitted, and the control circuit 760 may generate the motor drive signal 774 directly.
The motor 754 may receive power from an energy source 762. The energy source 762 may be or include a battery, a super capacitor, or any other suitable energy source. The motor 754 may be mechanically coupled to the I-beam 764 via a transmission 756. The transmission 756 may include one or more gears or other linkage components to couple the motor 754 to the I-beam 764. A position sensor 784 may sense a position of the I-beam 764. The position sensor 784 may be or include any type of sensor that is capable of generating position data that indicate a position of the I-beam 764. In some examples, the position sensor 784 may include an encoder configured to provide a series of pulses to the control circuit 760 as the I-beam 764 translates distally and proximally. The control circuit 760 may track the pulses to determine the position of the I-beam 764. Other suitable position sensors may be used, including, for example, a proximity sensor. Other types of position sensors may provide other signals indicating motion of the I-beam 764. Also, in some examples, the position sensor 784 may be omitted. Where the motor 754 is a stepper motor, the control circuit 760 may track the position of the I-beam 764 by aggregating the number and direction of steps that the motor has been instructed to execute. The position sensor 784 may be located in the end effector 792 or at any other portion of the instrument.
The control circuit 760 may be in communication with one or more sensors 788. The sensors 788 may be positioned on the end effector 792 and adapted to operate with the surgical instrument 790 to measure the various derived parameters such as gap distance versus time, tissue compression versus time, and anvil strain versus time. The sensors 788 may comprise a magnetic sensor, a magnetic field sensor, a strain gauge, a pressure sensor, a force sensor, an inductive sensor such as an eddy current sensor, a resistive sensor, a capacitive sensor, an optical sensor, and/or any other suitable sensor for measuring one or more parameters of the end effector 792. The sensors 788 may include one or more sensors.
The one or more sensors 788 may comprise a strain gauge, such as a micro-strain gauge, configured to measure the magnitude of the strain in the anvil 766 during a clamped condition. The strain gauge provides an electrical signal whose amplitude varies with the magnitude of the strain. The sensors 788 may comprise a pressure sensor configured to detect a pressure generated by the presence of compressed tissue between the anvil 766 and the staple cartridge 768. The sensors 788 may be configured to detect impedance of a tissue section located between the anvil 766 and the staple cartridge 768 that is indicative of the thickness and/or fullness of tissue located therebetween.
The sensors 788 may be is configured to measure forces exerted on the anvil 766 by the closure drive system. For example, one or more sensors 788 can be at an interaction point between a closure tube and the anvil 766 to detect the closure forces applied by a closure tube to the anvil 766. The forces exerted on the anvil 766 can be representative of the tissue compression experienced by the tissue section captured between the anvil 766 and the staple cartridge 768. The one or more sensors 788 can be positioned at various interaction points along the closure drive system to detect the closure forces applied to the anvil 766 by the closure drive system. The one or more sensors 788 may be sampled in real time during a clamping operation by a processor portion of the control circuit 760. The control circuit 760 receives real-time sample measurements to provide and analyze time-based information and assess, in real time, closure forces applied to the anvil 766.
A current sensor 786 can be employed to measure the current drawn by the motor 754. The force required to advance the I-beam 764 corresponds to the current drawn by the motor 754. The force is converted to a digital signal and provided to the control circuit 760.
An RF energy source 794 is coupled to the end effector 792 and is applied to the RF cartridge 796 when the RF cartridge 796 is loaded in the end effector 792 in place of the staple cartridge 768. The control circuit 760 controls the delivery of the RF energy to the RF cartridge 796.
Additional details are disclosed in U.S. patent application Ser. No. 15/636,096, titled SURGICAL SYSTEM COUPLABLE WITH STAPLE CARTRIDGE AND RADIO FREQUENCY CARTRIDGE, AND METHOD OF USING SAME, filed Jun. 28, 2017, which is herein incorporated by reference in its entirety.
In certain forms, the ultrasonic and electrosurgical drive signals may be provided simultaneously to distinct surgical instruments and/or to a single surgical instrument, such as the multifunction surgical instrument, having the capability to deliver both ultrasonic and electrosurgical energy to tissue. It will be appreciated that the electrosurgical signal, provided either to a dedicated electrosurgical instrument and/or to a combined multifunction ultrasonic/electrosurgical instrument may be either a therapeutic or sub-therapeutic level signal where the sub-therapeutic signal can be used, for example, to monitor tissue or instrument conditions and provide feedback to the generator. For example, the ultrasonic and RF signals can be delivered separately or simultaneously from a generator with a single output port in order to provide the desired output signal to the surgical instrument, as will be discussed in more detail below. Accordingly, the generator can combine the ultrasonic and electrosurgical RF energies and deliver the combined energies to the multifunction ultrasonic/electrosurgical instrument. Bipolar electrodes can be placed on one or both jaws of the end effector. One jaw may be driven by ultrasonic energy in addition to electrosurgical RF energy, working simultaneously. The ultrasonic energy may be employed to dissect tissue, while the electrosurgical RF energy may be employed for vessel sealing.
The non-isolated stage 804 may comprise a power amplifier 812 having an output connected to a primary winding 814 of the power transformer 806. In certain forms, the power amplifier 812 may comprise a push-pull amplifier. For example, the non-isolated stage 804 may further comprise a logic device 816 for supplying a digital output to a digital-to-analog converter (DAC) circuit 818, which in turn supplies a corresponding analog signal to an input of the power amplifier 812. In certain forms, the logic device 816 may comprise a programmable gate array (PGA), a FPGA, programmable logic device (PLD), among other logic circuits, for example. The logic device 816, by virtue of controlling the input of the power amplifier 812 via the DAC circuit 818, may therefore control any of a number of parameters (e.g., frequency, waveform shape, waveform amplitude) of drive signals appearing at the drive signal outputs 810a, 810b, 810c. In certain forms and as discussed below, the logic device 816, in conjunction with a processor (e.g., a DSP discussed below), may implement a number of DSP-based and/or other control algorithms to control parameters of the drive signals output by the generator 800.
Power may be supplied to a power rail of the power amplifier 812 by a switch-mode regulator 820, e.g., a power converter. In certain forms, the switch-mode regulator 820 may comprise an adjustable buck regulator, for example. The non-isolated stage 804 may further comprise a first processor 822, which in one form may comprise a DSP processor such as an Analog Devices ADSP-21469 SHARC DSP, available from Analog Devices, Norwood, Mass., for example, although in various forms any suitable processor may be employed. In certain forms the DSP processor 822 may control the operation of the switch-mode regulator 820 responsive to voltage feedback data received from the power amplifier 812 by the DSP processor 822 via an ADC circuit 824. In one form, for example, the DSP processor 822 may receive as input, via the ADC circuit 824, the waveform envelope of a signal (e.g., an RF signal) being amplified by the power amplifier 812. The DSP processor 822 may then control the switch-mode regulator 820 (e.g., via a PWM output) such that the rail voltage supplied to the power amplifier 812 tracks the waveform envelope of the amplified signal. By dynamically modulating the rail voltage of the power amplifier 812 based on the waveform envelope, the efficiency of the power amplifier 812 may be significantly improved relative to a fixed rail voltage amplifier schemes.
In certain forms, the logic device 816, in conjunction with the DSP processor 822, may implement a digital synthesis circuit such as a direct digital synthesizer control scheme to control the waveform shape, frequency, and/or amplitude of drive signals output by the generator 800. In one form, for example, the logic device 816 may implement a DDS control algorithm by recalling waveform samples stored in a dynamically updated lookup table (LUT), such as a RAM LUT, which may be embedded in an FPGA. This control algorithm is particularly useful for ultrasonic applications in which an ultrasonic transducer, such as an ultrasonic transducer, may be driven by a clean sinusoidal current at its resonant frequency. Because other frequencies may excite parasitic resonances, minimizing or reducing the total distortion of the motional branch current may correspondingly minimize or reduce undesirable resonance effects. Because the waveform shape of a drive signal output by the generator 800 is impacted by various sources of distortion present in the output drive circuit (e.g., the power transformer 806, the power amplifier 812), voltage and current feedback data based on the drive signal may be input into an algorithm, such as an error control algorithm implemented by the DSP processor 822, which compensates for distortion by suitably pre-distorting or modifying the waveform samples stored in the LUT on a dynamic, ongoing basis (e.g., in real time). In one form, the amount or degree of pre-distortion applied to the LUT samples may be based on the error between a computed motional branch current and a desired current waveform shape, with the error being determined on a sample-by-sample basis. In this way, the pre-distorted LUT samples, when processed through the drive circuit, may result in a motional branch drive signal having the desired waveform shape (e.g., sinusoidal) for optimally driving the ultrasonic transducer. In such forms, the LUT waveform samples will therefore not represent the desired waveform shape of the drive signal, but rather the waveform shape that is required to ultimately produce the desired waveform shape of the motional branch drive signal when distortion effects are taken into account.
The non-isolated stage 804 may further comprise a first ADC circuit 826 and a second ADC circuit 828 coupled to the output of the power transformer 806 via respective isolation transformers 830, 832 for respectively sampling the voltage and current of drive signals output by the generator 800. In certain forms, the ADC circuits 826, 828 may be configured to sample at high speeds (e.g., 80 mega samples per second (MSPS)) to enable oversampling of the drive signals. In one form, for example, the sampling speed of the ADC circuits 826, 828 may enable approximately 200× (depending on frequency) oversampling of the drive signals. In certain forms, the sampling operations of the ADC circuit 826, 828 may be performed by a single ADC circuit receiving input voltage and current signals via a two-way multiplexer. The use of high-speed sampling in forms of the generator 800 may enable, among other things, calculation of the complex current flowing through the motional branch (which may be used in certain forms to implement DDS-based waveform shape control described above), accurate digital filtering of the sampled signals, and calculation of real power consumption with a high degree of precision. Voltage and current feedback data output by the ADC circuits 826, 828 may be received and processed (e.g., first-in-first-out (FIFO) buffer, multiplexer) by the logic device 816 and stored in data memory for subsequent retrieval by, for example, the DSP processor 822. As noted above, voltage and current feedback data may be used as input to an algorithm for pre-distorting or modifying LUT waveform samples on a dynamic and ongoing basis. In certain forms, this may require each stored voltage and current feedback data pair to be indexed based on, or otherwise associated with, a corresponding LUT sample that was output by the logic device 816 when the voltage and current feedback data pair was acquired. Synchronization of the LUT samples and the voltage and current feedback data in this manner contributes to the correct timing and stability of the pre-distortion algorithm.
In certain forms, the voltage and current feedback data may be used to control the frequency and/or amplitude (e.g., current amplitude) of the drive signals. In one form, for example, voltage and current feedback data may be used to determine impedance phase. The frequency of the drive signal may then be controlled to minimize or reduce the difference between the determined impedance phase and an impedance phase setpoint (e.g., 0°), thereby minimizing or reducing the effects of harmonic distortion and correspondingly enhancing impedance phase measurement accuracy. The determination of phase impedance and a frequency control signal may be implemented in the DSP processor 822, for example, with the frequency control signal being supplied as input to a DDS control algorithm implemented by the logic device 816.
In another form, for example, the current feedback data may be monitored in order to maintain the current amplitude of the drive signal at a current amplitude setpoint. The current amplitude setpoint may be specified directly or determined indirectly based on specified voltage amplitude and power setpoints. In certain forms, control of the current amplitude may be implemented by control algorithm, such as, for example, a proportional-integral-derivative (PID) control algorithm, in the DSP processor 822. Variables controlled by the control algorithm to suitably control the current amplitude of the drive signal may include, for example, the scaling of the LUT waveform samples stored in the logic device 816 and/or the full-scale output voltage of the DAC circuit 818 (which supplies the input to the power amplifier 812) via a DAC circuit 834.
The non-isolated stage 804 may further comprise a second processor 836 for providing, among other things user interface (UI) functionality. In one form, the UI processor 836 may comprise an Atmel AT91SAM9263 processor having an ARM 926EJ-S core, available from Atmel Corporation, San Jose, Calif., for example. Examples of UI functionality supported by the UI processor 836 may include audible and visual user feedback, communication with peripheral devices (e.g., via a USB interface), communication with a foot switch, communication with an input device (e.g., a touch screen display) and communication with an output device (e.g., a speaker). The UI processor 836 may communicate with the DSP processor 822 and the logic device 816 (e.g., via SPI buses). Although the UI processor 836 may primarily support UI functionality, it may also coordinate with the DSP processor 822 to implement hazard mitigation in certain forms. For example, the UI processor 836 may be programmed to monitor various aspects of user input and/or other inputs (e.g., touch screen inputs, foot switch inputs, temperature sensor inputs) and may disable the drive output of the generator 800 when an erroneous condition is detected.
In certain forms, both the DSP processor 822 and the UI processor 836, for example, may determine and monitor the operating state of the generator 800. For the DSP processor 822, the operating state of the generator 800 may dictate, for example, which control and/or diagnostic processes are implemented by the DSP processor 822. For the UI processor 836, the operating state of the generator 800 may dictate, for example, which elements of a UI (e.g., display screens, sounds) are presented to a user. The respective DSP and UI processors 822, 836 may independently maintain the current operating state of the generator 800 and recognize and evaluate possible transitions out of the current operating state. The DSP processor 822 may function as the master in this relationship and determine when transitions between operating states are to occur. The UI processor 836 may be aware of valid transitions between operating states and may confirm if a particular transition is appropriate. For example, when the DSP processor 822 instructs the UI processor 836 to transition to a specific state, the UI processor 836 may verify that requested transition is valid. In the event that a requested transition between states is determined to be invalid by the UI processor 836, the UI processor 836 may cause the generator 800 to enter a failure mode.
The non-isolated stage 804 may further comprise a controller 838 for monitoring input devices (e.g., a capacitive touch sensor used for turning the generator 800 on and off, a capacitive touch screen). In certain forms, the controller 838 may comprise at least one processor and/or other controller device in communication with the UI processor 836. In one form, for example, the controller 838 may comprise a processor (e.g., a Meg168 8-bit controller available from Atmel) configured to monitor user input provided via one or more capacitive touch sensors. In one form, the controller 838 may comprise a touch screen controller (e.g., a QT5480 touch screen controller available from Atmel) to control and manage the acquisition of touch data from a capacitive touch screen.
In certain forms, when the generator 800 is in a “power off” state, the controller 838 may continue to receive operating power (e.g., via a line from a power supply of the generator 800, such as the power supply 854 discussed below). In this way, the controller 838 may continue to monitor an input device (e.g., a capacitive touch sensor located on a front panel of the generator 800) for turning the generator 800 on and off. When the generator 800 is in the power off state, the controller 838 may wake the power supply (e.g., enable operation of one or more DC/DC voltage converters 856 of the power supply 854) if activation of the “on/off” input device by a user is detected. The controller 838 may therefore initiate a sequence for transitioning the generator 800 to a “power on” state. Conversely, the controller 838 may initiate a sequence for transitioning the generator 800 to the power off state if activation of the “on/off” input device is detected when the generator 800 is in the power on state. In certain forms, for example, the controller 838 may report activation of the “on/off” input device to the UI processor 836, which in turn implements the necessary process sequence for transitioning the generator 800 to the power off state. In such forms, the controller 838 may have no independent ability for causing the removal of power from the generator 800 after its power on state has been established.
In certain forms, the controller 838 may cause the generator 800 to provide audible or other sensory feedback for alerting the user that a power on or power off sequence has been initiated. Such an alert may be provided at the beginning of a power on or power off sequence and prior to the commencement of other processes associated with the sequence.
In certain forms, the isolated stage 802 may comprise an instrument interface circuit 840 to, for example, provide a communication interface between a control circuit of a surgical instrument (e.g., a control circuit comprising handpiece switches) and components of the non-isolated stage 804, such as, for example, the logic device 816, the DSP processor 822, and/or the UI processor 836. The instrument interface circuit 840 may exchange information with components of the non-isolated stage 804 via a communication link that maintains a suitable degree of electrical isolation between the isolated and non-isolated stages 802, 804, such as, for example, an IR-based communication link. Power may be supplied to the instrument interface circuit 840 using, for example, a low-dropout voltage regulator powered by an isolation transformer driven from the non-isolated stage 804.
In one form, the instrument interface circuit 840 may comprise a logic circuit 842 (e.g., logic circuit, programmable logic circuit, PGA, FPGA, PLD) in communication with a signal conditioning circuit 844. The signal conditioning circuit 844 may be configured to receive a periodic signal from the logic circuit 842 (e.g., a 2 kHz square wave) to generate a bipolar interrogation signal having an identical frequency. The interrogation signal may be generated, for example, using a bipolar current source fed by a differential amplifier. The interrogation signal may be communicated to a surgical instrument control circuit (e.g., by using a conductive pair in a cable that connects the generator 800 to the surgical instrument) and monitored to determine a state or configuration of the control circuit. The control circuit may comprise a number of switches, resistors, and/or diodes to modify one or more characteristics (e.g., amplitude, rectification) of the interrogation signal such that a state or configuration of the control circuit is uniquely discernable based on the one or more characteristics. In one form, for example, the signal conditioning circuit 844 may comprise an ADC circuit for generating samples of a voltage signal appearing across inputs of the control circuit resulting from passage of interrogation signal therethrough. The logic circuit 842 (or a component of the non-isolated stage 804) may then determine the state or configuration of the control circuit based on the ADC circuit samples.
In one form, the instrument interface circuit 840 may comprise a first data circuit interface 846 to enable information exchange between the logic circuit 842 (or other element of the instrument interface circuit 840) and a first data circuit disposed in or otherwise associated with a surgical instrument. In certain forms, for example, a first data circuit may be disposed in a cable integrally attached to a surgical instrument handpiece or in an adaptor for interfacing a specific surgical instrument type or model with the generator 800. The first data circuit may be implemented in any suitable manner and may communicate with the generator according to any suitable protocol, including, for example, as described herein with respect to the first data circuit. In certain forms, the first data circuit may comprise a non-volatile storage device, such as an EEPROM device. In certain forms, the first data circuit interface 846 may be implemented separately from the logic circuit 842 and comprise suitable circuitry (e.g., discrete logic devices, a processor) to enable communication between the logic circuit 842 and the first data circuit. In other forms, the first data circuit interface 846 may be integral with the logic circuit 842.
In certain forms, the first data circuit may store information pertaining to the particular surgical instrument with which it is associated. Such information may include, for example, a model number, a serial number, a number of operations in which the surgical instrument has been used, and/or any other type of information. This information may be read by the instrument interface circuit 840 (e.g., by the logic circuit 842), transferred to a component of the non-isolated stage 804 (e.g., to logic device 816, DSP processor 822, and/or UI processor 836) for presentation to a user via an output device and/or for controlling a function or operation of the generator 800. Additionally, any type of information may be communicated to the first data circuit for storage therein via the first data circuit interface 846 (e.g., using the logic circuit 842). Such information may comprise, for example, an updated number of operations in which the surgical instrument has been used and/or dates and/or times of its usage.
As discussed previously, a surgical instrument may be detachable from a handpiece (e.g., the multifunction surgical instrument may be detachable from the handpiece) to promote instrument interchangeability and/or disposability. In such cases, conventional generators may be limited in their ability to recognize particular instrument configurations being used and to optimize control and diagnostic processes accordingly. The addition of readable data circuits to surgical instruments to address this issue is problematic from a compatibility standpoint, however. For example, designing a surgical instrument to remain backwardly compatible with generators that lack the requisite data reading functionality may be impractical due to, for example, differing signal schemes, design complexity, and cost. Forms of instruments discussed herein address these concerns by using data circuits that may be implemented in existing surgical instruments economically and with minimal design changes to preserve compatibility of the surgical instruments with current generator platforms.
Additionally, forms of the generator 800 may enable communication with instrument-based data circuits. For example, the generator 800 may be configured to communicate with a second data circuit contained in an instrument (e.g., the multifunction surgical instrument). In some forms, the second data circuit may be implemented in a many similar to that of the first data circuit described herein. The instrument interface circuit 840 may comprise a second data circuit interface 848 to enable this communication. In one form, the second data circuit interface 848 may comprise a tri-state digital interface, although other interfaces may also be used. In certain forms, the second data circuit may generally be any circuit for transmitting and/or receiving data. In one form, for example, the second data circuit may store information pertaining to the particular surgical instrument with which it is associated. Such information may include, for example, a model number, a serial number, a number of operations in which the surgical instrument has been used, and/or any other type of information.
In some forms, the second data circuit may store information about the electrical and/or ultrasonic properties of an associated ultrasonic transducer, end effector, or ultrasonic drive system. For example, the first data circuit may indicate a burn-in frequency slope, as described herein. Additionally or alternatively, any type of information may be communicated to second data circuit for storage therein via the second data circuit interface 848 (e.g., using the logic circuit 842). Such information may comprise, for example, an updated number of operations in which the instrument has been used and/or dates and/or times of its usage. In certain forms, the second data circuit may transmit data acquired by one or more sensors (e.g., an instrument-based temperature sensor). In certain forms, the second data circuit may receive data from the generator 800 and provide an indication to a user (e.g., a light emitting diode indication or other visible indication) based on the received data.
In certain forms, the second data circuit and the second data circuit interface 848 may be configured such that communication between the logic circuit 842 and the second data circuit can be effected without the need to provide additional conductors for this purpose (e.g., dedicated conductors of a cable connecting a handpiece to the generator 800). In one form, for example, information may be communicated to and from the second data circuit using a one-wire bus communication scheme implemented on existing cabling, such as one of the conductors used transmit interrogation signals from the signal conditioning circuit 844 to a control circuit in a handpiece. In this way, design changes or modifications to the surgical instrument that might otherwise be necessary are minimized or reduced. Moreover, because different types of communications implemented over a common physical channel can be frequency-band separated, the presence of a second data circuit may be “invisible” to generators that do not have the requisite data reading functionality, thus enabling backward compatibility of the surgical instrument.
In certain forms, the isolated stage 802 may comprise at least one blocking capacitor 850-1 connected to the drive signal output 810b to prevent passage of DC current to a patient. A single blocking capacitor may be required to comply with medical regulations or standards, for example. While failure in single-capacitor designs is relatively uncommon, such failure may nonetheless have negative consequences. In one form, a second blocking capacitor 850-2 may be provided in series with the blocking capacitor 850-1, with current leakage from a point between the blocking capacitors 850-1, 850-2 being monitored by, for example, an ADC circuit 852 for sampling a voltage induced by leakage current. The samples may be received by the logic circuit 842, for example. Based changes in the leakage current (as indicated by the voltage samples), the generator 800 may determine when at least one of the blocking capacitors 850-1, 850-2 has failed, thus providing a benefit over single-capacitor designs having a single point of failure.
In certain forms, the non-isolated stage 804 may comprise a power supply 854 for delivering DC power at a suitable voltage and current. The power supply may comprise, for example, a 400 W power supply for delivering a 48 VDC system voltage. The power supply 854 may further comprise one or more DC/DC voltage converters 856 for receiving the output of the power supply to generate DC outputs at the voltages and currents required by the various components of the generator 800. As discussed above in connection with the controller 838, one or more of the DC/DC voltage converters 856 may receive an input from the controller 838 when activation of the “on/off” input device by a user is detected by the controller 838 to enable operation of, or wake, the DC/DC voltage converters 856.
The generator 900 comprises a processor 902 coupled to a waveform generator 904. The processor 902 and waveform generator 904 are configured to generate a variety of signal waveforms based on information stored in a memory coupled to the processor 902, not shown for clarity of disclosure. The digital information associated with a waveform is provided to the waveform generator 904 which includes one or more DAC circuits to convert the digital input into an analog output. The analog output is fed to an amplifier 1106 for signal conditioning and amplification. The conditioned and amplified output of the amplifier 906 is coupled to a power transformer 908. The signals are coupled across the power transformer 908 to the secondary side, which is in the patient isolation side. A first signal of a first energy modality is provided to the surgical instrument between the terminals labeled ENERGY1 and RETURN. A second signal of a second energy modality is coupled across a capacitor 910 and is provided to the surgical instrument between the terminals labeled ENERGY2 and RETURN. It will be appreciated that more than two energy modalities may be output and thus the subscript “n” may be used to designate that up to n ENERGYn terminals may be provided, where n is a positive integer greater than 1. It also will be appreciated that up to “n” return paths RETURNn may be provided without departing from the scope of the present disclosure.
A first voltage sensing circuit 912 is coupled across the terminals labeled ENERGY1 and the RETURN path to measure the output voltage therebetween. A second voltage sensing circuit 924 is coupled across the terminals labeled ENERGY2 and the RETURN path to measure the output voltage therebetween. A current sensing circuit 914 is disposed in series with the RETURN leg of the secondary side of the power transformer 908 as shown to measure the output current for either energy modality. If different return paths are provided for each energy modality, then a separate current sensing circuit should be provided in each return leg. The outputs of the first and second voltage sensing circuits 912, 924 are provided to respective isolation transformers 916, 922 and the output of the current sensing circuit 914 is provided to another isolation transformer 918. The outputs of the isolation transformers 916, 928, 922 in the on the primary side of the power transformer 908 (non-patient isolated side) are provided to a one or more ADC circuit 926. The digitized output of the ADC circuit 926 is provided to the processor 902 for further processing and computation. The output voltages and output current feedback information can be employed to adjust the output voltage and current provided to the surgical instrument and to compute output impedance, among other parameters. Input/output communications between the processor 902 and patient isolated circuits is provided through an interface circuit 920. Sensors also may be in electrical communication with the processor 902 by way of the interface circuit 920.
In one aspect, the impedance may be determined by the processor 902 by dividing the output of either the first voltage sensing circuit 912 coupled across the terminals labeled ENERGY1/RETURN or the second voltage sensing circuit 924 coupled across the terminals labeled ENERGY2/RETURN by the output of the current sensing circuit 914 disposed in series with the RETURN leg of the secondary side of the power transformer 908. The outputs of the first and second voltage sensing circuits 912, 924 are provided to separate isolations transformers 916, 922 and the output of the current sensing circuit 914 is provided to another isolation transformer 916. The digitized voltage and current sensing measurements from the ADC circuit 926 are provided the processor 902 for computing impedance. As an example, the first energy modality ENERGY1 may be ultrasonic energy and the second energy modality ENERGY2 may be RF energy. Nevertheless, in addition to ultrasonic and bipolar or monopolar RF energy modalities, other energy modalities include irreversible and/or reversible electroporation and/or microwave energy, among others. Also, although the example illustrated in
As shown in
Additional details are disclosed in U.S. Patent Application Publication No. 2017/0086914, titled TECHNIQUES FOR OPERATING GENERATOR FOR DIGITALLY GENERATING ELECTRICAL SIGNAL WAVEFORMS AND SURGICAL INSTRUMENTS, which published on Mar. 30, 2017, which is herein incorporated by reference in its entirety.
As used throughout this description, the term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some aspects they might not. The communication module may implement any of a number of wireless or wired communication standards or protocols, including but not limited to W-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, long term evolution (LTE), Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, Bluetooth, Ethernet derivatives thereof, as well as any other wireless and wired protocols that are designated as 3G, 4G, 5G, and beyond. The computing module may include a plurality of communication modules. For instance, a first communication module may be dedicated to shorter range wireless communications such as Wi-Fi and Bluetooth and a second communication module may be dedicated to longer range wireless communications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, and others.
As used herein a processor or processing unit is an electronic circuit which performs operations on some external data source, usually memory or some other data stream. The term is used herein to refer to the central processor (central processing unit) in a system or computer systems (especially systems on a chip (SoCs)) that combine a number of specialized “processors.”
As used herein, a system on a chip or system on chip (SoC or SOC) is an integrated circuit (also known as an “IC” or “chip”) that integrates all components of a computer or other electronic systems. It may contain digital, analog, mixed-signal, and often radio-frequency functions—all on a single substrate. A SoC integrates a microcontroller (or microprocessor) with advanced peripherals like graphics processing unit (GPU), W-Fi module, or coprocessor. A SoC may or may not contain built-in memory.
As used herein, a microcontroller or controller is a system that integrates a microprocessor with peripheral circuits and memory. A microcontroller (or MCU for microcontroller unit) may be implemented as a small computer on a single integrated circuit. It may be similar to a SoC; an SoC may include a microcontroller as one of its components. A microcontroller may contain one or more core processing units (CPUs) along with memory and programmable input/output peripherals. Program memory in the form of Ferroelectric RAM, NOR flash or OTP ROM is also often included on chip, as well as a small amount of RAM. Microcontrollers may be employed for embedded applications, in contrast to the microprocessors used in personal computers or other general purpose applications consisting of various discrete chips.
As used herein, the term controller or microcontroller may be a stand-alone IC or chip device that interfaces with a peripheral device. This may be a link between two parts of a computer or a controller on an external device that manages the operation of (and connection with) that device.
Any of the processors or microcontrollers described herein, may be implemented by any single core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In one aspect, the processor may be an LM4F230H5QR ARM Cortex-M4F Processor Core, available from Texas Instruments, for example, comprising on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle serial random access memory (SRAM), internal read-only memory (ROM) loaded with StellarisWare® software, 2 KB electrically erasable programmable read-only memory (EEPROM), one or more pulse width modulation (PWM) modules, one or more quadrature encoder inputs (QEI) analog, one or more 12-bit Analog-to-Digital Converters (ADC) with 12 analog input channels, details of which are available for the product datasheet.
In one aspect, the processor may comprise a safety controller comprising two controller-based families such as TMS570 and RM4x known under the trade name Hercules ARM Cortex R4, also by Texas Instruments. The safety controller may be configured specifically for IEC 61508 and ISO 26262 safety critical applications, among others, to provide advanced integrated safety features while delivering scalable performance, connectivity, and memory options.
Modular devices include the modules (as described in connection with
In addition, surgical instruments 7012 may comprise transceivers for data transmission to and from their corresponding surgical hubs 7006 (which may also comprise transceivers). Combinations of surgical instruments 7012 and corresponding hubs 7006 may indicate particular locations, such as operating theaters in healthcare facilities (e.g., hospitals), for providing medical operations. For example, the memory of a surgical hub 7006 may store location data. As shown in
Based on connections to various surgical hubs 7006 via the network 7001, the cloud 7004 can aggregate data from specific data generated by various surgical instruments 7012 and their corresponding hubs 7006. Such aggregated data may be stored within the aggregated medical databases 7012 of the cloud 7004. In particular, the cloud 7004 may advantageously perform data analysis and operations on the aggregated data to yield insights and/or perform functions that individual hubs 7006 could not achieve on their own. To this end, as shown in
The particular cloud computing system configuration described in the present disclosure is specifically designed to address various issues arising in the context of medical operations and procedures performed using medical devices, such as the surgical instruments 7012, 112. In particular, the surgical instruments 7012 may be digital surgical devices configured to interact with the cloud 7004 for implementing techniques to improve the performance of surgical operations. Various surgical instruments 7012 and/or surgical hubs 7006 may comprise touch controlled user interfaces such that clinicians may control aspects of interaction between the surgical instruments 7012 and the cloud 7004. Other suitable user interfaces for control such as auditory controlled user interfaces can also be used.
For example, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons. In particular, pair data sets generated from operations of surgical instruments 7012 can comprise applying a binary classification, e.g., a bleeding or a non-bleeding event. More generally, the binary classification may be characterized as either a desirable event (e.g., a successful surgical procedure) or an undesirable event (e.g., a misfired or misused surgical instrument 7012). The aggregated self-describing data may correspond to individual data received from various groups or subgroups of surgical hubs 7006. Accordingly, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs 7006. To this end, the processors 7008 can be operationally coupled to the hub applications 7014 and aggregated medical data databases 7011 for executing the data analytics modules 7034. The data collection and aggregation module 7022 may store the aggregated organized data into the aggregated medical data databases 2212.
The resource optimization module 7020 can be configured to analyze this aggregated data to determine an optimal usage of resources for a particular or group of healthcare facilities. For example, the resource optimization module 7020 may determine an optimal order point of surgical stapling instruments 7012 for a group of healthcare facilities based on corresponding predicted demand of such instruments 7012. The resource optimization module 7020 might also assess the resource usage or other operational configurations of various healthcare facilities to determine whether resource usage could be improved. Similarly, the recommendations module 7030 can be configured to analyze aggregated organized data from the data collection and aggregation module 7022 to provide recommendations. For example, the recommendations module 7030 could recommend to healthcare facilities (e.g., medical service providers such as hospitals) that a particular surgical instrument 7012 should be upgraded to an improved version based on a higher than expected error rate, for example. Additionally, the recommendations module 7030 and/or resource optimization module 7020 could recommend better supply chain parameters such as product reorder points and provide suggestions of different surgical instrument 7012, uses thereof, or procedure steps to improve surgical outcomes. The healthcare facilities can receive such recommendations via corresponding surgical hubs 7006. More specific recommendations regarding parameters or configurations of various surgical instruments 7012 can also be provided. Hubs 7006 and/or surgical instruments 7012 each could also have display screens that display data or recommendations provided by the cloud 7004.
The patient outcome analysis module 7028 can analyze surgical outcomes associated with currently used operational parameters of surgical instruments 7012. The patient outcome analysis module 7028 may also analyze and assess other potential operational parameters. In this connection, the recommendations module 7030 could recommend using these other potential operational parameters based on yielding better surgical outcomes, such as better sealing or less bleeding. For example, the recommendations module 7030 could transmit recommendations to a surgical 7006 regarding when to use a particular cartridge for a corresponding stapling surgical instrument 7012. Thus, the cloud-based analytics system, while controlling for common variables, may be configured to analyze the large collection of raw data and to provide centralized recommendations over multiple healthcare facilities (advantageously determined based on aggregated data). For example, the cloud-based analytics system could analyze, evaluate, and/or aggregate data based on type of medical practice, type of patient, number of patients, geographic similarity between medical providers, which medical providers/facilities use similar types of instruments, etc., in a way that no single healthcare facility alone would be able to analyze independently. The control program updating module 7026 could be configured to implement various surgical instrument 7012 recommendations when corresponding control programs are updated. For example, the patient outcome analysis module 7028 could identify correlations linking specific control parameters with successful (or unsuccessful) results. Such correlations may be addressed when updated control programs are transmitted to surgical instruments 7012 via the control program updating module 7026. Updates to instruments 7012 that are transmitted via a corresponding hub 7006 may incorporate aggregated performance data that was gathered and analyzed by the data collection and aggregation module 7022 of the cloud 7004. Additionally, the patient outcome analysis module 7028 and recommendations module 7030 could identify improved methods of using instruments 7012 based on aggregated performance data.
The cloud-based analytics system may include security features implemented by the cloud 7004. These security features may be managed by the authorization and security module 7024. Each surgical hub 7006 can have associated unique credentials such as username, password, and other suitable security credentials. These credentials could be stored in the memory 7010 and be associated with a permitted cloud access level. For example, based on providing accurate credentials, a surgical hub 7006 may be granted access to communicate with the cloud to a predetermined extent (e.g., may only engage in transmitting or receiving certain defined types of information). To this end, the aggregated medical data databases 7011 of the cloud 7004 may comprise a database of authorized credentials for verifying the accuracy of provided credentials. Different credentials may be associated with varying levels of permission for interaction with the cloud 7004, such as a predetermined access level for receiving the data analytics generated by the cloud 7004. Furthermore, for security purposes, the cloud could maintain a database of hubs 7006, instruments 7012, and other devices that may comprise a “black list” of prohibited devices. In particular, a surgical hubs 7006 listed on the black list may not be permitted to interact with the cloud, while surgical instruments 7012 listed on the black list may not have functional access to a corresponding hub 7006 and/or may be prevented from fully functioning when paired to its corresponding hub 7006. Additionally or alternatively, the cloud 7004 may flag instruments 7012 based on incompatibility or other specified criteria. In this manner, counterfeit medical devices and improper reuse of such devices throughout the cloud-based analytics system can be identified and addressed.
The surgical instruments 7012 may use wireless transceivers to transmit wireless signals that may represent, for example, authorization credentials for access to corresponding hubs 7006 and the cloud 7004. Wired transceivers may also be used to transmit signals. Such authorization credentials can be stored in the respective memory devices of the surgical instruments 7012. The authorization and security module 7024 can determine whether the authorization credentials are accurate or counterfeit. The authorization and security module 7024 may also dynamically generate authorization credentials for enhanced security. The credentials could also be encrypted, such as by using hash based encryption. Upon transmitting proper authorization, the surgical instruments 7012 may transmit a signal to the corresponding hubs 7006 and ultimately the cloud 7004 to indicate that the instruments 7012 are ready to obtain and transmit medical data. In response, the cloud 7004 may transition into a state enabled for receiving medical data for storage into the aggregated medical data databases 7011. This data transmission readiness could be indicated by a light indicator on the instruments 7012, for example. The cloud 7004 can also transmit signals to surgical instruments 7012 for updating their associated control programs. The cloud 7004 can transmit signals that are directed to a particular class of surgical instruments 7012 (e.g., electrosurgical instruments) so that software updates to control programs are only transmitted to the appropriate surgical instruments 7012. Moreover, the cloud 7004 could be used to implement system wide solutions to address local or global problems based on selective data transmission and authorization credentials. For example, if a group of surgical instruments 7012 are identified as having a common manufacturing defect, the cloud 7004 may change the authorization credentials corresponding to this group to implement an operational lockout of the group.
The cloud-based analytics system may allow for monitoring multiple healthcare facilities (e.g., medical facilities like hospitals) to determine improved practices and recommend changes (via the recommendations module 2030, for example) accordingly. Thus, the processors 7008 of the cloud 7004 can analyze data associated with an individual healthcare facility to identify the facility and aggregate the data with other data associated with other healthcare facilities in a group. Groups could be defined based on similar operating practices or geographical location, for example. In this way, the cloud 7004 may provide healthcare facility group wide analysis and recommendations. The cloud-based analytics system could also be used for enhanced situational awareness. For example, the processors 7008 may predictively model the effects of recommendations on the cost and effectiveness for a particular facility (relative to overall operations and/or various medical procedures). The cost and effectiveness associated with that particular facility can also be compared to a corresponding local region of other facilities or any other comparable facilities.
The data sorting and prioritization module 7032 may prioritize and sort data based on criticality (e.g., the severity of a medical event associated with the data, unexpectedness, suspiciousness). This sorting and prioritization may be used in conjunction with the functions of the other data analytics modules 7034 described above to improve the cloud-based analytics and operations described herein. For example, the data sorting and prioritization module 7032 can assign a priority to the data analysis performed by the data collection and aggregation module 7022 and patient outcome analysis modules 7028. Different prioritization levels can result in particular responses from the cloud 7004 (corresponding to a level of urgency) such as escalation for an expedited response, special processing, exclusion from the aggregated medical data databases 7011, or other suitable responses. Moreover, if necessary, the cloud 7004 can transmit a request (e.g., a push message) through the hub application servers for additional data from corresponding surgical instruments 7012. The push message can result in a notification displayed on the corresponding hubs 7006 for requesting supporting or additional data. This push message may be required in situations in which the cloud detects a significant irregularity or outlier and the cloud cannot determine the cause of the irregularity. The central servers 7013 may be programmed to trigger this push message in certain significant circumstances, such as when data is determined to be different from an expected value beyond a predetermined threshold or when it appears security has been comprised, for example.
Additional example details for the various functions described are provided in the ensuing descriptions below. Each of the various descriptions may utilize the cloud architecture as described in
Aspects of the present disclosure are presented for a cloud-based analytics system, communicatively coupled to a plurality of hubs and smart medical instruments, and configured to provide customized recommendations to localized medical care facilities regarding usage of medical supplies and other resources to improve efficiency and optimize resource allocation. A medical care facility, such as a hospital or medical clinic, may develop a set of practices for procuring, using, and disposing of various medical supplies that are often derived from routines and traditions maintained over time. The behaviors of a medical facility typically are risk-averse, and generally would be hesitant to adopt new and better practices unless and until convincingly shown of a better practice. Similarly, even if a better usage or efficiency model has been developed in a nearby facility, it is difficult for a local facility to adopt the improved practice because 1) each facility may be more natively resistant to change from the outside and 2) there are many unknowns for how or why the improved practice works in the nearby facility in relation to what the local facility does instead. Furthermore, even if a medical facility desired to improve its practices, it may be unable to do so optimally because it lacks enough knowledge from other similarly situated facilities, either in its region, according to a similar size, and/or according to similar practices or patients, and the like.
To help facilitate the dissemination of improved practices across multiple medical facilities, it would be desirable if a common source could have knowledge of the contexts from multiple medical facilities and be able to determine what changes should be made for any particular medical facility, based on the knowledge of the practices of any or all of the multiple facilities.
In some aspects, a cloud-based system communicatively coupled to knowledge centers in a medical facility, such as one or more medical hubs, may be configured to aggregate medical resource usage data from multiple medical facilities. The cloud-based system may then correlate the medical resource usage data with outcomes from those facilities, and may be able to derive various patterns within the data. For example, in some aspects, the cloud-based system may find which hospitals generate the least amount of waste per unit cost, based on an aggregation of all waste and procurement data obtained from medical facilities in a wide geographic region (e.g., all surgery centers in Japan). The cloud-based system may be configured to identify which medical facility produced the least amount of waste per unit cost, and then may analyze what practices differentiate that medical facility. If a trend is found, the cloud-based system may disseminate this information to all of the similarly situated medical facilities to improve their practices. This analysis may help improve inventory management, throughput efficiency, or overall efficiency of a medical facility. The improved inventory management may help surgical devices and other medical resources be utilized at their peak performance levels for longer periods of time, compared to if resources were badly managed, and therefore medical devices may be continuously used while they are older and more worn down.
In general, the cloud-based system may be configured to aggregate data from multiple medical facilities, something that no single facility alone would be able to accomplish on its own. Furthermore, the cloud-based system may be configured to analyze the large collection of data, controlling for common variables, such as type of practice, type of patient, number of patients, geographic similarity, which facilities use similar types of instruments, etc., that no single facility alone would be able to analyze on its own.
In this way, the cloud-based system of the present disclosure may be able to find more accurate causalities that lead to best practices at a particular facility, which can then be disseminated to all of the other facilities. Furthermore, the cloud-based system may be able to provide the data from all of the disparate sources that no single facility may be able to do on its own.
Referring to
The cloud system may be configured to identify wasted product that was gathered and not used or gathered and used in a manner that was not beneficial to the patient or the surgery. To do this, the cloud system may record in memory all records of inventory intake and disposal. During each intake, the inventory may be scanned and entered, and the bar codes of each inventory item may identify what type of product it is, as an example. In some aspects, smart disposal bins may be utilized to automatically tabulate when a product is being disposed of. These may be connected to the cloud system ultimately, either through one or more surgical hubs or through a separate inventory management system throughout the entire facility. Each facility may be tracked by its location, for example through a set GPS coordinate, inputted address or the like. This data may be organized in memory using one or more databases with various meta data associated with it, such as date and time of use, location of origin, type of procedure used for if applicable, cost per item, expiration date if applicable, and so on.
In addition, the cloud system may be configured to identify misfired or misused product and tracking of where the product was used, and may archive these results. For example, each surgical instrument communicatively coupled to a surgical hub may transmit a record of when the instrument was fired, such as to fire a staple or apply ultrasonic energy. Each record may be transmitted through the instrument and recorded at the cloud system ultimately. The action by the instrument may be tied with an outcome, either at that instant or with an overall outcome stating whether the procedure was successful or not. The action may be associated with a precise timestamp that places the action at an exact point during a surgery, where all of the actions of the surgery are also automatically recorded to the cloud, including start and end times of the surgery. This enables all of the human medical care workers to focus on their respective duties during surgery, rather than worry about an exact instance an action of a medical instrument occurred. The recordings of the medical instruments can be used to identify what products may be wasted during surgery, and the cloud system may be configured to also identify usage trends in this way.
In some aspects, the cloud system may be configured to perform trending analysis of the product tied to the overall length or amount of the product to identify short fires, or discarded product. For example, the cloud system may place the use of a product within a known period of when a surgical procedure is occurring, with a time stamp. The cloud system may then record an amount of resources utilized during that procedure, and may compare the materials used in that procedure with similarly situated procedures performed elsewhere. Out of this, several conclusions may be reached by the cloud system. For example, the cloud system may provide recommendations of a mix that provides smaller portions or an alternative usage that results in less wasted product. As another example, the cloud system may provide a suggestion or specified protocol change of specialized kits that would assemble the product in a manner more aligned to the detected institution usage. As yet another example, the cloud system may provide a suggestion or a change in protocol for alternative product mixes that would be more aligned to the detected usage and therefore should result in less wasted product. As yet another example, the cloud system may provide a recommendation on how to adjust a medical procedure during surgery based on timings of actions occurring before or after an event that typically results in wasteful resources, such as misfirings or multiple firings, based on identifying a correlation or pattern that actions during surgery occurring within a certain time interval relative to a prior action tend to result in wasteful actions. These analyses may be derived in part using algorithms that attempt to optimize the available resources with the rates of their disposals, taking into account various factors such as misfirings, native practices of the surgeons or the facility at large, and so forth.
Still referring to
In some aspects, the cloud system may be configured to analyze the inventory usage of any and all medical products and conduct procurement management for when to acquire new product. The cloud system may optimize the utilization of inventory space to determine how best to utilize what space is available, in light of rates of usage for certain products compared to others. It may often be the case that inventory is not closely monitored in terms of how long a product remains in storage. If certain products are utilized at slower rates, but there is a large amount of it, it may be determined that the storage space is allocated poorly. Therefore, the cloud system may better apportion the storage space to reflect actual resource usage.
To improve in this area, in some aspects, the cloud system may for example, identify missing or insufficient product within an operating room (OR) for a specified procedure. The cloud system may then provide an alert or notification or transmit data to display that deficiency at the surgical hub in the OR. As another example, when a product is used in the OR, it may communicate its usage information to the cloud, such as activate a sensor or activation identification. The product may be registered with a scan or a power on switch. Analysis of this information for a given hospital coupled with its ordering information, may eventually inform the supply status and can enable ordering recommendations. This may occur automatically, once the cloud system registers that products are being used in the OR, or through other means.
In some aspects, device utilization within a procedure is monitored by the cloud system and compared for a given segment (e.g., individual surgeon, individual hospital, network of hospitals, region, etc.) against device utilization for similar procedures in other segments. Recommendations are presented to optimize utilization based on unit resource used or expenditure spent to supply such resource. In general, the cloud system may focus on a comparison of product utilization between different institutions that it is connected with.
The example graph 7206 provides a visual depiction of an example trend or pattern that the cloud may derive from examining the resource and outcome data, according to some aspects. In this example, the cloud system may have analyzed resource and outcome data of number of stapler firings and their relation to performance in surgery. The cloud system may have gathered the data from multiple medical facilities, and multiple surgeons within each facility, based on automatically recorded firing data during each surgery that is generated directly from the operation of the surgical staplers themselves. The performance outcomes may be based on post-op examinations and evaluations, and/or immediate outcomes during surgery, such as whether there is a bleeding event or a successful wound closure. Based on all of the data, trends may be determined, and here, it may be discovered that there is a small window of the number of firings that results in the best performance outcomes, at interval “a” as shown. The magnitude of this performance compared to the most common number of firings is shown as interval “b.” Because the number of firings that results in the best outcomes may not be what is commonly practiced, it may not be readily easily to have discovered these outcomes without the aggregation and analytical abilities of the cloud system.
As another example: cartridge type, color, and adjunct usage that are monitored for sleeve gastrectomy procedures for individual surgeons within the same hospital may be obtained. The data may reveal an average procedure cost for one surgeon is higher for this surgeon when compared to others within the same hospital, yet short term patient outcomes remain the same. The hospital is then informed and is encouraged to look into differences in device utilization, techniques, etc. in search of optimizing costs potentially through the elimination of adjuncts.
In some aspects, the cloud system may also identify specialty cases. For example, specific cost information provided within the hospital, including OR time, device utilization, and staff, may be identified. These aspects may be unique to a particular OR, or facility. The cloud system may be configured to suggest efficiencies in OR time usage (scheduling), device inventory, etc. across specialties (orthopedics, thoracic, colorectal, bariatric, etc.) for these specialty cases.
In some aspects, the cloud system may also be configured to compare cost-benefit of robotic surgery vs traditional methods, such as laparoscopic procedures for given procedure type. The cloud system may compare device costs, OR time, patient discharge times, efficacy of the procedure done by the robot vs performed by surgeons exclusively, and the like.
According to some aspects of the cloud system, whereas the above disclosure focuses on a determination of efficiency (i.e., value) and optimizing based on that, here, this section centers around on identifying which local practices may be best disseminated to other similarly situated medical facilities.
A medical care facility, such as a hospital or medical clinic, may develop a set of practices for how to utilize medical devices for aiding medical procedures that are often derived from routines and traditions maintained over time. The behaviors of a medical facility typically are risk-averse, and generally would be hesitant to adopt new and better practices unless and until convincingly shown of a better practice. Similarly, even if a better practice for utilizing a device or for adjusting a procedure has been developed in a nearby facility, it is difficult for a local facility to adopt the improved practice because 1) each facility may be more natively resistant to change from the outside and 2) there are many unknowns for how or why the improved practice works in the nearby facility in relation to what the local facility does instead. Furthermore, even if a medical facility desired to improve its practices, it may be unable to do so optimally because it lacks enough knowledge from other similarly situated facilities, either in its region, according to a similar size, and/or according to similar practices or patients, and the like.
To help facilitate the dissemination of improved practices across multiple medical facilities, it would be desirable if a common source could have knowledge of the contexts from multiple medical facilities and be able to determine what changes should be made for any particular medical facility, based on the knowledge of the practices of any or all of the multiple facilities.
In some aspects, a cloud-based system communicatively coupled to knowledge centers in a medical facility, such as one or more medical hubs, may be configured to aggregate resource utilization data and patient outcomes from multiple medical facilities. The cloud-based system may then correlate the resource utilization data with the outcomes from those facilities, and may be able to derive various patterns within the data. For example, in some aspects, the cloud-based system may find which hospitals produce better outcomes for a particular type of procedure, based on an aggregation of all the patient outcome data for that particular procedure collected in a wide geographic region (e.g., all surgery centers in Germany). The cloud-based system may be configured to identify which medical facility produced a better procedural outcome compared to the average across the geographic region, and then may analyze what differences in that procedure occur in that medical facility. If a trend is found and one or more differences are identified, the cloud-based system may disseminate this information to all of the similarly situated medical facilities to improve their practices.
In general, the cloud-based system may be configured to aggregate data from multiple medical facilities, something that no single facility alone would be able to accomplish on its own. Furthermore, the cloud-based system may be configured to analyze the large collection of data, controlling for common variables, such as type of practice, type of patient, number of patients, geographic similarity, which facilities use similar types of instruments, etc., that no single facility alone would be able to analyze on its own.
In this way, the cloud-based system of the present disclosure may be able to find more accurate causalities that give rise to best practices at a particular facility, which can then be disseminated to all of the other facilities. Furthermore, the cloud-based system may be able to provide the data from all of the disparate sources that no single facility may be able to do on its own.
The cloud system may be configured to generate conclusions about the efficacy of any local facility in a number of ways. For example, the cloud system may determine if a local treatment facility is using a product mixture or usage that differs from the larger community and their outcomes are superior. The cloud system may then correlate the differences and highlight them for use in other facilities, other surgical hub, or in clinical sales as some examples. In general, this information may be disseminated widely in a way that no single facility may have had access or knowledge of, including the facility that practiced this improve procedure.
As another example, the cloud system may determine if the local facility has equal to or inferior outcomes to the larger community. The cloud system may then correlate suggestions and provide that information back to the local facility as recommendations. The system may display data showing their performance in relation to others, and may also display suggestions on what that facility is doing compared to what everybody else is doing. Again, the local facility may not even know they have an inefficiency in that respect, nor may everybody else realize they are utilizing their resources more efficiently, and thus nobody would ever know to examine these issues without the cloud system having a bigger picture of all of the data.
These suggestions can come in various forms. For example, the cloud system may provide recommendations at the purchasing level that suggest improvements in cost for similar outcomes. As another example, the cloud system may provide recommendations at the OR level when the procedure is being planned and outfitted as the less desirable products are being pulled suggest other techniques and product mixes that would be in line with the broader community which is achieving higher outcomes. As yet another example, the cloud system may display outcomes comparison needs to account for surgeon experience, possibly through a count of similar cases performed by that surgeon from cloud data. In some aspects, the learning curve of an individual may be reported against an aggregated larger dataset, as expectation of improved outcomes, or of surgeon performance relative to peers in obtaining a steady state outcome level.
As another example, a cartridge type and color are monitored for lobectomy procedures for individual surgeons within the same hospital. The data reveals average cost for one surgeon is higher on average for this surgeon, yet average length of stay is less. The hospital is informed by the cloud system and is encouraged to look into differences in device utilization, techniques, etc. in search of improving patient outcomes.
In some aspects, the cloud system may also be configured to provide predictive modeling of changes to procedures, product mixes, and timing for a given localized population or for the general population as a whole. The predictive modeling may be used to assess impact on resource utilization, resource efficiency, and resource performance, as some examples.
As another example, chart 7404 in
As another example, chart 7402 in
In general, to perform the predictive modeling, the cloud system may combine its knowledge of the exact steps to perform a procedure, what instruments may be used to perform each step, and its aggregated data for how each instrument performs each particular step. A surgeon may not have the combination of such knowledge in order to provide such an assessment alone. The predictive modeling therefore may be the result of continued monitoring and acquisition of data across multiple facilities, the likes of which would not be possible without the cloud system.
In some aspects, the cloud system may also derive the distilled information from multiple sources (e.g., HUB data collection sources, literature, etc.) to identify the optimal procedure technique. Various other examples for how predictive modeling may be utilized include:
(1) sigmoidectomy: multi-quadrant surgery; which is the best order of operations, etc.;
(2) RYGB: what is the ideal limb length, etc. based on the circumstances for this patient;
(3) Lobectomy: how many and which lymph nodes should be removed; and
(4) VSG: Bougie size and distance from pylorus.
In some aspects, when a suggestion is made to a surgeon, the surgeon is given the option to decline future suggestions like this, or to continue. In addition, through interface with the hub, the surgeon may inquire to the cloud system additional information to inform his or her decision. For example, the surgeon may want to isolate the times to a more localized set of data, such as the particular facility or a certain demographic that better caters to the patient undergoing the surgery. The data may change, for example, if the patient is a child or the patient is a woman.
Similar to the above section, the cloud-based system may also be configured to monitor smart instrument configurations and, more generally, configurations that utilize multiple smart instruments, such as an operating room preparing for surgery. For similar reasons as described above, such as to improve medical efficacy and efficiency, it may be useful to compare a procedural setup at any particular medical facility to aggregate data pertaining to the procedural setups at multiple other medical facilities.
The cloud-based system of the present disclosure may be configured to aggregate data pertaining to smart medical instrument configurations and operating room (OR) setups that utilize multiple smart medical instruments. The smart medical instruments may include: manual devices that are communicatively coupled to a medical data tower and are configured to generate sensor data; and robotic instruments that perform procedures in a more automated fashion. The cloud-based system may be configured to detect irregularities in an OR setup, either pertaining to what devices are present in the room and/or what materials are used to create a product mix for a medical procedure. The irregularities may be based on comparing the materials and equipment present in the OR with other setups from other medical facilities for a similar situation. The cloud system may then generate a change in firmware, software, or other settings and transmit those changes to the surgical devices like a device update.
In this way, the cloud-based system of the present disclosure may be able to identify errors and find more accurate causalities that give rise to best practices at a particular facility, which can then be disseminated to all of the other facilities. Furthermore, the cloud-based system may be able to provide the data from all of the disparate sources that no single facility may be able to do on its own. This can lead to safe and more efficient operating room procedures and medical practices in general.
In some aspects, the cloud system may be configured to provide recommendations of instrument configurations, and even generate the appropriate device settings changes, to customize performance to that of a pre-specified user.
For example, the cloud system may focus on a surgical device user or surgeon based on a comparison of current usage of a device with the historic trends of a larger data set. As some examples, the cloud system may provide recommendations of what type of cartridge to use based on what the user has previously used for the particular procedure or just what the particular surgeon desires in general. The cloud system may access data based on the particular surgeon, the type of procedure, and the type of instruments used in order to make this determination.
As another example, the cloud system may provide a recommendation based on an identified anatomy indicated in a display of the cartridge. As another example, the cloud system may provide a recommendation by referring to a baseline surgical device clamping and firing speed, based on local previous usage data that it has stored in its memory.
As yet another example, the cloud system may conduct a comparison of current device tissue interaction against a historical average for the same surgeon, or for the same step in the same procedure for a segment of surgeons in the database. The cloud system again may have access to all steps used to perform a procedure, and may access a catalog of all data when performing a particular step in a procedure across all surgeons who have ever performed that procedure in its network. The recommendation may also come from an analysis of how the current surgical device has been observed to interact with tissue historically. This type of analysis may be useful because it is often not the case that large amounts of live patient data can be collected for how a surgical device interacts precisely with the tissue. Furthermore, a surgeon typically knows only his or her experience, and does not have outside knowledge of what other surgeons experience for the same procedure. The cloud, on the other hand, is capable of collecting all of this data and providing new insights that any individual surgeon would not know alone.
As another example: In stapling, more than one of the following are known: cartridge color, stapler type, procedure, procedure step, patient information, clamp force over time, prior firing information, end effector deformations, etc. This information is compared against a historical average for a similar dataset. The current situation is compared against this average, informing the user about the nature of the current firing.
As another example, the cloud system may be configured to provide a recommendation of ultrasonic blade lengths or capacities based on likely to encounter vascular structures in a procedure. Similar to what is described above in reference to
In some aspects, the cloud system is also configured to provide recommendations to the staff about which devices to pull for an upcoming procedure. These recommendations may be based on a combination of surgeon preference (pick list) against historical device utilization rates for the same procedures performed by some segment of the larger database, as well as average recommendations or utilizations across different facilities that produce the best results. The data may be obtained by pairing good outcomes with the metadata, such as what devices were used to achieve those good outcomes. Recommendations can be influenced by other factors, including patient information, demographic data, etc.
Relatedly, in some aspects, the cloud system may also provide identification of pulled instruments that might not be the preferred device for a given procedure. The blacklisting of sorts can more clearly eliminate any obviously flaw uses of devices to help surgeons make the best decisions. This data may be obtained from manufacturer input, analysis of poor outcomes, specific input provided to the cloud system, and so on.
In addition, based on interrogating tissue for properties (elasticity, impedance, perfusion rate), a specific device with a given parameter set (clamp preload) could be suggested to be used from current stock in inventory by the cloud system. Some of the metadata associated with the outcomes of past procedures may include a description of the type of tissue being operated on, and an associated description of the physical characteristics of that tissue. The cloud system may then draw trends or patterns based on different types of procedures, but having in common all procedures that deal with similar types of tissue. This kind of analysis may be used as a secondary recommendation, when a new or unknown procedure must take place and new suggestions are welcome. If the recommendation is accepted, the cloud system may be configured to generate the change in parameters and transmit them to the interconnected medical device, through the surgical hub, to make the medical device readily available for use in the adjusted procedure.
In some aspects, the device setup recommendations can include suggestions of adjuncts for devices based on the pre-surgery imaging or locally collected data during the beginning of a procedure. That is, this suggestion of adjuncts may be for use on or with devices based on the local correlation of use to efficacy of the device. As an example, based on a given procedure, surgeon, and patient information, bleeding in a case must be tightly controlled, and therefore the cloud system may conclude that a buttress is recommended on all staple firings.
In some aspects, the cloud system may also be configured to provide awareness of any newly-launched products that are available and suitable for operation as well as instructions for use (IFU). The data may be gathered from one or more surgical hubs, or from direct factory input for the newly-launched products. The cloud system can download the information and make the information displayable to multiple medical hubs across multiple facilities.
In some aspects, regarding any of the above examples for recommendations being provided by the cloud system, the cloud system may also conversely provide alerts or other signals when a device or suggested setup is not followed or is disregarded. The cloud system may be configured to access procedural data from a surgical hub during a surgical procedure, for example. The surgical hub may collect data for what type of devices are in use during a procedure. The cloud system may monitor the progress of the procedure by verifying if an accepted method or device is used in the correct or prescribed order for the procedure. If there is a deviation, in that a particular device is not expected or a step is missed, the cloud system may send an alert to the surgical hub that a particular device is not being used properly, as an example. This would occur in real time, as the timing of the procedure is important for the patient's safety.
In some aspects, the cloud-based system may also be configured to provide recommendations or automatically adjust surgical instrument settings to account for specific differences at a medical facility. While there are a number of similarities that can be normalized across multiple facilities, there may also be particular differences that should be accounted for. For example, patient demographic differences, patient physiological differences more native to a local population, procedural differences—for example preferences by each individual surgeon—and region specific instrument availability or other differences may inspire certain adjustments to be made at any particular medical facility.
The cloud-based system of the present disclosure may be configured to aggregate not only data pertaining to smart medical instrument configurations and operating room (OR) setups that utilize multiple smart medical instruments, but also data that highlight specific differences that may be unique to that region or that particular medical facility. The cloud-based system may then factor in adjustments to device settings or recommendations to changes in procedures based on these differences. For example, the cloud-based system may first provide a baseline recommendation for how a smart instrument should be used, based on best practices discovered in the aggregate data. Then, the cloud-based system may augment the recommendation to account for one or more unique differences specific to a medical facility. Examples of these differences are described above. The cloud-based system may be made aware of what demographics and patient data gave rise to the optimal baseline procedure, and then compare the local facility demographics and patient data against that. The cloud-based system may develop or extrapolate a correlation from that baseline setting in order to develop an adjustment or offset that accounts for the differences in demographics and patient data.
In this way, the cloud-based system of the present disclosure may be able to make optimal adjustments specific to each medical facility or even specific to each operating room, or surgeon. The adjustments may offer improved performance that take into account the observed best practices as well as any unique differences.
In some aspects, the cloud system may be configured to provide changes to instrument variation of usage to improve outcomes. For example, the cloud system may determine a localized undesirable effect that is due to a specific manner of utilizing a surgical device.
In some aspects, the cloud system may be configured to communicate the deviation to the specific user and the recommendation of a differing technique or usage to improve outcomes from the specific device. The cloud system may transmit the data for display at the surgical hub to illustrate what changes ought to be made.
As an example: A stapler configured with a means to sense the force required to clamp the device transmits data indicating that the clamp force is still rapidly changing (viscoelastic creep) when the surgeon initiates firing of the staple, and it is observed that the staple line bleeds more often than expected. The cloud system and/or device is able to communicate a need to wait longer (e.g., 15 seconds) before firing the device to improve outcomes. This may be based on performing the statistical analysis described in
In some aspects, the cloud system may also be configured for intentional deployment of control algorithms to devices with an in-use criteria meeting specific criteria. For regional differences, the cloud system may adjust the control algorithms of various surgical devices. A different amount of force may be applied to a device for patients in a different demographic, for example. As another example, surgeons may have different uses for a type of surgical device, and control algorithms can be adjusted to account for this. The cloud system may be configured to send out a wide area update to a device, and may target the regional and specific instrument IDs which allow for targeted updates to their control programs.
In some aspects, the cloud system may provide for coding of the serial numbers of sales units and/or individual devices, which enables updated control programs to be pushed to a specific device or specific groups of devices based on meeting a specific criteria or threshold.
In addition, according to some aspects, the cloud system may be configured to perform analysis of peri-operative data against outcomes data seeking correlations that identify exceptional results (positive and negative). The analysis may be performed at multiple levels (e.g., individual, hospital, and geographic (e.g., city, county, state, country, etc.) filters). Furthermore, regional corroboration of improved outcomes may be target for only a limited geographic area, as it is known that the changes occur only within a limited area. The ability to tune devices to regional preferences, techniques, and surgical preferences may allow for nuanced improvements for regionally specific areas.
In addition to directly changing instrument settings, the cloud system may also be configured to provide recommendations on different instruments or equivalent device suggestions due to regional availability. That is, an equivalent suggestion to a device to perform a particular function may be recommended by the cloud system, in the event a device is lacking and a particular region has an excess or general availability of the different device that may be used to serve an equivalent purpose.
For example, the cloud system may determine that PPH hemorrhoid stapling devices or curved cutter 30 devices are only available in Italy due to a unique procedure configuration or teaching hospital procedure design. As another example, the cloud system may determine that there is an Asia-specific TX and open vascular stapler use due to cost sensitivity, lack of laparoscopic adoption, and teaching hospital preferred techniques and patient thoracic cavity size. As another example, the cloud system may provide awareness messages to OR staff of sub-standard knock-off products available in a certain region. This data may be derived from an ingestion of information from multiple sources, such as inputs provided by experts and doctors, and employing machine learning and natural language processing to interpret trends and news related to a local area.
In some aspects, the cloud system may also be configured to provide a surgical hub decision tree and local suggestions of post-operative care, based on data processed during the procedure and Cloud Analytics trending of results or performance of the devices aggregated from larger population sets.
In some aspects, the cloud system may provide update-able decision trees for post-operative care suggestions, based on device measured situational usage. The post-operative care decisions may initially be derived from traditionally known responses that doctors would normally recommend. Once additional data becomes available, say from aggregating types of post-operative care from other facilities, or from analyzing new types of care from literature or from research on new surgical devices, the decision can be updated by the cloud system. The decision tree may be displayable at a surgical hub and in a graphical form.
In using this decision tree, feedback can be provided for each node to state how effective the current solutions are. The data may be inputted based on whatever feedback patients may provide. A doctor or data admin need not perform any analysis at the time, but the cloud system can aggregate all of the data and observe what trends may arise. Feedback can then be provided to update the decision tree.
In some aspects, the cloud system may incorporate operative data & device performance to propose post-operative monitoring & activities. For example, various patient measures may change what decisions in post-operative care should be taken. These measurements can include but are not limited to: (a) blood pressure; (b) low hematocrit; (c) PTT (partial thromboplastin time); (d) INR (international normalized ratio); (e) Oxygen saturation; (f) Ventilation changes; and (g) X-Ray data.
As another example, anesthesia protocol can dictate what post-operative decisions should be taken. This may account for: (a) any fluids administered; (b) Anesthesia time; and (3) Medications, as some non-limiting examples.
As another example, the types of medications may also play a role. The application of Warfarin is one notable example. A patient post-operatively has abnormal PTT and INR, for example. Because the patient is on Warfarin, potential treatments could include vitamin K, factor 7, or the delivery of plasma (fpp). Plavix can be another example. A patient post-operatively has abnormal PTT and INR. Because patient is on Plavix, potential treatments for Warfarin would be ineffective. Deliver platelets instead may be the suggestion in the decision tree.
As a fourth example, post-operative instructions may be provided that are dependent on the type of procedure. Some non-limiting examples include colorectal time to solid food (motility); and (b) time to physical activity & PT. These varying decisions can be reflected in the decision tree, and all of the types of branching decisions may be stored in the cloud system and updated when additional data is gained from any connected facility.
Situational awareness is the ability of some aspects of a surgical system to determine or infer information related to a surgical procedure from data received from databases and/or instruments. The information can include the type of procedure being undertaken, the type of tissue being operated on, or the body cavity that is the subject of the procedure. With the contextual information related to the surgical procedure, the surgical system can, for example, improve the manner in which it controls the modular devices (e.g., a robotic arm and/or robotic surgical tool) that are connected to it and provide contextualized information or suggestions to the surgeon during the course of the surgical procedure. Situational awareness may be applied to perform and/or improve any of the functions described in
Referring now to
The situationally aware surgical hub 106, 206 receives data from the data sources throughout the course of the surgical procedure, including data generated each time medical personnel utilize a modular device that is paired with the surgical hub 106, 206. The surgical hub 106, 206 can receive this data from the paired modular devices and other data sources and continually derive inferences (i.e., contextual information) about the ongoing procedure as new data is received, such as which step of the procedure is being performed at any given time. The situational awareness system of the surgical hub 106, 206 is able to, for example, record data pertaining to the procedure for generating reports, verify the steps being taken by the medical personnel, provide data or prompts (e.g., via a display screen) that may be pertinent for the particular procedural step, adjust modular devices based on the context (e.g., activate monitors, adjust the field of view (FOV) of the medical imaging device, or change the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument), and take any other such action described above.
As the first step 5202 in this illustrative procedure, the hospital staff members retrieve the patient's EMR from the hospital's EMR database. Based on select patient data in the EMR, the surgical hub 106, 206 determines that the procedure to be performed is a thoracic procedure.
Second step 5204, the staff members scan the incoming medical supplies for the procedure. The surgical hub 106, 206 cross-references the scanned supplies with a list of supplies that are utilized in various types of procedures and confirms that the mix of supplies corresponds to a thoracic procedure. Further, the surgical hub 106, 206 is also able to determine that the procedure is not a wedge procedure (because the incoming supplies either lack certain supplies that are necessary for a thoracic wedge procedure or do not otherwise correspond to a thoracic wedge procedure).
Third step 5206, the medical personnel scan the patient band via a scanner that is communicably connected to the surgical hub 106, 206. The surgical hub 106, 206 can then confirm the patient's identity based on the scanned data.
Fourth step 5208, the medical staff turns on the auxiliary equipment. The auxiliary equipment being utilized can vary according to the type of surgical procedure and the techniques to be used by the surgeon, but in this illustrative case they include a smoke evacuator, insufflator, and medical imaging device. When activated, the auxiliary equipment that are modular devices can automatically pair with the surgical hub 106, 206 that is located within a particular vicinity of the modular devices as part of their initialization process. The surgical hub 106, 206 can then derive contextual information about the surgical procedure by detecting the types of modular devices that pair with it during this pre-operative or initialization phase. In this particular example, the surgical hub 106, 206 determines that the surgical procedure is a VATS procedure based on this particular combination of paired modular devices. Based on the combination of the data from the patient's EMR, the list of medical supplies to be used in the procedure, and the type of modular devices that connect to the hub, the surgical hub 106, 206 can generally infer the specific procedure that the surgical team will be performing. Once the surgical hub 106, 206 knows what specific procedure is being performed, the surgical hub 106, 206 can then retrieve the steps of that procedure from a memory or from the cloud and then cross-reference the data it subsequently receives from the connected data sources (e.g., modular devices and patient monitoring devices) to infer what step of the surgical procedure the surgical team is performing.
Fifth step 5210, the staff members attach the EKG electrodes and other patient monitoring devices to the patient. The EKG electrodes and other patient monitoring devices are able to pair with the surgical hub 106, 206. As the surgical hub 106, 206 begins receiving data from the patient monitoring devices, the surgical hub 106, 206 thus confirms that the patient is in the operating theater.
Sixth step 5212, the medical personnel induce anesthesia in the patient. The surgical hub 106, 206 can infer that the patient is under anesthesia based on data from the modular devices and/or patient monitoring devices, including EKG data, blood pressure data, ventilator data, or combinations thereof, for example. Upon completion of the sixth step 5212, the pre-operative portion of the lung segmentectomy procedure is completed and the operative portion begins.
Seventh step 5214, the patient's lung that is being operated on is collapsed (while ventilation is switched to the contralateral lung). The surgical hub 106, 206 can infer from the ventilator data that the patient's lung has been collapsed, for example. The surgical hub 106, 206 can infer that the operative portion of the procedure has commenced as it can compare the detection of the patient's lung collapsing to the expected steps of the procedure (which can be accessed or retrieved previously) and thereby determine that collapsing the lung is the first operative step in this particular procedure.
Eighth step 5216, the medical imaging device (e.g., a scope) is inserted and video from the medical imaging device is initiated. The surgical hub 106, 206 receives the medical imaging device data (i.e., video or image data) through its connection to the medical imaging device. Upon receipt of the medical imaging device data, the surgical hub 106, 206 can determine that the laparoscopic portion of the surgical procedure has commenced. Further, the surgical hub 106, 206 can determine that the particular procedure being performed is a segmentectomy, as opposed to a lobectomy (note that a wedge procedure has already been discounted by the surgical hub 106, 206 based on data received at the second step 5204 of the procedure). The data from the medical imaging device 124 (
Ninth step 5218, the surgical team begins the dissection step of the procedure. The surgical hub 106, 206 can infer that the surgeon is in the process of dissecting to mobilize the patient's lung because it receives data from the RF or ultrasonic generator indicating that an energy instrument is being fired. The surgical hub 106, 206 can cross-reference the received data with the retrieved steps of the surgical procedure to determine that an energy instrument being fired at this point in the process (i.e., after the completion of the previously discussed steps of the procedure) corresponds to the dissection step. In certain instances, the energy instrument can be an energy tool mounted to a robotic arm of a robotic surgical system.
Tenth step 5220, the surgical team proceeds to the ligation step of the procedure. The surgical hub 106, 206 can infer that the surgeon is ligating arteries and veins because it receives data from the surgical stapling and cutting instrument indicating that the instrument is being fired. Similarly to the prior step, the surgical hub 106, 206 can derive this inference by cross-referencing the receipt of data from the surgical stapling and cutting instrument with the retrieved steps in the process. In certain instances, the surgical instrument can be a surgical tool mounted to a robotic arm of a robotic surgical system.
Eleventh step 5222, the segmentectomy portion of the procedure is performed. The surgical hub 106, 206 can infer that the surgeon is transecting the parenchyma based on data from the surgical stapling and cutting instrument, including data from its cartridge. The cartridge data can correspond to the size or type of staple being fired by the instrument, for example. As different types of staples are utilized for different types of tissues, the cartridge data can thus indicate the type of tissue being stapled and/or transected. In this case, the type of staple being fired is utilized for parenchyma (or other similar tissue types), which allows the surgical hub 106, 206 to infer that the segmentectomy portion of the procedure is being performed.
Twelfth step 5224, the node dissection step is then performed. The surgical hub 106, 206 can infer that the surgical team is dissecting the node and performing a leak test based on data received from the generator indicating that an RF or ultrasonic instrument is being fired. For this particular procedure, an RF or ultrasonic instrument being utilized after parenchyma was transected corresponds to the node dissection step, which allows the surgical hub 106, 206 to make this inference. It should be noted that surgeons regularly switch back and forth between surgical stapling/cutting instruments and surgical energy (i.e., RF or ultrasonic) instruments depending upon the particular step in the procedure because different instruments are better adapted for particular tasks. Therefore, the particular sequence in which the stapling/cutting instruments and surgical energy instruments are used can indicate what step of the procedure the surgeon is performing. Moreover, in certain instances, robotic tools can be utilized for one or more steps in a surgical procedure and/or handheld surgical instruments can be utilized for one or more steps in the surgical procedure. The surgeon(s) can alternate between robotic tools and handheld surgical instruments and/or can use the devices concurrently, for example. Upon completion of the twelfth step 5224, the incisions are closed up and the post-operative portion of the procedure begins.
Thirteenth step 5226, the patient's anesthesia is reversed. The surgical hub 106, 206 can infer that the patient is emerging from the anesthesia based on the ventilator data (i.e., the patient's breathing rate begins increasing), for example.
Lastly, the fourteenth step 5228 is that the medical personnel remove the various patient monitoring devices from the patient. The surgical hub 106, 206 can thus infer that the patient is being transferred to a recovery room when the hub loses EKG, BP, and other data from the patient monitoring devices. As can be seen from the description of this illustrative procedure, the surgical hub 106, 206 can determine or infer when each step of a given surgical procedure is taking place according to data received from the various data sources that are communicably coupled to the surgical hub 106, 206.
Situational awareness is further described in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is incorporated by reference herein in its entirety. In certain instances, operation of a robotic surgical system, including the various robotic surgical systems disclosed herein, for example, can be controlled by the hub 106, 206 based on its situational awareness and/or feedback from the components thereof and/or based on information from the cloud 102.
Various aspects of the subject matter described herein are set out in the following numbered examples.
A cloud-based analytics medical system comprising: at least one processor; at least one memory communicatively coupled to the at least one processor; an input/output interface configured for accessing data from a plurality of medical hub communication devices, each communicatively coupled to at least one surgical instrument; and a database residing in the at least one memory and configured to store the data; the at least one memory storing instructions executable by the at least one processor to: aggregate patient outcome data from the plurality of medical hubs, the patient outcome data comprising: data pertaining to steps performed and corresponding timings for each step in patient procedures; data pertaining to an outcome of each patient procedure performed; data pertaining to medical resources used in the patient procedures; for each data item pertaining to the medical resource: location data describing which medical facility said medical resource was allocated to; and for each data item pertaining to the outcome of the patient procedure: data pertaining to an indication of whether the outcome was a success or failure; aggregate medical resource acquisition data from the plurality of medical hubs; determine a correlation between positive outcomes from the patient outcome data and the resource acquisition data; generate a medical recommendation to change a medical resource acquisition practice based on the correlation; and cause display of the medical recommendation to a plurality of medical hubs located at different medical facilities.
The cloud based analytics medical system of Example 1, wherein the at least one memory storing instructions executable by the at least one processor to: evaluate the patient outcome data and the resource acquisition data of a particular medical facility; determine that a level of performance of the particular medical facility is below average compared to other medical facilities, based on a comparison of the evaluated patient outcome data and the resource acquisition data of the particular medical facility to the aggregated patient outcome data; and generate a localized recommendation to change a practice of the particular medical facility.
The cloud based analytics medical system of any one of Examples 1-2, wherein the localized recommendation comprises instructions to revise a medical procedure to account for a surgeon level of experience.
The cloud based analytics medical system of any one of Examples 1-3, wherein the localized recommendation comprises instructions to revise resource inventory management to reduce inventory of a first product and increase inventory of a second product.
The cloud based analytics medical system of any one of Examples 1-4, wherein the at least one memory storing instructions executable by the at least one processor to: evaluate the patient outcome data and the resource acquisition data of medical facilities belonging to a geographical region; determine that a level of performance of the medical facilities in the geographical region is below average compared to a global average of medical facilities, based on a comparison of the evaluated patient outcome data and the resource acquisition data of the medical facilities in the geographical region to the aggregated patient outcome data; and generate a regionalized recommendation to change a practice of the medical facilities belonging to the geographical region.
The cloud based analytics medical system of any one of Examples 1-5, wherein the at least one memory storing instructions executable by the at least one processor to: perform trending analysis indicating an expected change in demographics of a population; and generate a predictive modeling recommendation indicating an instruction to change a medical procedure or inventory of one or more medical products over a period of time, to address the expected change in demographics, based on the trending analysis.
The cloud based analytics medical system of any one of Examples 1-6, wherein the at least one memory storing instructions executable by the at least one processor to: compare performance metrics of a first method for conducting a medical procedure with performance metrics of a second method for conducting the same medical procedure; and generate a predictive modeling recommendation indicating an instruction to perform the first method for conducting the medical procedure based on the performance comparison.
A non-transitory computer readable medium storing computer readable instructions executable by the at least one processor of a cloud-based analytics system to: aggregate patient outcome data from the plurality of medical hubs, the patient outcome data comprising: data pertaining to steps performed and corresponding timings for each step in patient procedures; data pertaining to an outcome of each patient procedure performed; data pertaining to medical resources used in the patient procedures; for each data item pertaining to the medical resource: location data describing which medical facility said medical resource was allocated to; and for each data item pertaining to the outcome of the patient procedure: data pertaining to an indication of whether the outcome was a success or failure; aggregate medical resource acquisition data from the plurality of medical hubs; determine a correlation between positive outcomes from the patient outcome data and the resource acquisition data; generate a medical recommendation to change a medical resource acquisition practice based on the correlation; and cause display of the medical recommendation to a plurality of medical hubs located at different medical facilities.
The non-transitory computer readable medium of Example 8, wherein the instructions are further executable to: evaluate the patient outcome data and the resource acquisition data of a particular medical facility; determine that a level of performance of the particular medical facility is below average compared to other medical facilities, based on a comparison of the evaluated patient outcome data and the resource acquisition data of the particular medical facility to the aggregated patient outcome data; and generate a localized recommendation to change a practice of the particular medical facility.
The non-transitory computer readable medium of any one of Examples 8-9, wherein the localized recommendation comprises instructions to revise a medical procedure to account for a surgeon level of experience.
The non-transitory computer readable medium of any one of Examples 8-10, wherein the localized recommendation comprises instructions to revise resource inventory management to reduce inventory of a first product and increase inventory of a second product.
The non-transitory computer readable medium of any one of Examples 8-11, wherein the instructions are further executable to: evaluate the patient outcome data and the resource acquisition data of medical facilities belonging to a geographical region; determine that a level of performance of the medical facilities in the geographical region is below average compared to a global average of medical facilities, based on a comparison of the evaluated patient outcome data and the resource acquisition data of the medical facilities in the geographical region to the aggregated patient outcome data; and generate a regionalized recommendation to change a practice of the medical facilities belonging to the geographical region.
The non-transitory computer readable medium of any one of Examples 8-12, wherein the instructions are further configured to: perform trending analysis indicating an expected change in demographics of a population; and generate a predictive modeling recommendation indicating an instruction to change a medical procedure or inventory of one or more medical products over a period of time, to address the expected change in demographics, based on the trending analysis.
The non-transitory computer readable medium of any one of Examples 8-13, wherein the instructions are further configured to: compare performance metrics of a first method for conducting a medical procedure with performance metrics of a second method for conducting the same medical procedure; and generate a predictive modeling recommendation indicating an instruction to perform the first method for conducting the medical procedure based on the performance comparison.
A cloud based analytics medical system comprising: at least one processor; at least one memory communicatively coupled to the at least one processor; an input/output interface configured for accessing data from a plurality of medical hub communication devices, each communicatively coupled to at least one surgical instrument; and a database residing in the at least one memory and configured to store the data; wherein the at least one memory storing instructions executable by the at least one processor to: aggregate medical instrument data from the plurality of medical hubs, the medical instrument data comprising: data pertaining to physical and performance parameters of medical devices; for each datum pertaining to the medical device: usage data pertaining to medical procedures that utilized the medical device; and for each medical procedure; an outcome of the medical procedure; and a status of the condition of the medical device during the medical procedure; determine a correlation between outcomes of the medical procedures and the statuses of the conditions of the medical devices utilized in the respective medical procedures; access live medical procedure data for a live medical procedure, the live medical procedure data comprising a description of the medical devices present in an operating room that is performing the live medical procedure; determine an irregularity in the description of the medical devices present in the live medical procedure, based on the determined correlation between the outcomes and the medical devices utilized; and provide an alert to a medical communication hub that is utilized in the operating room of the live medical procedure.
The cloud based analytics medical system of Example 15, wherein the medical devices present in the operating room comprise a manual medical instrument and a robotic medical instrument.
The cloud based analytics medical system of any one of Examples 15-16, wherein the at least one processor is further configured to generate a change in firmware or software of a medical device present in the live medical procedure in concert with the provided alert.
The cloud based analytics medical system of any one of Examples 15-17, wherein the irregularity comprises use of a medical resource in a medical device present in the live medical procedure that is inconsistent with the aggregated medical instrument data pertaining to the medical procedure.
The cloud based analytics medical system of any one of Examples 15-18, wherein the alert comprises an instruction to change a firing or clamping speed of a medical device present in the live medical procedure.
The cloud based analytics medical system of any one of Examples 15-19, wherein the alert comprises an instruction to change an ultrasonic blade length of a medical device present in the live medical procedure.
While several forms have been illustrated and described, it is not the intention of the applicant to restrict or limit the scope of the appended claims to such detail. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to those forms may be implemented and will occur to those skilled in the art without departing from the scope of the present disclosure. Moreover, the structure of each element associated with the described forms can be alternatively described as a means for providing the function performed by the element. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations as falling within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.
The foregoing detailed description has set forth various forms of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, and/or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as one or more program products in a variety of forms, and that an illustrative form of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution.
Instructions used to program logic to perform various disclosed aspects can be stored within a memory in the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage. Furthermore, the instructions can be distributed via a network or by way of other computer readable media. Thus a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), but is not limited to, floppy diskettes, optical disks, compact disc, read-only memory (CD-ROMs), and magneto-optical disks, read-only memory (ROMs), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or a tangible, machine-readable storage used in the transmission of information over the Internet via electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Accordingly, the non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).
As used in any aspect herein, the term “control circuit” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor comprising one or more individual instruction processing cores, processing unit, processor, microcontroller, microcontroller unit, controller, digital signal processor (DSP), programmable logic device (PLD), programmable logic array (PLA), or field programmable gate array (FPGA)), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. The control circuit may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. Accordingly, as used herein “control circuit” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.
As used in any aspect herein, the term “logic” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices.
As used in any aspect herein, the terms “component,” “system,” “module” and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.
As used in any aspect herein, an “algorithm” refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities and/or logic states which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and/or states.
A network may include a packet switched network. The communication devices may be capable of communicating with each other using a selected packet switched network communications protocol. One example communications protocol may include an Ethernet communications protocol which may be capable permitting communication using a Transmission Control Protocol/Internet Protocol (TCP/IP). The Ethernet protocol may comply or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) titled “IEEE 802.3 Standard”, published in December, 2008 and/or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communications protocol. The X.25 communications protocol may comply or be compatible with a standard promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communications protocol. The frame relay communications protocol may comply or be compatible with a standard promulgated by Consultative Committee for International Telegraph and Telephone (CCITT) and/or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may comply or be compatible with an ATM standard published by the ATM Forum titled “ATM-MPLS Network Interworking 2.0” published August 2001, and/or later versions of this standard. Of course, different and/or after-developed connection-oriented network communication protocols are equally contemplated herein.
Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that, throughout the foregoing disclosure, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
One or more components may be referred to herein as “configured to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “configured to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.
The terms “proximal” and “distal” are used herein with reference to a clinician manipulating the handle portion of the surgical instrument. The term “proximal” refers to the portion closest to the clinician and the term “distal” refers to the portion located away from the clinician. It will be further appreciated that, for convenience and clarity, spatial terms such as “vertical”, “horizontal”, “up”, and “down” may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and/or absolute.
Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”
With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flow diagrams are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.
It is worthy to note that any reference to “one aspect,” “an aspect,” “an exemplification,” “one exemplification,” and the like means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in an exemplification,” and “in one exemplification” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.
Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification and/or listed in any Application Data Sheet is incorporated by reference herein, to the extent that the incorporated materials is not inconsistent herewith. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.
In summary, numerous benefits have been described which result from employing the concepts described herein. The foregoing description of the one or more forms has been presented for purposes of illustration and description. It is not intended to be exhaustive or limiting to the precise form disclosed. Modifications or variations are possible in light of the above teachings. The one or more forms were chosen and described in order to illustrate principles and practical application to thereby enable one of ordinary skill in the art to utilize the various forms and with various modifications as are suited to the particular use contemplated. It is intended that the claims submitted herewith define the overall scope.
This application claims the benefit of priority under 35 U.S.C. 119(e) to U.S. Provisional Patent Application Ser. No. 62/649,333, titled CLOUD-BASED MEDICAL ANALYTICS FOR CUSTOMIZATION AND RECOMMENDATIONS TO A USER, filed Mar. 28, 2018, the disclosure of which is herein incorporated by reference in its entirety. This application also claims the benefit of priority under 35 U.S.C. 119(e) to U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, of U.S. Provisional Patent Application Ser. No. 62/611,340, titled CLOUD-BASED MEDICAL ANALYTICS, filed Dec. 28, 2017, of U.S. Provisional Patent Application Ser. No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of each of which is herein incorporated by reference in its entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3082426 | Miles | Mar 1963 | A |
| 3503396 | Pierie et al. | Mar 1970 | A |
| 3584628 | Green | Jun 1971 | A |
| 3759017 | Young | Sep 1973 | A |
| 4448193 | Ivanov | May 1984 | A |
| 4523695 | Braun et al. | Jun 1985 | A |
| 4701193 | Robertson et al. | Oct 1987 | A |
| 4735603 | Goodson et al. | Apr 1988 | A |
| 4788977 | Farin et al. | Dec 1988 | A |
| 5042460 | Sakurai et al. | Aug 1991 | A |
| 5084057 | Green et al. | Jan 1992 | A |
| 5100402 | Fan | Mar 1992 | A |
| 5151102 | Kamiyama et al. | Sep 1992 | A |
| 5156315 | Green et al. | Oct 1992 | A |
| 5197962 | Sansom et al. | Mar 1993 | A |
| 5242474 | Herbst et al. | Sep 1993 | A |
| 5253793 | Green et al. | Oct 1993 | A |
| RE34519 | Fox et al. | Jan 1994 | E |
| 5318516 | Cosmescu | Jun 1994 | A |
| 5322055 | Davison et al. | Jun 1994 | A |
| 5342349 | Kaufman | Aug 1994 | A |
| 5383880 | Hooven | Jan 1995 | A |
| 5396900 | Slater et al. | Mar 1995 | A |
| 5397046 | Savage et al. | Mar 1995 | A |
| 5403312 | Yates et al. | Apr 1995 | A |
| 5403327 | Thornton et al. | Apr 1995 | A |
| 5413267 | Solyntjes et al. | May 1995 | A |
| 5417699 | Klein et al. | May 1995 | A |
| 5439468 | Schulze et al. | Aug 1995 | A |
| 5445304 | Plyley et al. | Aug 1995 | A |
| 5465895 | Knodel et al. | Nov 1995 | A |
| 5467911 | Tsuruta et al. | Nov 1995 | A |
| 5474566 | Alesi et al. | Dec 1995 | A |
| 5496315 | Weaver et al. | Mar 1996 | A |
| 5503320 | Webster et al. | Apr 1996 | A |
| 5531743 | Nettekoven et al. | Jul 1996 | A |
| 5545148 | Wurster | Aug 1996 | A |
| 5610379 | Muz et al. | Mar 1997 | A |
| 5613966 | Makower et al. | Mar 1997 | A |
| 5624452 | Yates | Apr 1997 | A |
| 5643291 | Pier et al. | Jul 1997 | A |
| 5654750 | Weil et al. | Aug 1997 | A |
| 5673841 | Schulze et al. | Oct 1997 | A |
| 5673842 | Bittner et al. | Oct 1997 | A |
| 5675227 | Roos et al. | Oct 1997 | A |
| 5693052 | Weaver | Dec 1997 | A |
| 5695502 | Pier et al. | Dec 1997 | A |
| 5697926 | Weaver | Dec 1997 | A |
| 5706998 | Plyley et al. | Jan 1998 | A |
| 5725536 | Oberlin et al. | Mar 1998 | A |
| 5725542 | Yoon | Mar 1998 | A |
| 5735848 | Yates et al. | Apr 1998 | A |
| 5746209 | Yost et al. | May 1998 | A |
| 5749362 | Funda et al. | May 1998 | A |
| 5749893 | Vidal et al. | May 1998 | A |
| 5752644 | Bolanos et al. | May 1998 | A |
| 5762255 | Chrisman et al. | Jun 1998 | A |
| 5766186 | Faraz et al. | Jun 1998 | A |
| 5769791 | Benaron et al. | Jun 1998 | A |
| 5797537 | Oberlin et al. | Aug 1998 | A |
| D399561 | Ellingson | Oct 1998 | S |
| 5817093 | Williamson, IV et al. | Oct 1998 | A |
| 5820009 | Melling et al. | Oct 1998 | A |
| 5836909 | Cosmescu | Nov 1998 | A |
| 5843080 | Fleenor et al. | Dec 1998 | A |
| 5846237 | Nettekoven | Dec 1998 | A |
| 5849022 | Sakashita et al. | Dec 1998 | A |
| 5873873 | Smith et al. | Feb 1999 | A |
| 5878938 | Bittner et al. | Mar 1999 | A |
| 5893849 | Weaver | Apr 1999 | A |
| 5906625 | Bito et al. | May 1999 | A |
| 5942333 | Arnett et al. | Aug 1999 | A |
| 5947996 | Logeman | Sep 1999 | A |
| 5968032 | Sleister | Oct 1999 | A |
| 5980510 | Tsonton et al. | Nov 1999 | A |
| 5997528 | Bisch et al. | Dec 1999 | A |
| 6010054 | Johnson et al. | Jan 2000 | A |
| 6030437 | Gourrier et al. | Feb 2000 | A |
| 6036637 | Kudo | Mar 2000 | A |
| 6039735 | Greep | Mar 2000 | A |
| 6059799 | Aranyi et al. | May 2000 | A |
| 6066137 | Greep | May 2000 | A |
| 6079606 | Milliman et al. | Jun 2000 | A |
| 6090107 | Borgmeier et al. | Jul 2000 | A |
| 6099537 | Sugai et al. | Aug 2000 | A |
| 6155473 | Tompkins et al. | Dec 2000 | A |
| 6214000 | Fleenor et al. | Apr 2001 | B1 |
| 6273887 | Yamauchi et al. | Aug 2001 | B1 |
| 6301495 | Gueziec et al. | Oct 2001 | B1 |
| 6302881 | Farin | Oct 2001 | B1 |
| 6325808 | Bernard et al. | Dec 2001 | B1 |
| 6325811 | Messerly | Dec 2001 | B1 |
| 6341164 | Dilkie et al. | Jan 2002 | B1 |
| 6391102 | Bodden et al. | May 2002 | B1 |
| 6443973 | Whitman | Sep 2002 | B1 |
| 6461352 | Morgan et al. | Oct 2002 | B2 |
| 6530933 | Yeung et al. | Mar 2003 | B1 |
| 6551243 | Bocionek et al. | Apr 2003 | B2 |
| 6569109 | Sakurai et al. | May 2003 | B2 |
| 6582424 | Fleenor et al. | Jun 2003 | B2 |
| 6585791 | Garito et al. | Jul 2003 | B1 |
| 6618626 | West, Jr. et al. | Sep 2003 | B2 |
| 6648223 | Boukhny et al. | Nov 2003 | B2 |
| 6685704 | Greep | Feb 2004 | B2 |
| 6699187 | Webb et al. | Mar 2004 | B2 |
| 6742895 | Robin | Jun 2004 | B2 |
| 6752816 | Culp et al. | Jun 2004 | B2 |
| 6773444 | Messerly | Aug 2004 | B2 |
| 6778846 | Martinez et al. | Aug 2004 | B1 |
| 6781683 | Kacyra et al. | Aug 2004 | B2 |
| 6783524 | Anderson et al. | Aug 2004 | B2 |
| 6783525 | Greep et al. | Aug 2004 | B2 |
| 6852219 | Hammond | Feb 2005 | B2 |
| 6869430 | Balbierz et al. | Mar 2005 | B2 |
| 6869435 | Blake, III | Mar 2005 | B2 |
| 6911033 | de Guillebon et al. | Jun 2005 | B2 |
| 6937892 | Leyde et al. | Aug 2005 | B2 |
| 6945981 | Donofrio et al. | Sep 2005 | B2 |
| 6951559 | Greep | Oct 2005 | B1 |
| 6978921 | Shelton, IV et al. | Dec 2005 | B2 |
| 6988649 | Shelton, IV et al. | Jan 2006 | B2 |
| 7000818 | Shelton, IV et al. | Feb 2006 | B2 |
| 7030146 | Baynes et al. | Apr 2006 | B2 |
| 7032798 | Whitman et al. | Apr 2006 | B2 |
| 7041941 | Faries, Jr. et al. | May 2006 | B2 |
| 7044352 | Shelton, IV et al. | May 2006 | B2 |
| 7044911 | Drinan et al. | May 2006 | B2 |
| 7048775 | Jornitz et al. | May 2006 | B2 |
| 7053752 | Wang et al. | May 2006 | B2 |
| 7077853 | Kramer et al. | Jul 2006 | B2 |
| 7077856 | Whitman | Jul 2006 | B2 |
| 7081096 | Brister et al. | Jul 2006 | B2 |
| 7097640 | Wang et al. | Aug 2006 | B2 |
| 7103688 | Strong | Sep 2006 | B2 |
| 7118564 | Ritchie et al. | Oct 2006 | B2 |
| 7121460 | Parsons et al. | Oct 2006 | B1 |
| 7143923 | Shelton, IV et al. | Dec 2006 | B2 |
| 7143925 | Shelton, IV et al. | Dec 2006 | B2 |
| 7147139 | Schwemberger et al. | Dec 2006 | B2 |
| 7169145 | Isaacson et al. | Jan 2007 | B2 |
| 7177533 | McFarlin et al. | Feb 2007 | B2 |
| 7182775 | de Guillebon et al. | Feb 2007 | B2 |
| 7208005 | Frecker et al. | Apr 2007 | B2 |
| 7230529 | Ketcherside, Jr. et al. | Jun 2007 | B2 |
| 7232447 | Gellman et al. | Jun 2007 | B2 |
| 7236817 | Papas et al. | Jun 2007 | B2 |
| 7246734 | Shelton, IV | Jul 2007 | B2 |
| 7278563 | Green | Oct 2007 | B1 |
| 7294106 | Birkenbach et al. | Nov 2007 | B2 |
| 7294116 | Ellman et al. | Nov 2007 | B1 |
| 7296724 | Green et al. | Nov 2007 | B2 |
| 7317955 | McGreevy | Jan 2008 | B2 |
| 7328828 | Ortiz et al. | Feb 2008 | B2 |
| 7362228 | Nycz et al. | Apr 2008 | B2 |
| 7371227 | Zeiner | May 2008 | B2 |
| 7380695 | Doll et al. | Jun 2008 | B2 |
| 7383088 | Spinelli et al. | Jun 2008 | B2 |
| 7391173 | Schena | Jun 2008 | B2 |
| 7407074 | Ortiz et al. | Aug 2008 | B2 |
| 7422139 | Shelton, IV et al. | Sep 2008 | B2 |
| 7423972 | Shaham et al. | Sep 2008 | B2 |
| 7457804 | Uber, III et al. | Nov 2008 | B2 |
| 7464847 | Viola et al. | Dec 2008 | B2 |
| 7464849 | Shelton, IV et al. | Dec 2008 | B2 |
| 7515961 | Germanson et al. | Apr 2009 | B2 |
| 7575144 | Ortiz et al. | Aug 2009 | B2 |
| 7621192 | Conti et al. | Nov 2009 | B2 |
| 7621898 | Lalomia et al. | Nov 2009 | B2 |
| 7637410 | Marczyk | Dec 2009 | B2 |
| 7641092 | Kruszynski et al. | Jan 2010 | B2 |
| 7667839 | Bates | Feb 2010 | B2 |
| 7670334 | Hueil et al. | Mar 2010 | B2 |
| 7694865 | Scirica | Apr 2010 | B2 |
| 7699860 | Huitema et al. | Apr 2010 | B2 |
| 7720306 | Gardiner et al. | May 2010 | B2 |
| 7721934 | Shelton, IV et al. | May 2010 | B2 |
| 7736357 | Lee, Jr. et al. | Jun 2010 | B2 |
| 7742176 | Braunecker et al. | Jun 2010 | B2 |
| 7753245 | Boudreaux et al. | Jul 2010 | B2 |
| 7766905 | Paterson et al. | Aug 2010 | B2 |
| 7770773 | Whitman et al. | Aug 2010 | B2 |
| 7776037 | Odom | Aug 2010 | B2 |
| 7782789 | Stultz et al. | Aug 2010 | B2 |
| 7784663 | Shelton, IV | Aug 2010 | B2 |
| 7803151 | Whitman | Sep 2010 | B2 |
| 7818041 | Kim et al. | Oct 2010 | B2 |
| 7837079 | Holsten et al. | Nov 2010 | B2 |
| 7837680 | Isaacson et al. | Nov 2010 | B2 |
| 7845537 | Shelton, IV et al. | Dec 2010 | B2 |
| 7862560 | Marion | Jan 2011 | B2 |
| 7862579 | Ortiz et al. | Jan 2011 | B2 |
| 7892337 | Palmerton et al. | Feb 2011 | B2 |
| 7913891 | Doll et al. | Mar 2011 | B2 |
| 7918230 | Whitman et al. | Apr 2011 | B2 |
| 7918377 | Measamer et al. | Apr 2011 | B2 |
| 7920706 | Asokan et al. | Apr 2011 | B2 |
| 7955322 | Devengenzo et al. | Jun 2011 | B2 |
| 7963433 | Whitman et al. | Jun 2011 | B2 |
| 7966269 | Bauer et al. | Jun 2011 | B2 |
| 7967180 | Scirica | Jun 2011 | B2 |
| 7976553 | Shelton, IV et al. | Jul 2011 | B2 |
| 7979157 | Anvari | Jul 2011 | B2 |
| 7980443 | Scheib et al. | Jul 2011 | B2 |
| 7982776 | Dunki-Jacobs et al. | Jul 2011 | B2 |
| 7988028 | Farascioni et al. | Aug 2011 | B2 |
| 7993140 | Sakezles | Aug 2011 | B2 |
| 7995045 | Dunki-Jacobs | Aug 2011 | B2 |
| 8005947 | Morris et al. | Aug 2011 | B2 |
| 8007513 | Nalagatla et al. | Aug 2011 | B2 |
| 8010180 | Quaid et al. | Aug 2011 | B2 |
| 8012170 | Whitman et al. | Sep 2011 | B2 |
| 8015976 | Shah | Sep 2011 | B2 |
| 8035685 | Jensen | Oct 2011 | B2 |
| 8038686 | Huitema et al. | Oct 2011 | B2 |
| 8043560 | Okumoto et al. | Oct 2011 | B2 |
| 8054184 | Cline et al. | Nov 2011 | B2 |
| 8062306 | Nobis et al. | Nov 2011 | B2 |
| 8062330 | Prommersberger et al. | Nov 2011 | B2 |
| 8066721 | Kortenbach et al. | Nov 2011 | B2 |
| 8075571 | Vitali et al. | Dec 2011 | B2 |
| 8096459 | Ortiz et al. | Jan 2012 | B2 |
| 8120301 | Goldberg et al. | Feb 2012 | B2 |
| 8123764 | Meade et al. | Feb 2012 | B2 |
| 8131565 | Dicks et al. | Mar 2012 | B2 |
| 8147486 | Honour et al. | Apr 2012 | B2 |
| 8157145 | Shelton, IV et al. | Apr 2012 | B2 |
| 8157150 | Viola et al. | Apr 2012 | B2 |
| 8160098 | Yan et al. | Apr 2012 | B1 |
| 8161977 | Shelton, IV et al. | Apr 2012 | B2 |
| 8172836 | Ward | May 2012 | B2 |
| 8181839 | Beetel | May 2012 | B2 |
| 8185409 | Putnam et al. | May 2012 | B2 |
| 8206345 | Abboud et al. | Jun 2012 | B2 |
| 8210411 | Yates et al. | Jul 2012 | B2 |
| 8220688 | Laurent et al. | Jul 2012 | B2 |
| 8225979 | Farascioni et al. | Jul 2012 | B2 |
| 8229549 | Whitman et al. | Jul 2012 | B2 |
| 8257387 | Cunningham | Sep 2012 | B2 |
| 8262560 | Whitman | Sep 2012 | B2 |
| 8292888 | Whitman | Oct 2012 | B2 |
| 8308040 | Huang et al. | Nov 2012 | B2 |
| 8321581 | Katis et al. | Nov 2012 | B2 |
| 8328065 | Shah | Dec 2012 | B2 |
| 8335590 | Costa et al. | Dec 2012 | B2 |
| 8346392 | Walser et al. | Jan 2013 | B2 |
| 8364222 | Cook et al. | Jan 2013 | B2 |
| 8365975 | Manoux et al. | Feb 2013 | B1 |
| 8388652 | Viola | Mar 2013 | B2 |
| 8393514 | Shelton, IV et al. | Mar 2013 | B2 |
| 8397972 | Kostrzewski | Mar 2013 | B2 |
| 8398541 | DiMaio et al. | Mar 2013 | B2 |
| 8403946 | Whitfield et al. | Mar 2013 | B2 |
| 8406859 | Zuzak et al. | Mar 2013 | B2 |
| 8422035 | Hinderling et al. | Apr 2013 | B2 |
| 8423182 | Robinson et al. | Apr 2013 | B2 |
| 8428722 | Verhoef et al. | Apr 2013 | B2 |
| 8439910 | Greep et al. | May 2013 | B2 |
| 8444663 | Houser et al. | May 2013 | B2 |
| 8452615 | Abri | May 2013 | B2 |
| 8454506 | Rothman et al. | Jun 2013 | B2 |
| 8461744 | Wiener et al. | Jun 2013 | B2 |
| 8468030 | Stroup et al. | Jun 2013 | B2 |
| 8469973 | Meade et al. | Jun 2013 | B2 |
| 8472630 | Konrad et al. | Jun 2013 | B2 |
| 8476227 | Kaplan et al. | Jul 2013 | B2 |
| 8489235 | Moll et al. | Jul 2013 | B2 |
| 8499992 | Whitman et al. | Aug 2013 | B2 |
| 8503759 | Greer et al. | Aug 2013 | B2 |
| 8505801 | Ehrenfels et al. | Aug 2013 | B2 |
| 8512365 | Wiener et al. | Aug 2013 | B2 |
| 8521331 | Itkowitz | Aug 2013 | B2 |
| 8523043 | Ullrich et al. | Sep 2013 | B2 |
| 8546996 | Messerly et al. | Oct 2013 | B2 |
| 8560047 | Haider et al. | Oct 2013 | B2 |
| 8561870 | Baxter, III et al. | Oct 2013 | B2 |
| 8562598 | Falkenstein et al. | Oct 2013 | B2 |
| 8566115 | Moore | Oct 2013 | B2 |
| 8573459 | Smith et al. | Nov 2013 | B2 |
| 8573465 | Shelton, IV | Nov 2013 | B2 |
| 8591536 | Robertson | Nov 2013 | B2 |
| 8595607 | Nekoomaram et al. | Nov 2013 | B2 |
| 8596513 | Olson et al. | Dec 2013 | B2 |
| 8608044 | Hueil et al. | Dec 2013 | B2 |
| 8608045 | Smith et al. | Dec 2013 | B2 |
| 8616431 | Timm et al. | Dec 2013 | B2 |
| 8620473 | Diolaiti et al. | Dec 2013 | B2 |
| 8623027 | Price et al. | Jan 2014 | B2 |
| 8627483 | Rachlin et al. | Jan 2014 | B2 |
| 8627995 | Smith et al. | Jan 2014 | B2 |
| 8628518 | Blumenkranz et al. | Jan 2014 | B2 |
| 8628545 | Cabrera et al. | Jan 2014 | B2 |
| 8632525 | Kerr et al. | Jan 2014 | B2 |
| 8652086 | Gerg et al. | Feb 2014 | B2 |
| 8652128 | Ward | Feb 2014 | B2 |
| 8657176 | Shelton, IV et al. | Feb 2014 | B2 |
| 8657177 | Scirica et al. | Feb 2014 | B2 |
| 8663220 | Wiener et al. | Mar 2014 | B2 |
| 8666544 | Moll et al. | Mar 2014 | B2 |
| 8682049 | Zhao et al. | Mar 2014 | B2 |
| 8682489 | Itkowitz et al. | Mar 2014 | B2 |
| 8685056 | Evans et al. | Apr 2014 | B2 |
| 8701962 | Kostrzewski | Apr 2014 | B2 |
| 8733613 | Huitema et al. | May 2014 | B2 |
| 8740840 | Foley et al. | Jun 2014 | B2 |
| 8740866 | Reasoner et al. | Jun 2014 | B2 |
| 8752749 | Moore et al. | Jun 2014 | B2 |
| 8757465 | Woodard, Jr. et al. | Jun 2014 | B2 |
| 8761717 | Buchheit | Jun 2014 | B1 |
| 8763879 | Shelton, IV et al. | Jul 2014 | B2 |
| 8768251 | Claus et al. | Jul 2014 | B2 |
| 8771270 | Burbank | Jul 2014 | B2 |
| 8775196 | Simpson et al. | Jul 2014 | B2 |
| 8779648 | Giordano et al. | Jul 2014 | B2 |
| 8794497 | Zingman | Aug 2014 | B2 |
| 8799008 | Johnson et al. | Aug 2014 | B2 |
| 8799009 | Mellin et al. | Aug 2014 | B2 |
| 8801703 | Gregg et al. | Aug 2014 | B2 |
| 8814996 | Giurgiutiu et al. | Aug 2014 | B2 |
| 8818556 | Sanchez et al. | Aug 2014 | B2 |
| 8820603 | Shelton, IV et al. | Sep 2014 | B2 |
| 8820608 | Miyamoto | Sep 2014 | B2 |
| 8827134 | Viola et al. | Sep 2014 | B2 |
| 8840003 | Morgan et al. | Sep 2014 | B2 |
| 8851354 | Swensgard et al. | Oct 2014 | B2 |
| 8852174 | Burbank | Oct 2014 | B2 |
| 8875973 | Whitman | Nov 2014 | B2 |
| 8882662 | Charles | Nov 2014 | B2 |
| 8905977 | Shelton et al. | Dec 2014 | B2 |
| 8912746 | Reid et al. | Dec 2014 | B2 |
| 8914098 | Brennan et al. | Dec 2014 | B2 |
| 8918207 | Prisco | Dec 2014 | B2 |
| 8920414 | Stone et al. | Dec 2014 | B2 |
| 8920433 | Barrier et al. | Dec 2014 | B2 |
| 8930203 | Kiaie et al. | Jan 2015 | B2 |
| 8930214 | Woolford | Jan 2015 | B2 |
| 8931679 | Kostrzewski | Jan 2015 | B2 |
| 8945095 | Blumenkranz et al. | Feb 2015 | B2 |
| 8945163 | Voegele et al. | Feb 2015 | B2 |
| 8956581 | Rosenbaum et al. | Feb 2015 | B2 |
| 8960519 | Whitman et al. | Feb 2015 | B2 |
| 8960520 | McCuen | Feb 2015 | B2 |
| 8962062 | Podhajsky et al. | Feb 2015 | B2 |
| 8967455 | Zhou | Mar 2015 | B2 |
| 8968276 | Zemlok et al. | Mar 2015 | B2 |
| 8968309 | Roy et al. | Mar 2015 | B2 |
| 8968337 | Whitfield et al. | Mar 2015 | B2 |
| 8968358 | Reschke | Mar 2015 | B2 |
| 8974429 | Gordon et al. | Mar 2015 | B2 |
| 8986302 | Aldridge et al. | Mar 2015 | B2 |
| 8989903 | Weir et al. | Mar 2015 | B2 |
| 8991678 | Wellman et al. | Mar 2015 | B2 |
| 8992565 | Brisson et al. | Mar 2015 | B2 |
| 8998797 | Omori | Apr 2015 | B2 |
| 9002518 | Manzo et al. | Apr 2015 | B2 |
| 9011366 | Dean et al. | Apr 2015 | B2 |
| 9011427 | Price et al. | Apr 2015 | B2 |
| 9016539 | Kostrzewski et al. | Apr 2015 | B2 |
| 9017326 | DiNardo et al. | Apr 2015 | B2 |
| 9020240 | Pettersson et al. | Apr 2015 | B2 |
| 9023071 | Miller et al. | May 2015 | B2 |
| 9027431 | Tang et al. | May 2015 | B2 |
| 9028494 | Shelton, IV et al. | May 2015 | B2 |
| 9035568 | Ganton et al. | May 2015 | B2 |
| 9038882 | Racenet et al. | May 2015 | B2 |
| 9043027 | Durant et al. | May 2015 | B2 |
| 9044227 | Shelton, IV et al. | Jun 2015 | B2 |
| 9044244 | Ludwin et al. | Jun 2015 | B2 |
| 9044261 | Houser | Jun 2015 | B2 |
| 9050063 | Roe et al. | Jun 2015 | B2 |
| 9050083 | Yates et al. | Jun 2015 | B2 |
| 9050120 | Swarup et al. | Jun 2015 | B2 |
| 9052809 | Vesto | Jun 2015 | B2 |
| 9055035 | Porsch et al. | Jun 2015 | B2 |
| 9060770 | Shelton, IV et al. | Jun 2015 | B2 |
| 9060775 | Wiener et al. | Jun 2015 | B2 |
| 9066650 | Sekiguchi | Jun 2015 | B2 |
| 9072535 | Shelton, IV et al. | Jul 2015 | B2 |
| 9072536 | Shelton, IV et al. | Jul 2015 | B2 |
| 9078653 | Leimbach et al. | Jul 2015 | B2 |
| 9078727 | Miller | Jul 2015 | B2 |
| 9084606 | Greep | Jul 2015 | B2 |
| 9089360 | Messerly et al. | Jul 2015 | B2 |
| 9095362 | Dachs, II et al. | Aug 2015 | B2 |
| 9095367 | Olson et al. | Aug 2015 | B2 |
| 9101358 | Kerr et al. | Aug 2015 | B2 |
| 9101359 | Smith et al. | Aug 2015 | B2 |
| 9101374 | Hoch et al. | Aug 2015 | B1 |
| 9106270 | Puterbaugh et al. | Aug 2015 | B2 |
| 9107573 | Birnkrant | Aug 2015 | B2 |
| 9107662 | Kostrzewski | Aug 2015 | B2 |
| 9107684 | Ma | Aug 2015 | B2 |
| 9107688 | Kimball et al. | Aug 2015 | B2 |
| 9107689 | Robertson et al. | Aug 2015 | B2 |
| 9107694 | Hendriks et al. | Aug 2015 | B2 |
| 9114494 | Mah | Aug 2015 | B1 |
| 9116597 | Gulasky | Aug 2015 | B1 |
| 9119655 | Bowling et al. | Sep 2015 | B2 |
| 9119657 | Shelton, IV et al. | Sep 2015 | B2 |
| 9123155 | Cunningham et al. | Sep 2015 | B2 |
| 9129054 | Nawana et al. | Sep 2015 | B2 |
| 9137254 | Bilbrey et al. | Sep 2015 | B2 |
| 9138129 | Diolaiti | Sep 2015 | B2 |
| 9149322 | Knowlton | Oct 2015 | B2 |
| 9161803 | Yates et al. | Oct 2015 | B2 |
| 9168054 | Turner et al. | Oct 2015 | B2 |
| 9179912 | Yates et al. | Nov 2015 | B2 |
| 9183723 | Sherman et al. | Nov 2015 | B2 |
| 9186143 | Timm et al. | Nov 2015 | B2 |
| 9192375 | Skinlo et al. | Nov 2015 | B2 |
| 9192447 | Choi et al. | Nov 2015 | B2 |
| 9192707 | Gerber et al. | Nov 2015 | B2 |
| 9202078 | Abuelsaad et al. | Dec 2015 | B2 |
| 9204879 | Shelton, IV | Dec 2015 | B2 |
| 9204995 | Scheller et al. | Dec 2015 | B2 |
| 9216062 | Duque et al. | Dec 2015 | B2 |
| 9218053 | Komuro et al. | Dec 2015 | B2 |
| 9226766 | Aldridge et al. | Jan 2016 | B2 |
| 9226767 | Stulen et al. | Jan 2016 | B2 |
| 9237891 | Shelton, IV | Jan 2016 | B2 |
| 9241728 | Price et al. | Jan 2016 | B2 |
| 9241731 | Boudreaux et al. | Jan 2016 | B2 |
| 9250172 | Harris et al. | Feb 2016 | B2 |
| 9255907 | Heanue et al. | Feb 2016 | B2 |
| 9265585 | Wingardner et al. | Feb 2016 | B2 |
| 9272406 | Aronhalt et al. | Mar 2016 | B2 |
| 9277956 | Zhang | Mar 2016 | B2 |
| 9280884 | Schultz et al. | Mar 2016 | B1 |
| 9282974 | Shelton, IV | Mar 2016 | B2 |
| 9283054 | Morgan et al. | Mar 2016 | B2 |
| 9289212 | Shelton, IV et al. | Mar 2016 | B2 |
| 9295514 | Shelton, IV et al. | Mar 2016 | B2 |
| 9301691 | Hufnagel et al. | Apr 2016 | B2 |
| 9301753 | Aldridge et al. | Apr 2016 | B2 |
| 9301759 | Spivey et al. | Apr 2016 | B2 |
| 9301810 | Amiri et al. | Apr 2016 | B2 |
| 9307894 | von Grunberg et al. | Apr 2016 | B2 |
| 9307914 | Fahey | Apr 2016 | B2 |
| 9307986 | Hall et al. | Apr 2016 | B2 |
| 9314246 | Shelton, IV et al. | Apr 2016 | B2 |
| 9314308 | Parihar et al. | Apr 2016 | B2 |
| 9326767 | Koch et al. | May 2016 | B2 |
| 9331422 | Nazzaro et al. | May 2016 | B2 |
| 9332987 | Leimbach et al. | May 2016 | B2 |
| 9333042 | Diolaiti et al. | May 2016 | B2 |
| 9345481 | Hall et al. | May 2016 | B2 |
| 9345490 | Ippisch | May 2016 | B2 |
| 9351726 | Leimbach et al. | May 2016 | B2 |
| 9351727 | Leimbach et al. | May 2016 | B2 |
| 9358003 | Hall et al. | Jun 2016 | B2 |
| 9358685 | Meier et al. | Jun 2016 | B2 |
| 9360449 | Duric | Jun 2016 | B2 |
| 9364231 | Wenchell | Jun 2016 | B2 |
| 9364249 | Kimball et al. | Jun 2016 | B2 |
| 9364294 | Razzaque et al. | Jun 2016 | B2 |
| 9375282 | Nau, Jr. et al. | Jun 2016 | B2 |
| 9375539 | Stearns et al. | Jun 2016 | B2 |
| 9381003 | Todor et al. | Jul 2016 | B2 |
| 9381058 | Houser et al. | Jul 2016 | B2 |
| 9386984 | Aronhalt et al. | Jul 2016 | B2 |
| 9386988 | Baxter, III et al. | Jul 2016 | B2 |
| 9387295 | Mastri et al. | Jul 2016 | B1 |
| 9393017 | Flanagan et al. | Jul 2016 | B2 |
| 9393037 | Olson et al. | Jul 2016 | B2 |
| 9398905 | Martin | Jul 2016 | B2 |
| 9398911 | Auld | Jul 2016 | B2 |
| 9402629 | Ehrenfels et al. | Aug 2016 | B2 |
| 9414776 | Sillay et al. | Aug 2016 | B2 |
| 9419018 | Sasagawa et al. | Aug 2016 | B2 |
| 9421014 | Ingmanson et al. | Aug 2016 | B2 |
| 9433470 | Choi | Sep 2016 | B2 |
| 9439736 | Olson | Sep 2016 | B2 |
| 9450701 | Do et al. | Sep 2016 | B2 |
| 9451958 | Shelton, IV et al. | Sep 2016 | B2 |
| 9463022 | Swayze et al. | Oct 2016 | B2 |
| 9468438 | Baber et al. | Oct 2016 | B2 |
| 9480492 | Aranyi et al. | Nov 2016 | B2 |
| 9485475 | Speier et al. | Nov 2016 | B2 |
| 9492146 | Kostrzewski et al. | Nov 2016 | B2 |
| 9492237 | Kang et al. | Nov 2016 | B2 |
| 9498215 | Duque et al. | Nov 2016 | B2 |
| 9516239 | Blanquart et al. | Dec 2016 | B2 |
| 9519753 | Gerdeman et al. | Dec 2016 | B1 |
| 9526407 | Hoeg et al. | Dec 2016 | B2 |
| 9526499 | Kostrzewski et al. | Dec 2016 | B2 |
| 9526587 | Zhao et al. | Dec 2016 | B2 |
| 9539007 | Dhakad et al. | Jan 2017 | B2 |
| 9539020 | Conlon et al. | Jan 2017 | B2 |
| 9542481 | Halter et al. | Jan 2017 | B2 |
| 9546662 | Shener-Irmakoglu et al. | Jan 2017 | B2 |
| 9554794 | Baber et al. | Jan 2017 | B2 |
| 9554854 | Yates et al. | Jan 2017 | B2 |
| 9561038 | Shelton, IV et al. | Feb 2017 | B2 |
| 9561045 | Hinman et al. | Feb 2017 | B2 |
| 9572592 | Price et al. | Feb 2017 | B2 |
| 9585657 | Shelton, IV et al. | Mar 2017 | B2 |
| 9592095 | Panescu et al. | Mar 2017 | B2 |
| 9597081 | Swayze et al. | Mar 2017 | B2 |
| 9603024 | Wang et al. | Mar 2017 | B2 |
| 9610114 | Baxter, III et al. | Apr 2017 | B2 |
| 9622808 | Beller et al. | Apr 2017 | B2 |
| 9629623 | Lytle, IV et al. | Apr 2017 | B2 |
| 9629629 | Leimbach et al. | Apr 2017 | B2 |
| 9630318 | Ibarz Gabardos et al. | Apr 2017 | B2 |
| 9636188 | Gattani et al. | May 2017 | B2 |
| 9641596 | Unagami et al. | May 2017 | B2 |
| 9641815 | Richardson et al. | May 2017 | B2 |
| 9649110 | Parihar et al. | May 2017 | B2 |
| 9649111 | Shelton, IV et al. | May 2017 | B2 |
| 9649126 | Robertson et al. | May 2017 | B2 |
| 9649169 | Cinquin et al. | May 2017 | B2 |
| 9652655 | Satish et al. | May 2017 | B2 |
| 9655616 | Aranyi | May 2017 | B2 |
| 9656092 | Golden | May 2017 | B2 |
| 9662116 | Smith et al. | May 2017 | B2 |
| 9662177 | Weir et al. | May 2017 | B2 |
| 9668729 | Williams et al. | Jun 2017 | B2 |
| 9668732 | Patel et al. | Jun 2017 | B2 |
| 9668765 | Grace et al. | Jun 2017 | B2 |
| 9681870 | Baxter, III et al. | Jun 2017 | B2 |
| 9686306 | Chizeck et al. | Jun 2017 | B2 |
| 9687230 | Leimbach et al. | Jun 2017 | B2 |
| 9690362 | Leimbach et al. | Jun 2017 | B2 |
| 9700292 | Nawana et al. | Jul 2017 | B2 |
| 9700309 | Jaworek et al. | Jul 2017 | B2 |
| 9700312 | Kostrzewski et al. | Jul 2017 | B2 |
| 9706993 | Hessler et al. | Jul 2017 | B2 |
| 9710644 | Reybok et al. | Jul 2017 | B2 |
| 9713424 | Spaide | Jul 2017 | B2 |
| 9717498 | Aranyi et al. | Aug 2017 | B2 |
| 9717525 | Ahluwalia et al. | Aug 2017 | B2 |
| 9717548 | Couture | Aug 2017 | B2 |
| 9724094 | Baber et al. | Aug 2017 | B2 |
| 9724118 | Schulte et al. | Aug 2017 | B2 |
| 9733663 | Leimbach et al. | Aug 2017 | B2 |
| 9737301 | Baber et al. | Aug 2017 | B2 |
| 9737310 | Whitfield et al. | Aug 2017 | B2 |
| 9737335 | Butler et al. | Aug 2017 | B2 |
| 9737355 | Yates et al. | Aug 2017 | B2 |
| 9740826 | Raghavan et al. | Aug 2017 | B2 |
| 9743016 | Nestares et al. | Aug 2017 | B2 |
| 9743929 | Leimbach et al. | Aug 2017 | B2 |
| 9743946 | Faller et al. | Aug 2017 | B2 |
| 9743947 | Price et al. | Aug 2017 | B2 |
| 9750499 | Leimbach et al. | Sep 2017 | B2 |
| 9750522 | Scheib et al. | Sep 2017 | B2 |
| 9750523 | Tsubuku | Sep 2017 | B2 |
| 9753135 | Bosch | Sep 2017 | B2 |
| 9757126 | Cappola | Sep 2017 | B2 |
| 9757128 | Baber et al. | Sep 2017 | B2 |
| 9757142 | Shimizu | Sep 2017 | B2 |
| 9757152 | Ogilvie et al. | Sep 2017 | B2 |
| 9764164 | Wiener et al. | Sep 2017 | B2 |
| 9770541 | Carr et al. | Sep 2017 | B2 |
| 9777913 | Talbert et al. | Oct 2017 | B2 |
| 9782164 | Mumaw et al. | Oct 2017 | B2 |
| 9782169 | Kimsey et al. | Oct 2017 | B2 |
| 9782212 | Wham et al. | Oct 2017 | B2 |
| 9782214 | Houser et al. | Oct 2017 | B2 |
| 9788836 | Overmyer et al. | Oct 2017 | B2 |
| 9788851 | Dannaher et al. | Oct 2017 | B2 |
| 9788902 | Inoue et al. | Oct 2017 | B2 |
| 9788907 | Alvi et al. | Oct 2017 | B1 |
| 9795436 | Yates et al. | Oct 2017 | B2 |
| 9801626 | Parihar et al. | Oct 2017 | B2 |
| 9801627 | Harris et al. | Oct 2017 | B2 |
| 9801679 | Trees et al. | Oct 2017 | B2 |
| 9802033 | Hibner et al. | Oct 2017 | B2 |
| 9804618 | Leimbach et al. | Oct 2017 | B2 |
| 9805472 | Chou et al. | Oct 2017 | B2 |
| 9808244 | Leimbach et al. | Nov 2017 | B2 |
| 9808245 | Richard et al. | Nov 2017 | B2 |
| 9808246 | Shelton, IV et al. | Nov 2017 | B2 |
| 9808248 | Hoffman | Nov 2017 | B2 |
| 9814457 | Martin et al. | Nov 2017 | B2 |
| 9814460 | Kimsey et al. | Nov 2017 | B2 |
| 9814462 | Woodard, Jr. et al. | Nov 2017 | B2 |
| 9814463 | Williams et al. | Nov 2017 | B2 |
| 9820699 | Bingley et al. | Nov 2017 | B2 |
| 9820738 | Lytle, IV et al. | Nov 2017 | B2 |
| 9820741 | Kostrzewski | Nov 2017 | B2 |
| 9826976 | Parihar et al. | Nov 2017 | B2 |
| 9826977 | Leimbach et al. | Nov 2017 | B2 |
| 9827054 | Richmond et al. | Nov 2017 | B2 |
| 9827059 | Robinson et al. | Nov 2017 | B2 |
| 9833241 | Huitema et al. | Dec 2017 | B2 |
| 9839419 | Deck et al. | Dec 2017 | B2 |
| 9839424 | Zergiebel et al. | Dec 2017 | B2 |
| 9839428 | Baxter, III et al. | Dec 2017 | B2 |
| 9839470 | Gilbert et al. | Dec 2017 | B2 |
| 9839487 | Dachs, II | Dec 2017 | B2 |
| 9844368 | Boudreaux et al. | Dec 2017 | B2 |
| 9844369 | Huitema et al. | Dec 2017 | B2 |
| 9844374 | Lytle, IV et al. | Dec 2017 | B2 |
| 9844375 | Overmyer et al. | Dec 2017 | B2 |
| 9844379 | Shelton, IV et al. | Dec 2017 | B2 |
| 9848058 | Johnson et al. | Dec 2017 | B2 |
| 9848877 | Shelton, IV et al. | Dec 2017 | B2 |
| 9861354 | Saliman et al. | Jan 2018 | B2 |
| 9861363 | Chen et al. | Jan 2018 | B2 |
| 9861428 | Trees et al. | Jan 2018 | B2 |
| 9867612 | Parihar et al. | Jan 2018 | B2 |
| 9867651 | Wham | Jan 2018 | B2 |
| 9867914 | Bonano et al. | Jan 2018 | B2 |
| 9872609 | Levy | Jan 2018 | B2 |
| 9872683 | Hopkins et al. | Jan 2018 | B2 |
| 9877718 | Weir et al. | Jan 2018 | B2 |
| 9877721 | Schellin et al. | Jan 2018 | B2 |
| 9883860 | Leimbach | Feb 2018 | B2 |
| 9888914 | Martin et al. | Feb 2018 | B2 |
| 9888919 | Leimbach et al. | Feb 2018 | B2 |
| 9888921 | Williams et al. | Feb 2018 | B2 |
| 9895148 | Shelton, IV et al. | Feb 2018 | B2 |
| 9900787 | Ou | Feb 2018 | B2 |
| 9901342 | Shelton, IV et al. | Feb 2018 | B2 |
| 9905000 | Chou et al. | Feb 2018 | B2 |
| 9907550 | Sniffin et al. | Mar 2018 | B2 |
| 9913642 | Leimbach et al. | Mar 2018 | B2 |
| 9913645 | Zerkle et al. | Mar 2018 | B2 |
| 9918778 | Walberg et al. | Mar 2018 | B2 |
| 9918788 | Paul et al. | Mar 2018 | B2 |
| 9922304 | DeBusk et al. | Mar 2018 | B2 |
| 9924941 | Burbank | Mar 2018 | B2 |
| 9924961 | Shelton, IV et al. | Mar 2018 | B2 |
| 9931040 | Homyk et al. | Apr 2018 | B2 |
| 9931118 | Shelton, IV et al. | Apr 2018 | B2 |
| 9931124 | Gokharu | Apr 2018 | B2 |
| 9936942 | Chin et al. | Apr 2018 | B2 |
| 9936955 | Miller et al. | Apr 2018 | B2 |
| 9936961 | Chien et al. | Apr 2018 | B2 |
| 9937012 | Hares et al. | Apr 2018 | B2 |
| 9937014 | Bowling et al. | Apr 2018 | B2 |
| 9937626 | Rockrohr | Apr 2018 | B2 |
| 9938972 | Walley | Apr 2018 | B2 |
| 9943309 | Shelton, IV et al. | Apr 2018 | B2 |
| 9943377 | Yates et al. | Apr 2018 | B2 |
| 9943379 | Gregg, II et al. | Apr 2018 | B2 |
| 9943918 | Grogan et al. | Apr 2018 | B2 |
| 9949785 | Price et al. | Apr 2018 | B2 |
| 9962157 | Sapre | May 2018 | B2 |
| 9968355 | Shelton, IV et al. | May 2018 | B2 |
| 9980778 | Ohline et al. | May 2018 | B2 |
| 9987000 | Shelton, IV et al. | Jun 2018 | B2 |
| 9993248 | Shelton, IV et al. | Jun 2018 | B2 |
| 9993258 | Shelton, IV et al. | Jun 2018 | B2 |
| 10004491 | Martin et al. | Jun 2018 | B2 |
| 10004497 | Overmyer et al. | Jun 2018 | B2 |
| 10004500 | Shelton, IV et al. | Jun 2018 | B2 |
| 10004501 | Shelton, IV et al. | Jun 2018 | B2 |
| 10004527 | Gee et al. | Jun 2018 | B2 |
| D822206 | Shelton, IV et al. | Jul 2018 | S |
| 10010322 | Shelton, IV et al. | Jul 2018 | B2 |
| 10010324 | Huitema et al. | Jul 2018 | B2 |
| 10013049 | Leimbach et al. | Jul 2018 | B2 |
| 10016199 | Baber et al. | Jul 2018 | B2 |
| 10021318 | Hugosson et al. | Jul 2018 | B2 |
| 10022120 | Martin et al. | Jul 2018 | B2 |
| 10022391 | Ruderman Chen et al. | Jul 2018 | B2 |
| 10022568 | Messerly et al. | Jul 2018 | B2 |
| 10028761 | Leimbach et al. | Jul 2018 | B2 |
| 10028788 | Kang | Jul 2018 | B2 |
| 10034704 | Asher et al. | Jul 2018 | B2 |
| 10037641 | Hyde et al. | Jul 2018 | B2 |
| D826405 | Shelton, IV et al. | Aug 2018 | S |
| 10039564 | Hibner et al. | Aug 2018 | B2 |
| 10039565 | Vezzu | Aug 2018 | B2 |
| 10041822 | Zemlok | Aug 2018 | B2 |
| 10044791 | Kamen et al. | Aug 2018 | B2 |
| 10045776 | Shelton, IV et al. | Aug 2018 | B2 |
| 10045779 | Savage et al. | Aug 2018 | B2 |
| 10045781 | Cropper et al. | Aug 2018 | B2 |
| 10045813 | Mueller | Aug 2018 | B2 |
| 10048379 | Markendorf et al. | Aug 2018 | B2 |
| 10052044 | Shelton, IV et al. | Aug 2018 | B2 |
| 10052102 | Baxter, III et al. | Aug 2018 | B2 |
| 10054441 | Schorr et al. | Aug 2018 | B2 |
| 10076326 | Yates et al. | Sep 2018 | B2 |
| 10080618 | Marshall et al. | Sep 2018 | B2 |
| 10085748 | Morgan et al. | Oct 2018 | B2 |
| 10085749 | Cappola et al. | Oct 2018 | B2 |
| 10098527 | Weisenburgh, II et al. | Oct 2018 | B2 |
| 10098635 | Burbank | Oct 2018 | B2 |
| 10098705 | Brisson et al. | Oct 2018 | B2 |
| 10105140 | Malinouskas et al. | Oct 2018 | B2 |
| 10105142 | Baxter, III et al. | Oct 2018 | B2 |
| 10111658 | Chowaniec et al. | Oct 2018 | B2 |
| 10111665 | Aranyi et al. | Oct 2018 | B2 |
| 10111679 | Baber et al. | Oct 2018 | B2 |
| 10117649 | Baxter et al. | Nov 2018 | B2 |
| 10117651 | Whitman et al. | Nov 2018 | B2 |
| 10117702 | Danziger et al. | Nov 2018 | B2 |
| 10118119 | Sappok et al. | Nov 2018 | B2 |
| 10130359 | Hess et al. | Nov 2018 | B2 |
| 10130360 | Olson et al. | Nov 2018 | B2 |
| 10130367 | Cappola et al. | Nov 2018 | B2 |
| 10133248 | Fitzsimmons et al. | Nov 2018 | B2 |
| 10135242 | Baber et al. | Nov 2018 | B2 |
| 10136887 | Shelton, IV et al. | Nov 2018 | B2 |
| 10136949 | Felder et al. | Nov 2018 | B2 |
| 10143526 | Walker et al. | Dec 2018 | B2 |
| 10143948 | Bonifas et al. | Dec 2018 | B2 |
| 10149680 | Parihar et al. | Dec 2018 | B2 |
| 10152789 | Carnes et al. | Dec 2018 | B2 |
| 10159044 | Hrabak | Dec 2018 | B2 |
| 10159481 | Whitman et al. | Dec 2018 | B2 |
| 10159483 | Beckman et al. | Dec 2018 | B2 |
| 10164466 | Calderoni | Dec 2018 | B2 |
| 10166025 | Leimbach et al. | Jan 2019 | B2 |
| 10169862 | Andre et al. | Jan 2019 | B2 |
| 10172687 | Garbus et al. | Jan 2019 | B2 |
| 10175096 | Dickerson | Jan 2019 | B2 |
| 10175127 | Collins et al. | Jan 2019 | B2 |
| 10178992 | Wise et al. | Jan 2019 | B2 |
| 10179413 | Rockrohr | Jan 2019 | B2 |
| 10180463 | Beckman et al. | Jan 2019 | B2 |
| 10182814 | Okoniewski | Jan 2019 | B2 |
| 10182816 | Shelton, IV et al. | Jan 2019 | B2 |
| 10182818 | Hensel et al. | Jan 2019 | B2 |
| 10188385 | Kerr et al. | Jan 2019 | B2 |
| 10189157 | Schlegel et al. | Jan 2019 | B2 |
| 10194907 | Marczyk et al. | Feb 2019 | B2 |
| 10194913 | Nalagatla et al. | Feb 2019 | B2 |
| 10201349 | Leimbach et al. | Feb 2019 | B2 |
| 10201364 | Leimbach et al. | Feb 2019 | B2 |
| 10201365 | Boudreaux et al. | Feb 2019 | B2 |
| 10205708 | Fletcher et al. | Feb 2019 | B1 |
| 10206605 | Shelton, IV et al. | Feb 2019 | B2 |
| 10206752 | Hares et al. | Feb 2019 | B2 |
| 10213201 | Shelton, IV et al. | Feb 2019 | B2 |
| 10213266 | Zemlok et al. | Feb 2019 | B2 |
| 10213268 | Dachs, II | Feb 2019 | B2 |
| 10219491 | Stiles, Jr. et al. | Mar 2019 | B2 |
| 10226250 | Beckman et al. | Mar 2019 | B2 |
| 10226302 | Lacal et al. | Mar 2019 | B2 |
| 10231634 | Zand et al. | Mar 2019 | B2 |
| 10231733 | Ehrenfels et al. | Mar 2019 | B2 |
| 10238413 | Hibner et al. | Mar 2019 | B2 |
| 10245027 | Shelton, IV et al. | Apr 2019 | B2 |
| 10245028 | Shelton, IV et al. | Apr 2019 | B2 |
| 10245029 | Hunter et al. | Apr 2019 | B2 |
| 10245030 | Hunter et al. | Apr 2019 | B2 |
| 10245033 | Overmyer et al. | Apr 2019 | B2 |
| 10245037 | Conklin et al. | Apr 2019 | B2 |
| 10245038 | Hopkins et al. | Apr 2019 | B2 |
| 10251661 | Collings et al. | Apr 2019 | B2 |
| 10258331 | Shelton, IV et al. | Apr 2019 | B2 |
| 10258359 | Kapadia | Apr 2019 | B2 |
| 10258362 | Conlon | Apr 2019 | B2 |
| 10258415 | Harrah et al. | Apr 2019 | B2 |
| 10258418 | Shelton, IV et al. | Apr 2019 | B2 |
| 10258425 | Mustufa et al. | Apr 2019 | B2 |
| 10263171 | Wiener et al. | Apr 2019 | B2 |
| 10265035 | Fehre et al. | Apr 2019 | B2 |
| 10265068 | Harris et al. | Apr 2019 | B2 |
| 10265072 | Shelton, IV et al. | Apr 2019 | B2 |
| 10265090 | Ingmanson et al. | Apr 2019 | B2 |
| 10265130 | Hess et al. | Apr 2019 | B2 |
| 10271840 | Sapre | Apr 2019 | B2 |
| 10271844 | Valentine et al. | Apr 2019 | B2 |
| 10271850 | Williams | Apr 2019 | B2 |
| 10271851 | Shelton, IV et al. | Apr 2019 | B2 |
| D847989 | Shelton, IV et al. | May 2019 | S |
| 10278698 | Racenet | May 2019 | B2 |
| 10278778 | State et al. | May 2019 | B2 |
| 10285698 | Cappola et al. | May 2019 | B2 |
| 10285705 | Shelton, IV et al. | May 2019 | B2 |
| 10292704 | Harris et al. | May 2019 | B2 |
| 10292707 | Shelton, IV et al. | May 2019 | B2 |
| 10292758 | Boudreaux et al. | May 2019 | B2 |
| 10292771 | Wood et al. | May 2019 | B2 |
| 10299792 | Huitema et al. | May 2019 | B2 |
| 10299870 | Connolly et al. | May 2019 | B2 |
| D850617 | Shelton, IV et al. | Jun 2019 | S |
| 10307159 | Harris et al. | Jun 2019 | B2 |
| 10307170 | Parfett et al. | Jun 2019 | B2 |
| 10307199 | Farritor et al. | Jun 2019 | B2 |
| 10311036 | Hussam et al. | Jun 2019 | B1 |
| 10313137 | Aarnio et al. | Jun 2019 | B2 |
| 10314577 | Laurent et al. | Jun 2019 | B2 |
| 10314582 | Shelton, IV et al. | Jun 2019 | B2 |
| 10321907 | Shelton, IV et al. | Jun 2019 | B2 |
| 10321964 | Grover et al. | Jun 2019 | B2 |
| 10327764 | Harris et al. | Jun 2019 | B2 |
| 10335147 | Rector et al. | Jul 2019 | B2 |
| 10335149 | Baxter, III et al. | Jul 2019 | B2 |
| 10335227 | Heard | Jul 2019 | B2 |
| 10342543 | Shelton, IV et al. | Jul 2019 | B2 |
| 10342602 | Strobl et al. | Jul 2019 | B2 |
| 10342623 | Huelman et al. | Jul 2019 | B2 |
| 10343102 | Reasoner et al. | Jul 2019 | B2 |
| 10357246 | Shelton, IV et al. | Jul 2019 | B2 |
| 10357247 | Shelton, IV et al. | Jul 2019 | B2 |
| 10362179 | Harris | Jul 2019 | B2 |
| 10363037 | Aronhalt et al. | Jul 2019 | B2 |
| 10368861 | Baxter, III et al. | Aug 2019 | B2 |
| 10368865 | Harris et al. | Aug 2019 | B2 |
| 10368867 | Harris et al. | Aug 2019 | B2 |
| 10368876 | Bhatnagar et al. | Aug 2019 | B2 |
| 10368894 | Madan et al. | Aug 2019 | B2 |
| 10376263 | Morgan et al. | Aug 2019 | B2 |
| 10376305 | Yates et al. | Aug 2019 | B2 |
| 10376337 | Kilroy et al. | Aug 2019 | B2 |
| 10376338 | Taylor et al. | Aug 2019 | B2 |
| 10378893 | Mankovskii | Aug 2019 | B2 |
| 10383518 | Abu-Tarif et al. | Aug 2019 | B2 |
| 10383699 | Kilroy et al. | Aug 2019 | B2 |
| 10390718 | Chen et al. | Aug 2019 | B2 |
| 10390794 | Kuroiwa et al. | Aug 2019 | B2 |
| 10390825 | Shelton, IV et al. | Aug 2019 | B2 |
| 10390831 | Holsten et al. | Aug 2019 | B2 |
| 10390895 | Henderson et al. | Aug 2019 | B2 |
| 10398434 | Shelton, IV et al. | Sep 2019 | B2 |
| 10398517 | Eckert et al. | Sep 2019 | B2 |
| 10398521 | Itkowitz et al. | Sep 2019 | B2 |
| 10404521 | McChord et al. | Sep 2019 | B2 |
| 10404801 | Martch | Sep 2019 | B2 |
| 10405857 | Shelton, IV et al. | Sep 2019 | B2 |
| 10405863 | Wise et al. | Sep 2019 | B2 |
| 10413291 | Worthington et al. | Sep 2019 | B2 |
| 10413293 | Shelton, IV et al. | Sep 2019 | B2 |
| 10413297 | Harris et al. | Sep 2019 | B2 |
| 10417446 | Takeyama | Sep 2019 | B2 |
| 10420552 | Shelton, IV et al. | Sep 2019 | B2 |
| 10420558 | Nalagatla et al. | Sep 2019 | B2 |
| 10420559 | Marczyk et al. | Sep 2019 | B2 |
| 10420620 | Rockrohr | Sep 2019 | B2 |
| 10420865 | Reasoner et al. | Sep 2019 | B2 |
| 10422727 | Pliskin | Sep 2019 | B2 |
| 10426466 | Contini et al. | Oct 2019 | B2 |
| 10426467 | Miller et al. | Oct 2019 | B2 |
| 10426468 | Contini et al. | Oct 2019 | B2 |
| 10426471 | Shelton, IV et al. | Oct 2019 | B2 |
| 10433837 | Worthington et al. | Oct 2019 | B2 |
| 10433844 | Shelton, IV et al. | Oct 2019 | B2 |
| 10433849 | Shelton, IV et al. | Oct 2019 | B2 |
| 10441279 | Shelton, IV et al. | Oct 2019 | B2 |
| 10441345 | Aldridge et al. | Oct 2019 | B2 |
| 10448948 | Shelton, IV et al. | Oct 2019 | B2 |
| 10448950 | Shelton, IV et al. | Oct 2019 | B2 |
| 10456137 | Vendely et al. | Oct 2019 | B2 |
| 10456140 | Shelton, IV et al. | Oct 2019 | B2 |
| 10456193 | Yates et al. | Oct 2019 | B2 |
| 10463365 | Williams | Nov 2019 | B2 |
| 10463367 | Kostrzewski et al. | Nov 2019 | B2 |
| 10463371 | Kostrzewski | Nov 2019 | B2 |
| 10463436 | Jackson et al. | Nov 2019 | B2 |
| 10470762 | Leimbach et al. | Nov 2019 | B2 |
| 10470764 | Baxter, III et al. | Nov 2019 | B2 |
| 10470768 | Harris et al. | Nov 2019 | B2 |
| 10470791 | Houser | Nov 2019 | B2 |
| 10471254 | Sano et al. | Nov 2019 | B2 |
| 10478181 | Shelton, IV et al. | Nov 2019 | B2 |
| 10478190 | Miller et al. | Nov 2019 | B2 |
| 10478544 | Friederichs et al. | Nov 2019 | B2 |
| 10485450 | Gupta et al. | Nov 2019 | B2 |
| 10485542 | Shelton, IV et al. | Nov 2019 | B2 |
| 10485543 | Shelton, IV et al. | Nov 2019 | B2 |
| 10492783 | Shelton, IV et al. | Dec 2019 | B2 |
| 10492785 | Overmyer et al. | Dec 2019 | B2 |
| 10496788 | Amarasingham et al. | Dec 2019 | B2 |
| 10498269 | Zemlok et al. | Dec 2019 | B2 |
| 10499891 | Chaplin et al. | Dec 2019 | B2 |
| 10499914 | Huang et al. | Dec 2019 | B2 |
| 10499915 | Aranyi | Dec 2019 | B2 |
| 10499994 | Luks et al. | Dec 2019 | B2 |
| 10507068 | Kopp et al. | Dec 2019 | B2 |
| 10512461 | Gupta et al. | Dec 2019 | B2 |
| 10517588 | Gupta et al. | Dec 2019 | B2 |
| 10517595 | Hunter et al. | Dec 2019 | B2 |
| 10517596 | Hunter et al. | Dec 2019 | B2 |
| 10517686 | Vokrot et al. | Dec 2019 | B2 |
| 10524789 | Swayze et al. | Jan 2020 | B2 |
| 10531874 | Morgan et al. | Jan 2020 | B2 |
| 10531929 | Widenhouse et al. | Jan 2020 | B2 |
| 10532330 | Diallo et al. | Jan 2020 | B2 |
| 10536617 | Liang et al. | Jan 2020 | B2 |
| 10537324 | Shelton, IV et al. | Jan 2020 | B2 |
| 10537325 | Bakos et al. | Jan 2020 | B2 |
| 10537351 | Shelton, IV et al. | Jan 2020 | B2 |
| 10542979 | Shelton, IV et al. | Jan 2020 | B2 |
| 10542982 | Beckman et al. | Jan 2020 | B2 |
| 10542991 | Shelton, IV et al. | Jan 2020 | B2 |
| 10548504 | Shelton, IV et al. | Feb 2020 | B2 |
| 10548612 | Martinez et al. | Feb 2020 | B2 |
| 10552574 | Sweeney | Feb 2020 | B2 |
| 10555675 | Satish et al. | Feb 2020 | B2 |
| 10555748 | Yates et al. | Feb 2020 | B2 |
| 10555750 | Conlon et al. | Feb 2020 | B2 |
| 10555769 | Worrell et al. | Feb 2020 | B2 |
| 10561422 | Schellin et al. | Feb 2020 | B2 |
| 10561471 | Nichogi | Feb 2020 | B2 |
| 10568625 | Harris et al. | Feb 2020 | B2 |
| 10568626 | Shelton, IV et al. | Feb 2020 | B2 |
| 10568632 | Miller et al. | Feb 2020 | B2 |
| 10575868 | Hall et al. | Mar 2020 | B2 |
| 10582928 | Hunter et al. | Mar 2020 | B2 |
| 10582931 | Mujawar | Mar 2020 | B2 |
| 10586074 | Rose et al. | Mar 2020 | B2 |
| 10588625 | Weaner et al. | Mar 2020 | B2 |
| 10588629 | Malinouskas et al. | Mar 2020 | B2 |
| 10588630 | Shelton, IV et al. | Mar 2020 | B2 |
| 10588631 | Shelton, IV et al. | Mar 2020 | B2 |
| 10588632 | Shelton, IV et al. | Mar 2020 | B2 |
| 10595882 | Parfett et al. | Mar 2020 | B2 |
| 10595887 | Shelton, IV et al. | Mar 2020 | B2 |
| 10595952 | Forrest et al. | Mar 2020 | B2 |
| 10602848 | Magana | Mar 2020 | B2 |
| 10603036 | Hunter et al. | Mar 2020 | B2 |
| 10603128 | Zergiebel et al. | Mar 2020 | B2 |
| 10610223 | Wellman et al. | Apr 2020 | B2 |
| 10610224 | Shelton, IV et al. | Apr 2020 | B2 |
| 10610286 | Wiener et al. | Apr 2020 | B2 |
| 10610313 | Bailey et al. | Apr 2020 | B2 |
| 10617412 | Shelton, IV et al. | Apr 2020 | B2 |
| 10617414 | Shelton, IV et al. | Apr 2020 | B2 |
| 10617484 | Kilroy et al. | Apr 2020 | B2 |
| 10624635 | Harris et al. | Apr 2020 | B2 |
| 10624691 | Wiener et al. | Apr 2020 | B2 |
| 10631423 | Collins et al. | Apr 2020 | B2 |
| 10631916 | Horner et al. | Apr 2020 | B2 |
| 10631917 | Ineson | Apr 2020 | B2 |
| 10631939 | Dachs, II et al. | Apr 2020 | B2 |
| 10639027 | Shelton, IV et al. | May 2020 | B2 |
| 10639034 | Harris et al. | May 2020 | B2 |
| 10639035 | Shelton, IV et al. | May 2020 | B2 |
| 10639036 | Yates et al. | May 2020 | B2 |
| 10639037 | Shelton, IV et al. | May 2020 | B2 |
| 10639039 | Vendely et al. | May 2020 | B2 |
| 10639111 | Kopp | May 2020 | B2 |
| 10639185 | Agrawal et al. | May 2020 | B2 |
| 10653413 | Worthington et al. | May 2020 | B2 |
| 10653476 | Ross | May 2020 | B2 |
| 10653489 | Kopp | May 2020 | B2 |
| 10667809 | Bakos et al. | Jun 2020 | B2 |
| 10667810 | Shelton, IV et al. | Jun 2020 | B2 |
| 10667811 | Harris et al. | Jun 2020 | B2 |
| 10667877 | Kapadia | Jun 2020 | B2 |
| 10674897 | Levy | Jun 2020 | B2 |
| 10675021 | Harris et al. | Jun 2020 | B2 |
| 10675023 | Cappola | Jun 2020 | B2 |
| 10675024 | Shelton, IV et al. | Jun 2020 | B2 |
| 10675025 | Swayze et al. | Jun 2020 | B2 |
| 10675026 | Harris et al. | Jun 2020 | B2 |
| 10675104 | Kapadia | Jun 2020 | B2 |
| 10677764 | Ross et al. | Jun 2020 | B2 |
| 10679758 | Fox et al. | Jun 2020 | B2 |
| 10682136 | Harris et al. | Jun 2020 | B2 |
| 10682138 | Shelton, IV et al. | Jun 2020 | B2 |
| 10686805 | Reybok, Jr. et al. | Jun 2020 | B2 |
| 10687806 | Shelton, IV et al. | Jun 2020 | B2 |
| 10687809 | Shelton, IV et al. | Jun 2020 | B2 |
| 10687810 | Shelton, IV et al. | Jun 2020 | B2 |
| 10687884 | Wiener et al. | Jun 2020 | B2 |
| 10687905 | Kostrzewski | Jun 2020 | B2 |
| 10695055 | Shelton, IV et al. | Jun 2020 | B2 |
| 10695081 | Shelton, IV et al. | Jun 2020 | B2 |
| 10695134 | Barral et al. | Jun 2020 | B2 |
| 10702270 | Shelton, IV et al. | Jul 2020 | B2 |
| 10702271 | Aranyi et al. | Jul 2020 | B2 |
| 10709446 | Harris et al. | Jul 2020 | B2 |
| 10716615 | Shelton, IV et al. | Jul 2020 | B2 |
| 10716639 | Kapadia et al. | Jul 2020 | B2 |
| 10717194 | Griffiths et al. | Jul 2020 | B2 |
| 10722222 | Aranyi | Jul 2020 | B2 |
| 10722233 | Wellman | Jul 2020 | B2 |
| 10729458 | Stoddard et al. | Aug 2020 | B2 |
| 10733267 | Pedersen | Aug 2020 | B2 |
| 10736219 | Seow et al. | Aug 2020 | B2 |
| 10736616 | Scheib et al. | Aug 2020 | B2 |
| 10736628 | Yates et al. | Aug 2020 | B2 |
| 10736629 | Shelton, IV et al. | Aug 2020 | B2 |
| 10736636 | Baxter, III et al. | Aug 2020 | B2 |
| 10736705 | Scheib et al. | Aug 2020 | B2 |
| 10743872 | Leimbach et al. | Aug 2020 | B2 |
| 10748115 | Laster et al. | Aug 2020 | B2 |
| 10751136 | Farritor et al. | Aug 2020 | B2 |
| 10751768 | Hersey et al. | Aug 2020 | B2 |
| 10755813 | Shelton, IV et al. | Aug 2020 | B2 |
| 10758229 | Shelton, IV et al. | Sep 2020 | B2 |
| 10758230 | Shelton, IV et al. | Sep 2020 | B2 |
| 10758294 | Jones | Sep 2020 | B2 |
| 10758310 | Shelton, IV et al. | Sep 2020 | B2 |
| 10765376 | Brown, III et al. | Sep 2020 | B2 |
| 10765424 | Baxter, III et al. | Sep 2020 | B2 |
| 10765427 | Shelton, IV et al. | Sep 2020 | B2 |
| 10765470 | Yates et al. | Sep 2020 | B2 |
| 10772651 | Shelton, IV et al. | Sep 2020 | B2 |
| 10772688 | Peine et al. | Sep 2020 | B2 |
| 20020049551 | Friedman et al. | Apr 2002 | A1 |
| 20030093503 | Yamaki et al. | May 2003 | A1 |
| 20040078236 | Stoodley et al. | Apr 2004 | A1 |
| 20040199180 | Knodel et al. | Oct 2004 | A1 |
| 20040199659 | Ishikawa et al. | Oct 2004 | A1 |
| 20040243148 | Wasielewski | Dec 2004 | A1 |
| 20040243435 | Williams | Dec 2004 | A1 |
| 20050063575 | Ma et al. | Mar 2005 | A1 |
| 20050065438 | Miller | Mar 2005 | A1 |
| 20050131390 | Heinrich et al. | Jun 2005 | A1 |
| 20050149001 | Uchikubo et al. | Jul 2005 | A1 |
| 20050149356 | Cyr et al. | Jul 2005 | A1 |
| 20050222631 | Dalal et al. | Oct 2005 | A1 |
| 20050277913 | McCary | Dec 2005 | A1 |
| 20060020272 | Gildenberg | Jan 2006 | A1 |
| 20060116908 | Dew et al. | Jun 2006 | A1 |
| 20060241399 | Fabian | Oct 2006 | A1 |
| 20070010838 | Shelton et al. | Jan 2007 | A1 |
| 20070016235 | Tanaka et al. | Jan 2007 | A1 |
| 20070027459 | Horvath et al. | Feb 2007 | A1 |
| 20070078678 | DiSilvestro et al. | Apr 2007 | A1 |
| 20070167702 | Hasser et al. | Jul 2007 | A1 |
| 20070168461 | Moore | Jul 2007 | A1 |
| 20070175955 | Shelton et al. | Aug 2007 | A1 |
| 20070225556 | Ortiz et al. | Sep 2007 | A1 |
| 20070244478 | Bahney | Oct 2007 | A1 |
| 20070249990 | Cosmescu | Oct 2007 | A1 |
| 20070270660 | Caylor et al. | Nov 2007 | A1 |
| 20070293218 | Meylan et al. | Dec 2007 | A1 |
| 20080013460 | Allen et al. | Jan 2008 | A1 |
| 20080015664 | Podhajsky | Jan 2008 | A1 |
| 20080015912 | Rosenthal et al. | Jan 2008 | A1 |
| 20080033404 | Romoda et al. | Feb 2008 | A1 |
| 20080040151 | Moore | Feb 2008 | A1 |
| 20080059658 | Williams | Mar 2008 | A1 |
| 20080083414 | Messerges | Apr 2008 | A1 |
| 20080255413 | Zemlok et al. | Oct 2008 | A1 |
| 20080262654 | Omori et al. | Oct 2008 | A1 |
| 20080281678 | Keuls | Nov 2008 | A1 |
| 20080296346 | Shelton, IV et al. | Dec 2008 | A1 |
| 20090036750 | Weinstein et al. | Feb 2009 | A1 |
| 20090036794 | Stubhaug et al. | Feb 2009 | A1 |
| 20090043253 | Podaima | Feb 2009 | A1 |
| 20090046146 | Hoyt | Feb 2009 | A1 |
| 20090076409 | Wu et al. | Mar 2009 | A1 |
| 20090090763 | Zemlok et al. | Apr 2009 | A1 |
| 20090099866 | Newman | Apr 2009 | A1 |
| 20090182577 | Squilla et al. | Jul 2009 | A1 |
| 20090206131 | Weisenburgh, II et al. | Aug 2009 | A1 |
| 20090259149 | Tahara et al. | Oct 2009 | A1 |
| 20090259221 | Tahara et al. | Oct 2009 | A1 |
| 20090307681 | Armado et al. | Dec 2009 | A1 |
| 20090326321 | Jacobsen et al. | Dec 2009 | A1 |
| 20090326336 | Lemke et al. | Dec 2009 | A1 |
| 20100065604 | Weng | Mar 2010 | A1 |
| 20100070417 | Flynn et al. | Mar 2010 | A1 |
| 20100132334 | Duclos et al. | Jun 2010 | A1 |
| 20100191100 | Anderson et al. | Jul 2010 | A1 |
| 20100198248 | Vakharia | Aug 2010 | A1 |
| 20100217991 | Choi | Aug 2010 | A1 |
| 20100235689 | Tian et al. | Sep 2010 | A1 |
| 20100250571 | Pierce et al. | Sep 2010 | A1 |
| 20100292535 | Paskar | Nov 2010 | A1 |
| 20110087238 | Wang et al. | Apr 2011 | A1 |
| 20110105895 | Kornblau et al. | May 2011 | A1 |
| 20110118708 | Burbank et al. | May 2011 | A1 |
| 20110119075 | Dhoble | May 2011 | A1 |
| 20110125149 | El-Galley et al. | May 2011 | A1 |
| 20110237883 | Chun | Sep 2011 | A1 |
| 20110306840 | Allen et al. | Dec 2011 | A1 |
| 20120116381 | Houser et al. | May 2012 | A1 |
| 20120130217 | Kauphusman et al. | May 2012 | A1 |
| 20120191091 | Allen | Jul 2012 | A1 |
| 20120203785 | Awada | Aug 2012 | A1 |
| 20120211542 | Racenet | Aug 2012 | A1 |
| 20120245958 | Lawrence et al. | Sep 2012 | A1 |
| 20120292367 | Morgan et al. | Nov 2012 | A1 |
| 20120319859 | Taub et al. | Dec 2012 | A1 |
| 20130024213 | Poon | Jan 2013 | A1 |
| 20130046279 | Niklewski et al. | Feb 2013 | A1 |
| 20130066647 | Andrie et al. | Mar 2013 | A1 |
| 20130090526 | Suzuki et al. | Apr 2013 | A1 |
| 20130093829 | Rosenblatt et al. | Apr 2013 | A1 |
| 20130105552 | Weir et al. | May 2013 | A1 |
| 20130116218 | Kaplan et al. | May 2013 | A1 |
| 20130165776 | Blomqvist | Jun 2013 | A1 |
| 20130178853 | Hyink et al. | Jul 2013 | A1 |
| 20130206813 | Nalagatla | Aug 2013 | A1 |
| 20130214025 | Zemlok et al. | Aug 2013 | A1 |
| 20130253480 | Kimball et al. | Sep 2013 | A1 |
| 20130256373 | Schmid et al. | Oct 2013 | A1 |
| 20130277410 | Fernandez et al. | Oct 2013 | A1 |
| 20130317837 | Ballantyne et al. | Nov 2013 | A1 |
| 20130321425 | Greene et al. | Dec 2013 | A1 |
| 20130325809 | Kim et al. | Dec 2013 | A1 |
| 20130331875 | Ross et al. | Dec 2013 | A1 |
| 20140001234 | Shelton, IV et al. | Jan 2014 | A1 |
| 20140006132 | Barker | Jan 2014 | A1 |
| 20140006943 | Robbins et al. | Jan 2014 | A1 |
| 20140029411 | Nayak et al. | Jan 2014 | A1 |
| 20140035762 | Shelton, IV et al. | Feb 2014 | A1 |
| 20140066700 | Wilson et al. | Mar 2014 | A1 |
| 20140081255 | Johnson et al. | Mar 2014 | A1 |
| 20140081659 | Nawana | Mar 2014 | A1 |
| 20140087999 | Kaplan et al. | Mar 2014 | A1 |
| 20140092089 | Kasuya et al. | Apr 2014 | A1 |
| 20140107697 | Patani et al. | Apr 2014 | A1 |
| 20140108983 | William R et al. | Apr 2014 | A1 |
| 20140187856 | Holoien et al. | Jul 2014 | A1 |
| 20140204190 | Rosenblatt, III et al. | Jul 2014 | A1 |
| 20140246475 | Hall et al. | Sep 2014 | A1 |
| 20140249557 | Koch et al. | Sep 2014 | A1 |
| 20140252064 | Mozdzierz et al. | Sep 2014 | A1 |
| 20140263541 | Leimbach et al. | Sep 2014 | A1 |
| 20140263552 | Hall et al. | Sep 2014 | A1 |
| 20140303660 | Boyden et al. | Oct 2014 | A1 |
| 20150025549 | Kilroy et al. | Jan 2015 | A1 |
| 20150032150 | Ishida et al. | Jan 2015 | A1 |
| 20150051617 | Takemura et al. | Feb 2015 | A1 |
| 20150053737 | Leimbach et al. | Feb 2015 | A1 |
| 20150066000 | An et al. | Mar 2015 | A1 |
| 20150070187 | Wiesner et al. | Mar 2015 | A1 |
| 20150108198 | Estrella | Apr 2015 | A1 |
| 20150122870 | Zemlok et al. | May 2015 | A1 |
| 20150133945 | Dushyant et al. | May 2015 | A1 |
| 20150199109 | Lee | Jul 2015 | A1 |
| 20150238355 | Vezzu et al. | Aug 2015 | A1 |
| 20150272557 | Overmyer et al. | Oct 2015 | A1 |
| 20150272571 | Leimbach et al. | Oct 2015 | A1 |
| 20150272580 | Leimbach et al. | Oct 2015 | A1 |
| 20150272582 | Leimbach et al. | Oct 2015 | A1 |
| 20150297200 | Fitzsimmons et al. | Oct 2015 | A1 |
| 20150297222 | Huitema et al. | Oct 2015 | A1 |
| 20150297228 | Huitema et al. | Oct 2015 | A1 |
| 20150297233 | Huitema et al. | Oct 2015 | A1 |
| 20150297311 | Tesar | Oct 2015 | A1 |
| 20150302157 | Collar et al. | Oct 2015 | A1 |
| 20150310174 | Coudert et al. | Oct 2015 | A1 |
| 20150313538 | Bechtel et al. | Nov 2015 | A1 |
| 20150317899 | Dumbauld et al. | Nov 2015 | A1 |
| 20150332003 | Stamm et al. | Nov 2015 | A1 |
| 20150332196 | Stiller et al. | Nov 2015 | A1 |
| 20160000437 | Giordano et al. | Jan 2016 | A1 |
| 20160015471 | Piron et al. | Jan 2016 | A1 |
| 20160034648 | Mohlenbrock et al. | Feb 2016 | A1 |
| 20160038253 | Piron et al. | Feb 2016 | A1 |
| 20160066913 | Swayze et al. | Mar 2016 | A1 |
| 20160106934 | Hiraga et al. | Apr 2016 | A1 |
| 20160192960 | Bueno et al. | Jul 2016 | A1 |
| 20160206202 | Frangioni | Jul 2016 | A1 |
| 20160235303 | Fleming et al. | Aug 2016 | A1 |
| 20160249910 | Shelton, IV et al. | Sep 2016 | A1 |
| 20160296246 | Schaller | Oct 2016 | A1 |
| 20160302210 | Thornton et al. | Oct 2016 | A1 |
| 20160310055 | Zand et al. | Oct 2016 | A1 |
| 20160310203 | Gaspredes et al. | Oct 2016 | A1 |
| 20160321400 | Durrant et al. | Nov 2016 | A1 |
| 20160323283 | Kang et al. | Nov 2016 | A1 |
| 20160324537 | Green et al. | Nov 2016 | A1 |
| 20160342916 | Arceneaux et al. | Nov 2016 | A1 |
| 20160350490 | Martinez et al. | Dec 2016 | A1 |
| 20160374665 | DiNardo et al. | Dec 2016 | A1 |
| 20160374723 | Frankhouser et al. | Dec 2016 | A1 |
| 20160374762 | Case et al. | Dec 2016 | A1 |
| 20160374775 | Prpa | Dec 2016 | A1 |
| 20170000516 | Stulen et al. | Jan 2017 | A1 |
| 20170000553 | Wiener et al. | Jan 2017 | A1 |
| 20170027603 | Pandey | Feb 2017 | A1 |
| 20170068792 | Reiner | Mar 2017 | A1 |
| 20170086829 | Vendely et al. | Mar 2017 | A1 |
| 20170086930 | Thompson et al. | Mar 2017 | A1 |
| 20170105754 | Boudreaux et al. | Apr 2017 | A1 |
| 20170132374 | Lee et al. | May 2017 | A1 |
| 20170132785 | Wshah et al. | May 2017 | A1 |
| 20170143284 | Sehnert et al. | May 2017 | A1 |
| 20170143442 | Tesar et al. | May 2017 | A1 |
| 20170151026 | Panescu et al. | Jun 2017 | A1 |
| 20170156076 | Eom et al. | Jun 2017 | A1 |
| 20170164997 | Johnson et al. | Jun 2017 | A1 |
| 20170165012 | Chaplin et al. | Jun 2017 | A1 |
| 20170172565 | Heneveld | Jun 2017 | A1 |
| 20170172614 | Scheib et al. | Jun 2017 | A1 |
| 20170177807 | Fabian | Jun 2017 | A1 |
| 20170181745 | Penna et al. | Jun 2017 | A1 |
| 20170196637 | Shelton, IV et al. | Jul 2017 | A1 |
| 20170202591 | Shelton, IV et al. | Jul 2017 | A1 |
| 20170202605 | Shelton, IV et al. | Jul 2017 | A1 |
| 20170202607 | Shelton, IV et al. | Jul 2017 | A1 |
| 20170224332 | Hunter et al. | Aug 2017 | A1 |
| 20170224334 | Worthington et al. | Aug 2017 | A1 |
| 20170224428 | Kopp | Aug 2017 | A1 |
| 20170231627 | Shelton, IV et al. | Aug 2017 | A1 |
| 20170231628 | Shelton, IV et al. | Aug 2017 | A1 |
| 20170249432 | Grantcharov | Aug 2017 | A1 |
| 20170252095 | Johnson | Sep 2017 | A1 |
| 20170255751 | Sanmugalingham | Sep 2017 | A1 |
| 20170281171 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170281173 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170281186 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170281187 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170281189 | Nalagatla et al. | Oct 2017 | A1 |
| 20170290585 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170296169 | Yates et al. | Oct 2017 | A1 |
| 20170296173 | Shelton, IV et al. | Oct 2017 | A1 |
| 20170296177 | Harris et al. | Oct 2017 | A1 |
| 20170296185 | Swensgard et al. | Oct 2017 | A1 |
| 20170296213 | Swensgard et al. | Oct 2017 | A1 |
| 20170303984 | Malackowski | Oct 2017 | A1 |
| 20170304020 | Ng et al. | Oct 2017 | A1 |
| 20170360439 | Chen et al. | Dec 2017 | A1 |
| 20170360499 | Greep et al. | Dec 2017 | A1 |
| 20170367695 | Shelton, IV et al. | Dec 2017 | A1 |
| 20170367697 | Shelton, IV et al. | Dec 2017 | A1 |
| 20170367754 | Narisawa | Dec 2017 | A1 |
| 20180008359 | Randle | Jan 2018 | A1 |
| 20180014848 | Messerly et al. | Jan 2018 | A1 |
| 20180049817 | Swayze et al. | Feb 2018 | A1 |
| 20180050196 | Pawsey et al. | Feb 2018 | A1 |
| 20180055529 | Messerly et al. | Mar 2018 | A1 |
| 20180065248 | Barral et al. | Mar 2018 | A1 |
| 20180092706 | Anderson et al. | Apr 2018 | A1 |
| 20180098816 | Govari et al. | Apr 2018 | A1 |
| 20180110523 | Shelton, IV | Apr 2018 | A1 |
| 20180116662 | Shelton, IV et al. | May 2018 | A1 |
| 20180122506 | Grantcharov et al. | May 2018 | A1 |
| 20180125590 | Giordano et al. | May 2018 | A1 |
| 20180132895 | Silver | May 2018 | A1 |
| 20180153574 | Faller et al. | Jun 2018 | A1 |
| 20180153628 | Grover et al. | Jun 2018 | A1 |
| 20180153632 | Tokarchuk et al. | Jun 2018 | A1 |
| 20180161716 | Li et al. | Jun 2018 | A1 |
| 20180168575 | Simms et al. | Jun 2018 | A1 |
| 20180168577 | Aronhalt et al. | Jun 2018 | A1 |
| 20180168578 | Aronhalt et al. | Jun 2018 | A1 |
| 20180168579 | Aronhalt et al. | Jun 2018 | A1 |
| 20180168584 | Harris et al. | Jun 2018 | A1 |
| 20180168586 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168589 | Swayze et al. | Jun 2018 | A1 |
| 20180168590 | Overmyer et al. | Jun 2018 | A1 |
| 20180168592 | Overmyer et al. | Jun 2018 | A1 |
| 20180168593 | Overmyer et al. | Jun 2018 | A1 |
| 20180168597 | Fanelli et al. | Jun 2018 | A1 |
| 20180168598 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168601 | Bakos et al. | Jun 2018 | A1 |
| 20180168603 | Morgan et al. | Jun 2018 | A1 |
| 20180168605 | Baber et al. | Jun 2018 | A1 |
| 20180168607 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168608 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168609 | Fanelli et al. | Jun 2018 | A1 |
| 20180168610 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168614 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168615 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168616 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168617 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168618 | Scott et al. | Jun 2018 | A1 |
| 20180168619 | Scott et al. | Jun 2018 | A1 |
| 20180168623 | Simms et al. | Jun 2018 | A1 |
| 20180168625 | Posada et al. | Jun 2018 | A1 |
| 20180168627 | Weaner et al. | Jun 2018 | A1 |
| 20180168628 | Hunter et al. | Jun 2018 | A1 |
| 20180168629 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168632 | Harris et al. | Jun 2018 | A1 |
| 20180168633 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168639 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168647 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168648 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168649 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168650 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180168651 | Shelton, IV et al. | Jun 2018 | A1 |
| 20180177557 | Kapadia et al. | Jun 2018 | A1 |
| 20180199995 | Odermatt et al. | Jul 2018 | A1 |
| 20180214025 | Homyk et al. | Aug 2018 | A1 |
| 20180221598 | Silver | Aug 2018 | A1 |
| 20180228557 | Darisse et al. | Aug 2018 | A1 |
| 20180242967 | Meade | Aug 2018 | A1 |
| 20180250080 | Kopp | Sep 2018 | A1 |
| 20180250084 | Kopp et al. | Sep 2018 | A1 |
| 20180263710 | Sakaguchi et al. | Sep 2018 | A1 |
| 20180263717 | Kopp | Sep 2018 | A1 |
| 20180271603 | Nir et al. | Sep 2018 | A1 |
| 20180296286 | Peine et al. | Oct 2018 | A1 |
| 20180304471 | Tokuchi | Oct 2018 | A1 |
| 20180310935 | Wixey | Nov 2018 | A1 |
| 20180310986 | Batchelor et al. | Nov 2018 | A1 |
| 20180317826 | Muhsin et al. | Nov 2018 | A1 |
| 20180317915 | McDonald, II | Nov 2018 | A1 |
| 20180338806 | Grubbs | Nov 2018 | A1 |
| 20180358112 | Sharifi Sedeh et al. | Dec 2018 | A1 |
| 20180360449 | Shelton, IV et al. | Dec 2018 | A1 |
| 20180360452 | Shelton, IV et al. | Dec 2018 | A1 |
| 20180360454 | Shelton, IV et al. | Dec 2018 | A1 |
| 20180360456 | Shelton, IV et al. | Dec 2018 | A1 |
| 20180368930 | Esterberg et al. | Dec 2018 | A1 |
| 20180369511 | Zergiebel et al. | Dec 2018 | A1 |
| 20190000446 | Shelton, IV et al. | Jan 2019 | A1 |
| 20190000448 | Shelton, IV et al. | Jan 2019 | A1 |
| 20190000478 | Messerly et al. | Jan 2019 | A1 |
| 20190000530 | Yates et al. | Jan 2019 | A1 |
| 20190000565 | Shelton, IV et al. | Jan 2019 | A1 |
| 20190000569 | Crawford et al. | Jan 2019 | A1 |
| 20190001079 | Zergiebel et al. | Jan 2019 | A1 |
| 20190005641 | Yamamoto | Jan 2019 | A1 |
| 20190006047 | Gorek et al. | Jan 2019 | A1 |
| 20190008600 | Pedros et al. | Jan 2019 | A1 |
| 20190029712 | Stoddard et al. | Jan 2019 | A1 |
| 20190038364 | Enoki | Feb 2019 | A1 |
| 20190053801 | Wixey et al. | Feb 2019 | A1 |
| 20190053866 | Seow et al. | Feb 2019 | A1 |
| 20190069949 | Vrba et al. | Mar 2019 | A1 |
| 20190069962 | Tabandeh et al. | Mar 2019 | A1 |
| 20190069964 | Hagn | Mar 2019 | A1 |
| 20190070550 | Lalomia et al. | Mar 2019 | A1 |
| 20190070731 | Bowling et al. | Mar 2019 | A1 |
| 20190090969 | Jarc et al. | Mar 2019 | A1 |
| 20190099227 | Rockrohr | Apr 2019 | A1 |
| 20190104919 | Shelton, IV et al. | Apr 2019 | A1 |
| 20190125320 | Shelton, IV et al. | May 2019 | A1 |
| 20190125321 | Shelton, IV et al. | May 2019 | A1 |
| 20190125324 | Scheib et al. | May 2019 | A1 |
| 20190125335 | Shelton, IV et al. | May 2019 | A1 |
| 20190125336 | Deck et al. | May 2019 | A1 |
| 20190125337 | Shelton, IV et al. | May 2019 | A1 |
| 20190125338 | Shelton, IV et al. | May 2019 | A1 |
| 20190125339 | Shelton, IV et al. | May 2019 | A1 |
| 20190125344 | DiNardo et al. | May 2019 | A1 |
| 20190125347 | Stokes et al. | May 2019 | A1 |
| 20190125348 | Shelton, IV et al. | May 2019 | A1 |
| 20190125352 | Shelton, IV et al. | May 2019 | A1 |
| 20190125353 | Shelton, IV et al. | May 2019 | A1 |
| 20190125354 | Deck et al. | May 2019 | A1 |
| 20190125355 | Shelton, IV et al. | May 2019 | A1 |
| 20190125356 | Shelton, IV et al. | May 2019 | A1 |
| 20190125357 | Shelton, IV et al. | May 2019 | A1 |
| 20190125358 | Shelton, IV et al. | May 2019 | A1 |
| 20190125359 | Shelton, IV et al. | May 2019 | A1 |
| 20190125360 | Shelton, IV et al. | May 2019 | A1 |
| 20190125361 | Shelton, IV et al. | May 2019 | A1 |
| 20190125377 | Shelton, IV | May 2019 | A1 |
| 20190125378 | Shelton, IV et al. | May 2019 | A1 |
| 20190125379 | Shelton, IV et al. | May 2019 | A1 |
| 20190125381 | Scheib et al. | May 2019 | A1 |
| 20190125383 | Scheib et al. | May 2019 | A1 |
| 20190125384 | Scheib et al. | May 2019 | A1 |
| 20190125385 | Scheib et al. | May 2019 | A1 |
| 20190125386 | Shelton, IV et al. | May 2019 | A1 |
| 20190125387 | Parihar et al. | May 2019 | A1 |
| 20190125388 | Shelton, IV et al. | May 2019 | A1 |
| 20190125389 | Shelton, IV et al. | May 2019 | A1 |
| 20190125430 | Shelton, IV et al. | May 2019 | A1 |
| 20190125431 | Shelton, IV et al. | May 2019 | A1 |
| 20190125432 | Shelton, IV et al. | May 2019 | A1 |
| 20190125454 | Stokes et al. | May 2019 | A1 |
| 20190125455 | Shelton, IV et al. | May 2019 | A1 |
| 20190125456 | Shelton, IV et al. | May 2019 | A1 |
| 20190125457 | Parihar et al. | May 2019 | A1 |
| 20190125458 | Shelton, IV et al. | May 2019 | A1 |
| 20190125459 | Shelton, IV et al. | May 2019 | A1 |
| 20190125476 | Shelton, IV et al. | May 2019 | A1 |
| 20190133703 | Seow et al. | May 2019 | A1 |
| 20190142449 | Shelton, IV et al. | May 2019 | A1 |
| 20190142535 | Seow et al. | May 2019 | A1 |
| 20190145942 | Dutriez et al. | May 2019 | A1 |
| 20190150975 | Kawasaki et al. | May 2019 | A1 |
| 20190159778 | Shelton, IV et al. | May 2019 | A1 |
| 20190162179 | O'Shea et al. | May 2019 | A1 |
| 20190164285 | Nye et al. | May 2019 | A1 |
| 20190192157 | Scott et al. | Jun 2019 | A1 |
| 20190192236 | Shelton, IV et al. | Jun 2019 | A1 |
| 20190200844 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200863 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200905 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200906 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200977 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200980 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200981 | Harris et al. | Jul 2019 | A1 |
| 20190200984 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200985 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200986 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200987 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200988 | Shelton, IV | Jul 2019 | A1 |
| 20190200996 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200997 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190200998 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201020 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201021 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201023 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201024 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201025 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201026 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201027 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201028 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201029 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201030 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201033 | Yates et al. | Jul 2019 | A1 |
| 20190201034 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201036 | Nott et al. | Jul 2019 | A1 |
| 20190201037 | Houser et al. | Jul 2019 | A1 |
| 20190201038 | Yates et al. | Jul 2019 | A1 |
| 20190201039 | Widenhouse et al. | Jul 2019 | A1 |
| 20190201040 | Messerly et al. | Jul 2019 | A1 |
| 20190201041 | Kimball et al. | Jul 2019 | A1 |
| 20190201042 | Nott et al. | Jul 2019 | A1 |
| 20190201043 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201044 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201045 | Yates et al. | Jul 2019 | A1 |
| 20190201046 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201047 | Yates et al. | Jul 2019 | A1 |
| 20190201073 | Nott et al. | Jul 2019 | A1 |
| 20190201074 | Yates et al. | Jul 2019 | A1 |
| 20190201075 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201077 | Yates et al. | Jul 2019 | A1 |
| 20190201079 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201080 | Messerly et al. | Jul 2019 | A1 |
| 20190201081 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201082 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201083 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201084 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201085 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201086 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201087 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201088 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201090 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201091 | Yates et al. | Jul 2019 | A1 |
| 20190201092 | Yates et al. | Jul 2019 | A1 |
| 20190201102 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201104 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201105 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201111 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201112 | Wiener et al. | Jul 2019 | A1 |
| 20190201113 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201114 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201115 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201116 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201117 | Yates et al. | Jul 2019 | A1 |
| 20190201118 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201119 | Harris et al. | Jul 2019 | A1 |
| 20190201120 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201123 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201124 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201125 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201126 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201127 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201128 | Yates et al. | Jul 2019 | A1 |
| 20190201129 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201130 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201135 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201136 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201137 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201138 | Yates et al. | Jul 2019 | A1 |
| 20190201139 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201140 | Yates et al. | Jul 2019 | A1 |
| 20190201141 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201142 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201143 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201145 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201146 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201158 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201159 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201594 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190201597 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190204201 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190205001 | Messerly et al. | Jul 2019 | A1 |
| 20190205441 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190205566 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190205567 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206003 | Harris et al. | Jul 2019 | A1 |
| 20190206004 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206050 | Yates et al. | Jul 2019 | A1 |
| 20190206216 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206542 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206551 | Yates et al. | Jul 2019 | A1 |
| 20190206555 | Morgan et al. | Jul 2019 | A1 |
| 20190206556 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206561 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206562 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206563 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206564 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206565 | Shelton, IV | Jul 2019 | A1 |
| 20190206569 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190206576 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190207773 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190207857 | Shelton, IV et al. | Jul 2019 | A1 |
| 20190207911 | Wiener et al. | Jul 2019 | A1 |
| 20190208641 | Yates et al. | Jul 2019 | A1 |
| 20190254759 | Azizian | Aug 2019 | A1 |
| 20190269476 | Bowling et al. | Sep 2019 | A1 |
| 20190274662 | Rockman et al. | Sep 2019 | A1 |
| 20190274705 | Sawhney et al. | Sep 2019 | A1 |
| 20190274706 | Nott et al. | Sep 2019 | A1 |
| 20190274707 | Sawhney et al. | Sep 2019 | A1 |
| 20190274708 | Boudreaux | Sep 2019 | A1 |
| 20190274709 | Scoggins | Sep 2019 | A1 |
| 20190274710 | Black | Sep 2019 | A1 |
| 20190274711 | Scoggins et al. | Sep 2019 | A1 |
| 20190274712 | Faller et al. | Sep 2019 | A1 |
| 20190274713 | Scoggins et al. | Sep 2019 | A1 |
| 20190274714 | Cuti et al. | Sep 2019 | A1 |
| 20190274716 | Nott et al. | Sep 2019 | A1 |
| 20190274717 | Nott et al. | Sep 2019 | A1 |
| 20190274718 | Denzinger et al. | Sep 2019 | A1 |
| 20190274719 | Stulen | Sep 2019 | A1 |
| 20190274720 | Gee et al. | Sep 2019 | A1 |
| 20190274749 | Brady et al. | Sep 2019 | A1 |
| 20190274750 | Jayme et al. | Sep 2019 | A1 |
| 20190274752 | Denzinger et al. | Sep 2019 | A1 |
| 20190290389 | Kopp | Sep 2019 | A1 |
| 20190298340 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298341 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298342 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298343 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298346 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298347 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298350 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298351 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298352 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298353 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298354 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298355 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298356 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298357 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190298464 | Abbott | Oct 2019 | A1 |
| 20190298481 | Rosenberg et al. | Oct 2019 | A1 |
| 20190307520 | Peine et al. | Oct 2019 | A1 |
| 20190314015 | Shelton, IV et al. | Oct 2019 | A1 |
| 20190321117 | Itkowitz et al. | Oct 2019 | A1 |
| 20190343594 | Kilroy et al. | Nov 2019 | A1 |
| 20190374140 | Tucker et al. | Dec 2019 | A1 |
| 20200054317 | Pisarnwongs et al. | Feb 2020 | A1 |
| 20200054320 | Harris et al. | Feb 2020 | A1 |
| 20200054321 | Harris et al. | Feb 2020 | A1 |
| 20200054322 | Harris et al. | Feb 2020 | A1 |
| 20200054323 | Harris et al. | Feb 2020 | A1 |
| 20200054325 | Harris et al. | Feb 2020 | A1 |
| 20200054326 | Harris et al. | Feb 2020 | A1 |
| 20200054327 | Harris et al. | Feb 2020 | A1 |
| 20200054328 | Harris et al. | Feb 2020 | A1 |
| 20200054329 | Shelton, IV et al. | Feb 2020 | A1 |
| 20200054330 | Harris et al. | Feb 2020 | A1 |
| 20200054331 | Harris et al. | Feb 2020 | A1 |
| 20200100830 | Henderson et al. | Apr 2020 | A1 |
| 20200178971 | Harris et al. | Jun 2020 | A1 |
| 20200261075 | Boudreaux et al. | Aug 2020 | A1 |
| 20200261076 | Boudreaux et al. | Aug 2020 | A1 |
| 20200261077 | Shelton, IV et al. | Aug 2020 | A1 |
| 20200261078 | Bakos et al. | Aug 2020 | A1 |
| 20200261080 | Bakos et al. | Aug 2020 | A1 |
| 20200261081 | Boudreaux et al. | Aug 2020 | A1 |
| 20200261082 | Boudreaux et al. | Aug 2020 | A1 |
| 20200261083 | Bakos et al. | Aug 2020 | A1 |
| 20200261084 | Bakos et al. | Aug 2020 | A1 |
| 20200261085 | Boudreaux et al. | Aug 2020 | A1 |
| 20200261086 | Zeiner et al. | Aug 2020 | A1 |
| 20200261087 | Timm et al. | Aug 2020 | A1 |
| 20200261088 | Harris et al. | Aug 2020 | A1 |
| 20200261089 | Shelton, IV et al. | Aug 2020 | A1 |
| Number | Date | Country |
|---|---|---|
| 2015201140 | Mar 2015 | AU |
| 2795323 | May 2014 | CA |
| 101617950 | Jan 2010 | CN |
| 104490448 | Mar 2017 | CN |
| 206097107 | Apr 2017 | CN |
| 3824913 | Feb 1990 | DE |
| 4002843 | Apr 1991 | DE |
| 102005051367 | Apr 2007 | DE |
| 102016207666 | Nov 2017 | DE |
| 0000756 | Oct 1981 | EP |
| 2732772 | May 2014 | EP |
| 3047806 | Jul 2016 | EP |
| 3056923 | Aug 2016 | EP |
| 3095399 | Nov 2016 | EP |
| 3120781 | Jan 2017 | EP |
| 3135225 | Mar 2017 | EP |
| 3141181 | Mar 2017 | EP |
| 2509523 | Jul 2014 | GB |
| S5373315 | Jun 1978 | JP |
| 2017513561 | Jun 2017 | JP |
| 20140104587 | Aug 2014 | KR |
| 101587721 | Jan 2016 | KR |
| WO-9734533 | Sep 1997 | WO |
| WO-0024322 | May 2000 | WO |
| WO-0108578 | Feb 2001 | WO |
| WO-0112089 | Feb 2001 | WO |
| WO-0120892 | Mar 2001 | WO |
| WO-2007137304 | Nov 2007 | WO |
| WO-2008056618 | May 2008 | WO |
| WO-2008069816 | Jun 2008 | WO |
| WO-2008147555 | Dec 2008 | WO |
| WO-2011112931 | Sep 2011 | WO |
| WO-2013143573 | Oct 2013 | WO |
| WO-2014134196 | Sep 2014 | WO |
| WO-2015129395 | Sep 2015 | WO |
| WO-2016206015 | Dec 2016 | WO |
| WO-2017011382 | Jan 2017 | WO |
| WO-2017151996 | Sep 2017 | WO |
| WO-2017189317 | Nov 2017 | WO |
| WO-2017205308 | Nov 2017 | WO |
| WO-2017210499 | Dec 2017 | WO |
| WO-2017210501 | Dec 2017 | WO |
| WO-2018152141 | Aug 2018 | WO |
| Entry |
|---|
| US 10,504,709 B2, 12/2019, Karancsi et al. (withdrawn) |
| Flores et al., “Large-scale Offloading in the Internet of Things,” 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE, pp. 479-484, Mar. 13, 2017. |
| Kalantarian et al., “Computation Offloading for Real-Time Health-Monitoring Devices,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EBMC), IEEE, pp. 4971-4974, Aug. 16, 2016. |
| Yuyi Mao et al., “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, pp. 2322-2358, Jun. 13, 2017. |
| Benkmann et al., “Concept of iterative optimization of minimally invasive surgery,” 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), IEEE pp. 443-446, Aug. 28, 2017. |
| Trautman, Peter, “Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction,” Robotics: Science and Systems XIIII, pp. 1-10, Jul. 12, 2017. |
| Khazaei et al., “Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1-9, Oct. 21, 2015. |
| Yang et al., “A dynamic stategy for packet scheduling and bandwidth allocation based on channel quality in IEEE 802.16e OFDMA system,” Journal of Network and Computer Applications, vol. 39, pp. 52-60, May 2, 2013. |
| Takahashi et al., “Automatic smoke evacuation in laparoscopic surgery: a simplified method for objective evaluation,” Surgical Endoscopy, vol. 27, No. 8, pp. 2980-2987, Feb. 23, 2013. |
| Miksch et al., “Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants,” Artificial Intelligence in Medicine, vol. 8, No. 6, pp. 543-576 (1996). |
| Horn et al., “Effective data validation of high-frequency data: Time-point-time-interval-, and trend-based methods,” Computers in Biology and Medic, New York, NY, vol. 27, No. 5, pp. 389-409 (1997). |
| Stacey et al., “Temporal abstraction in intelligent clinical data analysis: a survey, ” Artificial Intelligence in Medicine, vol. 39, No. 1, pp. 1-24 (2006). |
| Zoccali, Bruno, “A Method for Approximating Component Temperatures at Altitude Conditions Based on CFD Analysis at Sea Level Conditions,” (white paper), www.tdmginc.com, Dec. 6, 2018 (9 pages). |
| Slocinski et al., “Distance measure for impedance spectra for quantified evaluations,” Lecture Notes on Impedance Spectroscopy, vol. 3, Taylor and Francis Group (Jul. 2012)—Book Not Attached. |
| Engel et al. “A safe robot system for craniofacial surgery”, 2013 IEEE International Conference on Robotics and Automation (ICRA); May 6-10, 2013; Karlsruhe, Germany, vol. 2, Jan. 1, 2001, pp. 2020-2024. |
| Bonaci et al., “To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots,” May 13, 2015. Retrieved from the Internet: URL:https://arxiv.org/pdf/1504.04339v2.pdf [retrieved on Aug. 24, 2019]. |
| Homa Alemzadeh et al., “Targeted Attacks on Teleoperated Surgical Robots: Dynamic Model-Based Detection and Mitigation,” 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), IEEE, Jun. 28, 2016, pp. 395-406. |
| Phumzile Malindi, “5. QoS in Telemedicine,” “Telemedicine,” Jun. 20, 2011, IntechOpen, pp. 119-138. |
| Staub et al., “Contour-based Surgical Instrument Tracking Supported by Kinematic Prediction,” Proceedings of the 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, Sep. 1, 2010, pp. 746-752. |
| Allan et al., “3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery,” IEEE Transactions on Medical Imaging, vol. 37, No. 5, May 1, 2018, pp. 1204-1213. |
| Kassahun et al., “Surgical Robotics Beyond Enhanced Dexterity Instrumentation: A Survey of the Machine Learning Techniques and their Role in Intelligent and Autonomous Surgical Actions.” International Journal of Computer Assisted Radiology and Surgery, vol. 11, No. 4, Oct. 8, 2015, pp. 553-568. |
| Weede et al. “An Intelligent and Autonomous Endoscopic Guidance System for Minimally Invasive Surgery,” 2013 IEEE International Conference on Robotics ad Automation (ICRA), May 6-10, 2013. Karlsruhe, Germany, May 1, 2011, pp. 5762-5768. |
| Altenberg et al., “Genes of Glycolysis are Ubiquitously Overexpressed in 24 Cancer Classes,” Genomics, vol. 84, pp. 1014-1020 (2004). |
| Harold I. Brandon and V. Leroy Young, Mar. 1997, Surgical Services Management vol. 3 No. 3. retrieved from the internet <https://www.surgimedics.com/Research%20Articles/Electrosurgical%20Plume/Characterization%20And%20Removal%200f%20Electrosurgical%20Smoke.pdf> (Year: 1997). |
| Marshall Brain, How Microcontrollers Work, 2006, retrieved from the internet <https://web.archive.org/web/20060221235221/http://electronics.howstuffworks.com/microcontroller.htm/printable> (Year: 2006). |
| CRC Press, “The Measurement, Instrumentation and Sensors Handbook,” 1999, Section VII, Chapter 41, Peter O'Shea, “Phase Measurement,” pp. 1303-1321, ISBN 0-8493-2145-X. |
| Jiang, “‘Sound of Silence’ : a secure indoor wireless ultrasonic communication system,” Article, 2014, pp. 46-50, Snapshots of Doctoral Research at University College Cork, School of Engineering—Electrical & Electronic Engineering, UCC, Cork, Ireland. |
| Li, et al., “Short-range ultrasonic communications in air using quadrature modulation,” Journal, Oct. 30, 2009, pp. 2060-2072, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, No. 10, IEEE. |
| Salamon, “AI Detects Polyps Better Than Colonoscopists” Online Article, Jun. 3, 2018, Medscape Medical News, Digestive Disease Week (DDW) 2018: Presentation 133. |
| Misawa, et al. “Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience,” Article, Jun. 2018, pp. 2027-2029, vol. 154, Issue 8, American Gastroenterolgy Association. |
| Dottorato, “Analysis and Design of the Rectangular Microstrip Patch Antennas for TM0n0 operating mode,”Article, Oct. 8, 2010, pp. 1-9, Microwave Journal. |
| Miller, et al., “Impact of Powered and Tissue-Specific Endoscopic Stapling Technology on Clinical and Economic Outcomes of Video-Assisted Thoracic Surgery Lobectomy Procedures: A Retrospective, Observational Study,” Article, Apr. 2018, pp. 707-723, vol. 35 (Issue 5), Advances in Therapy. |
| Hsiao-Wei Tang, “ARCM”, Video, Sep. 2012, YouTube, 5 screenshots, Retrieved from internet: <https://www.youtube.com/watch?v=UldQaxb3fRw&feature=youtu.be>. |
| Giannios, et al., “Visible to near-infrared refractive properties of freshly-excised human-liver tissues: marking hepatic malignancies,” Article, Jun. 14, 2016, pp. 1-10, Scientific Reports 6, Article No. 27910, Nature. |
| Vander Heiden, et al., “Understanding the Warburg effect: the metabolic requirements of cell proliferation,” Article, May 22, 2009, pp. 1-12, vol. 324, Issue 5930, Science. |
| Hirayama et al., “Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry,” Article, Jun. 2009, pp. 4918-4925, vol. 69, Issue 11, Cancer Research. |
| Cengiz, et al., “A Tale of Two Compartments: Interstitial Versus Blood Glucose Monitoring,” Article, Jun. 2009, pp. S11-S16, vol. 11, Supplement 1, Diabetes Technology & Therapeutics. |
| Shen, et al., “An iridium nanoparticles dispersed carbon based thick film electrochemical biosensor and its application for a single use, disposable glucose biosensor,” Article, Feb. 3, 2007, pp. 106-113, vol. 125, Issue 1, Sensors and Actuators B: Chemical, Science Direct. |
| “ATM-MPLS Network Interworking Version 2.0, af-aic-0178.001” ATM Standard, The ATM Forum Technical Committee, published Aug. 2003. |
| IEEE Std 802.3-2012 (Revision of IEEE Std 802.3-2008, published Dec. 28, 2012. |
| IEEE Std No. 177, “Standard Definitions and Methods of Measurement for Piezoelectric Vibrators,” published May 1966, The Institute of Electrical and Electronics Engineers, Inc., New York, N.Y. |
| Shi et al., An intuitive control console for robotic syrgery system, 2014, IEEE, p. 404-407 (Year: 2014). |
| Choi et al., A haptic augmented reality surgeon console for a laparoscopic surgery robot system, 2013, IEEE, p. 355-357 (Year: 2013). |
| Xie et al., Development of stereo vision and master-slave controller for a compact surgical robot system, 2015, IEEE, p. 403-407 (Year: 2015). |
| Sun et al., Innovative effector design for simulation training in robotic surgery, 2010, IEEE, p. 1735-1759 (Year: 2010). |
| Number | Date | Country | |
|---|---|---|---|
| 20190201144 A1 | Jul 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62649333 | Mar 2018 | US | |
| 62611341 | Dec 2017 | US | |
| 62611340 | Dec 2017 | US | |
| 62611339 | Dec 2017 | US |
