Patent Application
20240000500
The present disclosure relates to electrosurgical instruments that are designed to seal and cut tissue.
The following summary is provided to facilitate an understanding of some of the innovative features unique to the aspects disclosed herein, and is not intended to be a full description. A full appreciation of the various aspects can be gained by taking the entire specification, claims, and abstract as a whole.
In one general aspect, the present disclosure is directed to a surgical device. The surgical device comprises an end effector comprising a first jaw and a second jaw. The surgical device further comprises a first sensor to detect tissue disposed between the first jaw and the second jaw, a second sensor to detect the end effector in a closed configuration, and a third sensor to detect motion of the end effector. The surgical device further comprises a control circuit communicably coupled to the first sensor, the second sensor, and the third sensor. The control circuit comprises a processor and a memory, wherein the memory stores instructions that, when executed by the processor, cause the control circuit to determine that the end effector is in a closed configuration based on first sensor data, determine presence of tissue disposed between the first jaw and the second jaw based on second sensor data, and monitor motion of the end effector in the closed configuration with tissue present between the first and second jaw based on third sensor data. The memory stores further instructions that, when executed by the processor, cause the control circuit to detect motion of the end effector outside of a predetermined range based on the motion, and provide feedback data based on the detected motion of the end effector.
In at least one aspect, the memory stores further instructions that, when executed by the processor, cause the control circuit to calculate tension on the tissue based on the motion.
In at least one aspect, the feedback is visual. In at least one aspect, the visual feedback is superimposed over a display image of a surgical site.
In at least one aspect, the feedback is auditory.
In at least one aspect, the first jaw comprises a clamp arm and the second jaw comprises an ultrasonic blade.
In at least one aspect, the first jaw comprises an anvil and the second jaw comprises a staple cartridge.
In another general aspect, the present disclosure is directed to a surgical device. The surgical device comprises an end effector comprising a first jaw and a second jaw. The surgical device further comprises a first sensor to detect tissue disposed between the first jaw and the second jaw, a second sensor to detect that the end effector is in a closed configuration, a first fiducial mark, and a second fiducial mark. The surgical device further comprises a control circuit communicably coupled to the first sensor, the second sensor, and a camera. The control circuit comprises a processor and a memory, wherein the memory stores instructions that, when executed by the processor, cause the control circuit to receive video data of a surgical site from the camera, determine that the end effector is in the closed configuration on first sensor data, and determine presence of tissue disposed between the first jaw and the second jaw based on second sensor data. The memory stores further instructions that, when executed by the processor, cause the control circuit to determine a location of a device tip in the video data based on the first fiducial mark and the second fiducial mark, determine a region of interest in the video data based on the location of the device tip in the video data, and analyze the region of interest of the end effector in the closed configuration with tissue present between the first jaw and the second jaw. The memory stores further instructions that, when executed by the processor, cause the control circuit to determine tension on the tissue based on the analysis, and provide feedback based on the tension.
In at least one aspect, the memory stores further instructions that, when executed by the processor, cause the control circuit to detect motion of the end effector outside of a predetermined range based on the analysis.
In at least one aspect, the memory stores further instructions that, when executed by the processor, cause the control circuit to determine device type based on the first fiducial mark and the second fiducial mark.
In at least one aspect, the feedback is visual. In at least one aspect, the visual feedback is superimposed over a display image of a surgical site.
In at least one aspect, the feedback is auditory.
In at least one aspect, the first jaw comprises a clamp arm and the second jaw comprises an ultrasonic blade.
In at least one aspect, the first jaw comprises an anvil and the second jaw comprises a staple cartridge.
In yet another general aspect, the present disclosure is directed to a surgical system that includes a surgical instrument, an RF energy source, and a control circuit. The surgical instrument comprises an end effector to capture tissue. The end effector comprises an electrode to apply radio-frequency (RF) energy to the tissue captured by the end effector. The RF energy source is to provide RF energy to the electrode. The control circuit is to transmit a control signal to the RF energy source. The control signal causes the RF energy source to provide RF energy to the electrode to apply a seal to the tissue captured by the end effector. The control circuit is further to predict a quality of the seal and provide feedback to a user based on the prediction.
In at least one aspect, the control circuit is to generate a value associated with the seal and compare the value to a seal threshold. To provide feedback to the user based on the prediction, the control circuit is to provide feedback to the user based on results of the compare.
In at least one aspect, the control circuit is to abstain from providing feedback based on the value reaching or exceeding the seal threshold.
In at least one aspect, the surgical system further comprises a display. The control circuit is to transmit a signal to the display based on the value being below the seal threshold. The feedback comprises visual feedback on the display. The visual feedback is based on the signal.
In at least one aspect, the surgical system further comprises an audio feedback module. The control circuit is to transmit a signal to the audio feedback module based on the value being below the seal threshold. The feedback comprises audio feedback via the audio feedback module. The audio feedback is based on the signal.
In at least one aspect, the surgical system further comprises a haptic feedback module. The control circuit is to transmit a signal to a haptic feedback module based on the value being below the seal threshold. The feedback comprises haptic feedback via the haptic feedback module. The haptic feedback is based on the signal.
In at least one aspect, the RF energy source comprises the control circuit.
In at least one aspect, the surgical system further comprises a processing unit comprising the control circuit.
Various features of the embodiments described herein, together with advantages thereof, may be understood in accordance with the following description taken in conjunction with the accompanying drawings as follows:
Corresponding reference characters indicate corresponding parts throughout the several views. The exemplifications set out herein illustrate various embodiments of the invention, in one form, and such exemplifications are not to be construed as limiting the scope of the invention in any manner.
Numerous specific details are set forth to provide a thorough understanding of the overall structure, function, manufacture, and use of the embodiments as described in the specification and illustrated in the accompanying drawings. Well-known operations, components, and elements have not been described in detail so as not to obscure the embodiments described in the specification. The reader will understand that the embodiments described and illustrated herein are non-limiting examples, and thus it can be appreciated that the specific structural and functional details disclosed herein may be representative and illustrative. Variations and changes thereto may be made without departing from the scope of the claims.
The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a surgical system, device, or apparatus that “comprises,” “has,” “includes” or “contains” one or more elements possesses those one or more elements, but is not limited to possessing only those one or more elements. Likewise, an element of a system, device, or apparatus that “comprises,” “has,” “includes” or “contains” one or more features possesses those one or more features, but is not limited to possessing only those one or more features.
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.
Various exemplary devices and methods are provided for performing laparoscopic and minimally invasive surgical procedures. However, the reader will readily appreciate that the various methods and devices disclosed herein can be used in numerous surgical procedures and applications including, for example, in connection with open surgical procedures. As the present Detailed Description proceeds, the reader will further appreciate that the various instruments disclosed herein can be inserted into a body in any way, such as through a natural orifice, through an incision or puncture hole formed in tissue, etc. The working portions or end effector portions of the instruments can be inserted directly into a patient's body or can be inserted through an access device that has a working channel through which the end effector and elongate shaft of a surgical instrument can be advanced.
Referring 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 one 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 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 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, 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. In one aspect, 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. In an alternative aspect, the first energy-generator module is stackably movable into an electrical engagement with the power and data contacts and wherein the first energy-generator module is stackably 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 to generate a second energy, either the same or 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. In one aspect, 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 an alternative aspect, the second energy-generator module is stackably movable into an electrical engagement with the power and data contacts, and wherein the second energy-generator module is stackably 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, 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, 129 and interactive communication therebetween.
Additional information regarding the hub can be found in U.S. Patent Application Publication No. 2019/0201136 and U.S. Patent Application Publication No. 2020/0078106, which are hereby incorporated by reference in their entireties herein.
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 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 closure member 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.
An RF energy source 794 is coupled to the end effector 792 and is applied to the RF electrode 796 when the RF electrode 796 is provided in the end effector 792 in place of the blade 768 or to work in conjunction with the blade 768. For example, the blade is made of electrically conductive metal and may be employed as the return path for electrosurgical RF current. The control circuit 760 controls the delivery of the RF energy to the RF electrode 796.
The control circuit 760 may be in communication a haptic feedback module 870. In some embodiments, the haptic feedback module 870 is positioned in a handpiece, such as within the hand-pieces 1205, 1207, 1209 of the surgical instruments 1204, 1206, 1208, described in more detail below. In some embodiments, the haptic feedback module 870 is positioned within an input interface that a user interacts with to control the various surgical instruments described elsewhere herein. The control circuit 760 can transmit a control signal to the haptic feedback module 870 to cause the haptic feedback module 870 to actuate and provide haptic feedback, as described in more detail elsewhere herein.
The control circuit 760 may be in communication an audio feedback module 871. The control circuit 760 can transmit a control signal to the audio feedback module 871 to cause the audio feedback module 871 to actuate and provide audio feedback to a user, as described in more detail elsewhere herein.
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.
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 Wi-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), Wi-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; a 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
The modular energy system 1000 is to drive multiple surgical instruments 1204, 1206, 1208. The first surgical instrument is an ultrasonic surgical instrument 1204 and comprises a hand-piece 1205 (HP), an ultrasonic transducer 1220, a shaft 1226, and an end effector 1222. The end effector 1222 comprises an ultrasonic blade 1228 acoustically coupled to the ultrasonic transducer 1220 and a clamp arm 1240. The hand-piece 1205 comprises a trigger 1243 to operate the clamp arm 1240 and a combination of the toggle buttons 1234a, 1234b, 1234c to energize and drive the ultrasonic blade 1228 or other function. The toggle buttons 1234a, 1234b, 1234c can be to energize the ultrasonic transducer 1220 with the modular energy system 1000.
The modular energy system 1000 also is to drive a second surgical instrument 1206. The second surgical instrument 1206 is an RF electrosurgical instrument and comprises a hand-piece 1207 (HP), a shaft 1227, and an end effector 1224. The end effector 1224 comprises electrodes in clamp arms 1242a, 1242b and return through an electrical conductor portion of the shaft 1227. The electrodes are coupled to and energized by a bipolar energy source within the modular energy system 1000. The hand-piece 1207 comprises a trigger 1245 to operate the clamp arms 1242a, 1242b and an energy button 1235 to actuate an energy switch to energize the electrodes in the end effector 1224.
The modular energy system 1000 also is to drive a multifunction surgical instrument 1208. The multifunction surgical instrument 1208 comprises a hand-piece 1209 (HP), a shaft 1229, and an end effector 1225. The end effector 1225 comprises an ultrasonic blade 1249 and a clamp arm 1246. The ultrasonic blade 1249 is acoustically coupled to the ultrasonic transducer 1220. The ultrasonic transducer 1220 may be separable from or integral to the hand-piece 1209. The hand-piece 1209 comprises a trigger 1247 to operate the clamp arm 1246 and a combination of the toggle buttons 1237a, 1237b, 1237c to energize and drive the ultrasonic blade 1249 or other function. The toggle buttons 1237a, 1237b, 1237c can be to energize the ultrasonic transducer 1220 with the modular energy system 1000 and energize the ultrasonic blade 1249 with a bipolar energy source also contained within the modular energy system 1000.
The modular energy system 1000 is configurable for use with a variety of surgical instruments. According to various forms, the modular energy system 1000 may be configurable for use with different surgical instruments of different types including, for example, the ultrasonic surgical instrument 1204, the RF electrosurgical instrument 1206, and the multifunction surgical instrument 1208 that integrates RF and ultrasonic energies delivered individually or simultaneously from the modular energy system 1000. Although in the form of
Additional information regarding the modular energy system can be found in U.S. patent application Ser. No. 17/217,394, entitled “METHOD FOR MECHANICAL PACKAGING FOR MODULAR ENERGY SYSTEM”, filed Mar. 30, 2021, which is hereby incorporated by reference in its entirety herein.
Having described a general implementation of a modular energy system 1000 and some surgical instruments that can couple to it, the disclosure now turns to the issue of tissue tension with surgical devices and the capability to calculate tissue tension. Tissue tension is an important parameter for the performance of both energy and endomechanical devices and can negatively affect sealing or stapling quality if used inappropriately. Stated another way, tissue tension affects all surgical devices that clamp on tissue such as staplers, clip appliers, ultrasonic devices, suturing devices, bipolar devices, mono polar devices, and graspers. Having poor control over the device can result in more tissue tension, which can damage the tissue that is being treated and negatively affect surgical outcomes. Currently, many surgical devices, such as handheld surgical devices, do not have the capability to determine tissue tension.
The present disclosure provides various solutions to calculate tissue tension and alert a user of the current tissue tension. This can allow a surgeon to maintain a low tissue tension helping them generate beneficial surgical outcomes. A control circuit of a surgical device can be coupled to sensors that provide it with information. The information can allow the control circuit to determine tissue tension and alert the user. The control circuit could calculate the tissue tension quantitatively or qualitatively. Some qualitative examples could be “Low”, “Medium”, “High”, “Lower”/“Falling”, or “Higher”/“Rising”. The user could be alerted to the tissue tension through visual or auditory feedback in real-time. In some instances, the user could also be provided feedback on the tissue tension post operatively.
The surgical device 1300 can include a tissue sensor that is to detect tissue disposed between the first jaw 1322 and the second jaw 1324. In one instance, the tissue sensor could be a capacitive sensor that could detect tissue between the jaws 1322, 1324. In an alternative instance, the tissue sensor could detect the continuity between the two jaws 1322, 1324 and determine if there is tissue between the jaws 1322, 1324. For example, the surgical device could use a return electrode to calculate the continuity. In the instance of the surgical device being a bipolar electrosurgical device the continuity could be directly calculated. The tissue sensor allows the control circuit to determine when there is tissue between the jaws 1322, 1324.
The surgical device 1300 can include a jaw closure sensor that is to detect the end effector in a closed configuration. In one instance, the jaw closure sensor could be a jaw closure switch, where the switch is activated when the jaws 1322, 1324 are closed. In an alternative instance, the jaw closure sensor could be jaw closure mechanism position sensors, where the position sensors can be used to determine where the first jaw 1322 is in the closure process. For example, the positon sensors could be used to determine when the jaws 1322, 1324 are closed.
The surgical device 1300 can include a motion sensor to detect motion of the end effector 1320 or motion of the device 1300. For example, the motion sensor could detect the end effector motion including orientation, position, velocity, and acceleration. In various instances, the motion sensor could include accelerometers, gyros, inertial measurement units, etc. The motion sensor could also be a combination of multiple sensors that provide the motion of the end effector. The motion sensor or motion sensors could be mounted in or on the surgical device 1300.
The control circuit can also determine when the surgical device 1300 is in a surgeon's hand. In some instances, the control circuit can use the motion sensors to determine when the surgical device 1300 is in use. In an alternative instance, the control circuit can use video analysis from a camera viewing the surgical site to determine when the surgical device 1300 enters the field of view. For example, the video analysis could detect the label 1312 on the device 1300 when the device 1300 is in the field of view. As another example, the video analysis could detect the geometry of the device 1300 to detect when the device 1300 is in the field of view.
Referring to
The control circuit can monitor device motion as an indicator of tissue tension in accordance with
This monitoring of the motion includes monitoring the motion of the device at the end of activation when the tissue is separated or released. The end of activation can be detected through a change in the tissue sensor. It could also be detected through user de-activation or a change in the frequency for an ultrasonic device. Throughout monitoring the motion of the device, the control circuit can determine if there was tension on the tissue 1714. For example, an abrupt movement of the device following division of tissue can be indicative of tissue tension. The control circuit then proceeds to provide feedback to the user of tissue tension and/or abrupt or rough movements in a heads up display. For example, the feedback could be superimposed over the camera view 1390. The feedback could also be auditory. In either instance, the feedback can be provided to the surgeon in real-time allowing them to correct their movements to benefit the surgical outcome.
The feedback can also be provided to the surgeon post operatively. For example, the motion of the device throughout the surgical procedure can be provided to the user. Referring to
Additionally, abrupt or rough motions can be captured, measured, and the event tagged for procedure analytics. The rating system 1600 can also be used to provide a surgeon with an overall rating of their performance postoperatively. In various instances, the motion feedback could be broken into categories such as motion, motion while touching tissue, motion while clamped on tissue, and motion immediately following division or release of tissue. These categories could allow a surgeon to view the motion of the device during different aspects of the surgical procedure and see where they need to improve. It could also show if the surgeon continually has abrupt movements during a specific aspect of the surgical procedure. Overall, the movement feedback can be used to coach and enhance surgeon skills in real-time or in a postoperative report.
Referring to
The control circuit can use video data from the camera to calculate tissue tension during the surgical procedure. There can be an issue of computational burden when performing real-time video analysis. However, this burden can be mitigated by analyzing a small location within the video image and only performing the analysis over a specific time window based on events of interest. For example, an event of interest could be when the tissue is clamped in the jaws 1322, 1324 of the end effector 1320.
Referring to
The control circuit can perform video analysis to calculate tissue tension in accordance with
The distal tip location in the video data can be calculated by using one or more fiducial marks, such as fiducial marks 1362, 1364, and 1366 (
Once the device tip location is known, the control circuit can generate a region of interest around the device tip location in the video data 1810. An example region of interest in video data is the region of interest 1370 shown in
Referring to
Tissue tension information can be translated to the device user via a visual (e.g. a low-medium-high scale) or an auditory (e.g. an alert tone is played if too much tension is applied) indicator. Relative changes over a range of video images can be used to see tension develop such as how the angles and lengths of edges change. This information can be provided to a surgeon in real-time for them to manage the tissue tension with the goal of minimizing tissue tension during the surgical procedure.
Advanced Bipolar (“ABP”) tools are electrosurgical tools used in surgery for soft tissue dissection and vessel sealing and offer the primary benefits of reducing OR time and minimizing blood loss. These tools use radiofrequency current to create the heat needed for sealing, but the resultant temperatures are generally not sufficient to cause the tissue to cut. These ABP tools further utilize algorithms to ensure sealing completeness and a mechanically sharp knife, an ultrasonic blade, or any other suitable blade to cut tissue after sealing. Combining sealing and cutting into a single device enhances multifunctionality of the ABP tools, which increases their versatility, reduces the need for instrument exchanges into and out of the body, and enhances surgical efficiency, ease of use, and workflow.
Although the dissection and sealing leads to a desirable outcome most of the time, some of the activations of the ABP tool can still lead to minor or major intra-operative bleeding. Surgeons could use information about the quality of the seal to inform subsequent surgical maneuvers that would help prevent or mitigate bleeding, thus reducing patient blood loss and improving surgical efficiency.
The present disclosure provides a solution that can predict, in real time, poor seal quality with high likelihood of bleeding before it occurs. These predictions can occur during or after energy activation utilizing electrical and other measurements throughout the activation, as well as additional device data. Upon detection of poor seal quality, the system can issue a warning (auditory, visual or haptic, or combinations thereof) that allows the surgeon to decide what appropriate next steps should be taken to avoid bleeding, such as, for example, not mechanically cutting the tissue with the blade. The surgeon may also choose to reapply energy before transecting or perform other actions to prevent bleeding.
The seal quality prediction is based on an inference model that predicts, in near real-time, the likelihood of bleeding based on the data from the generator. The inference module is based on a pattern recognition model that is trained on annotated real-world data from surgeries on humans. The module can be deployed on digital hardware in the operating room with direct access to the device data.
In some embodiments, the system utilizes time-series electrical data throughout the activation of the instrument (current, voltage, impedance, power, sealing cycle phase, total energy, etc.) as well as device manufacturing data (clamp force, multi-point jaw gap measurements, device electrical impedance, etc.) as a basis for making predictions about seal quality. Information about the seal quality is the conveyed to the surgeon, who can then choose appropriate response. The surgical workflow is described in the “Concept” section, below.
To develop the algorithm, data is needed, which is described in the “Data Collection and Annotation” section, below. This data is preprocessed in real time into features through a number of different mathematical transformations and scaling operations, described in the “Feature Generation” section, below. Preprocessing may result in hundreds or thousands of features that are fed into the machine learning models, which takes the inputs and generates a numeric prediction, such as a variable scale from 0 to 1, as an example. In one embodiment, applying a threshold to this output, such as >0.5, as an example, this output can be transformed into a binary prediction for communication to the user. The machine learning models are described in the “Machine Learning (“ML”) Models” section, below.
In some embodiments, the threshold can be chosen for a particular model to appropriately balance the sensitivity and specificity of the prediction. To optimize the efficacy of the solution in the context of surgical workflow, it is important to balance out false positives (i.e. false alarms leading to alarm fatigue) as well as false negatives (i.e. missed bleeding events leading to bleeding). This is described in the “Metrics” section, below. Lastly, the information must be communicated to the surgeon. The electrosurgical system may provide its own interface, or work in conjunction with another system. This is described in the “Integration and Interface” section, below.
As referenced above, in some embodiments, the system utilizes time-series electrical data throughout the activation of the instrument (current, voltage, impedance, power, sealing cycle phase, total energy, etc.) as well as device manufacturing data (clamp force, multi-point jaw gap measurements, device electrical impedance, etc.) as a basis for making predictions about seal quality. Information about the seal quality is the conveyed to the surgeon, who can then choose appropriate response.
Referring now to
In one aspect, when the control system predicts 2004 that the seal is a good quality seal, the control system can provide feedback to surgeon indicating the same. Based on the good quality seal indication, the surgeon can confidently proceed with cutting 2006 the sealed tissue with a knife of the electrosurgical tool, such as with blade 768, ultrasonic blade 1249, ultrasonic blade 1228, or any other knife or blade described elsewhere herein. In various other embodiments, when the control system predicts 2004 that the seal is of good seal quality, the control system can provide no feedback to the surgeon. The lack of feedback can inform the surgeon that the electrosurgical tool performed as intended, i.e., that the applied seal was of good quality. The lack of feedback provides the advantage of preventing the surgeon from becoming distracted by feedback that does require a corrective action to be taken. Once the tissue has been cut by the blade, the surgeon can proceed 2008 to the next surgical task.
In one aspect, when the control system predicts 2010 that the seal is a poor quality seal, the control system can provide feedback to surgeon indicating the same. Based on the poor seal quality indication, the surgeon can decide 2012 on an appropriate course of action to take. In one aspect, the surgeon can pause 2014 the actuation of the electrosurgical tool to assess the sealed tissue prior to proceeding. In one aspect, the surgeon can release 2016 the tissue captured by the jaws of the electrosurgical tool and regrasp the tissue, or grasp different tissue. In one aspect, the surgeon can apply 2018 additional therapeutic energy to the grasped tissue, further sealing the tissue prior to proceeding. In one aspect, the surgeon can prepare 2020 an ancillary tool, such as a different energy instrument, that can be used to create hemostasis within the tissue.
As described above, to develop the algorithm that can implement the above-described predictor of seal quality, data is needed. In order to predict the quality of the seal, the control system can utilize a machine learning algorithm that is trained, optimized, and tested utilizing data (both input and output data) obtained from a variety of sources, such as clinical sources, preclinical sources, or benchtop sources, or combinations thereof. In one aspect, the more closely the data represents real use, the more variation that can be introduced during training of the algorithm, and the more robustly the algorithm will perform during use in a surgical procedure.
In some embodiments, the data used to train the algorithm can be obtained from clinical, real-world sources. In one aspect, the data can be clinical, real-world, data (both input and output data) that is obtained from identical or similar hardware components that are used on humans. Utilizing clinical data allows the algorithm to be trained with data that will very closely match what the algorithm with encounter during surgical procedures. In one aspect, the input data can include time-series electrical data from the electrosurgery generator, internal system event data, and device manufacturing data, as examples.
In various embodiments, the output data includes outputs obtained from surgical videos that are recorded and then annotated in post-processing. Video annotation is the process of applying labels to the surgical videos such that the algorithm can extract structured information from the videos. In one aspect, the labels, such as those shown in the table 2050 of
For seal quality prediction, it is important to know whether the tissue bleeds or not after every activation, i.e. the “Hemostasis Outcome”. Accordingly, in some embodiments, either “Bleeding” or “Dry” labels are applied to each activation, which serves as the ground truth for training algorithms. In some embodiments, bleeding vs. dry labels are converted to binary values for training the algorithm. In other embodiments, qualitative estimates of bleeding, e.g. dry, oozing, minor, major, can be encoded as ordinal data for multi-class algorithm training. Other labels obtained through video annotation, such as tissue thickness, tissue type, tissue sticking, as examples, can be used during training to aid in multi-task learning. In one aspect, multi-task learning is a machine-learning algorithm that is trained to predict multiple outputs, which improves the accuracy of seal quality prediction by taking advantage of additional tissue data not contained in the bleeding/dry labels.
In some embodiments, the data used to train the algorithm can be obtained from benchtop testing. Augmenting real-world data with benchtop tissue data enhances algorithm accuracy by expanding conditions outside of clinical use in humans. For example, creating benchtop data with devices with low and high tolerance components (at the edge of, or out of tolerance) enhances robustness of the algorithm by allowing it to learn to compensate better for these factors, and recognize the patterns of out-of-tolerance devices in the electrical data. This enhances accuracy of predictions even on nominal component tolerance devices.
In some embodiments, the data used to train the algorithm can be obtained from preclinical, live animal testing. Similar to adding benchtop testing data, described above, live animal data can further augment training and testing data sets. Adding animal data has the benefit of achieving good algorithm performance during development, and then pass verification and validation activities.
As referenced above, the machine-learning model can be trained to predict multiple outputs using multi-task learning to improve the accuracy of the seal quality prediction. Referring now to
Referring now to
As referenced above, the algorithm model can receive features (inputs) that can be used to train the machine-learning algorithm.
In various embodiments, the features can include algorithm-based features. In one aspect, the ABP generator algorithm can be controlled using a composite load curve (CLC) look-up table 2300, illustrated in
For feature generation, the generator file is split up into CLC codes and then into smaller chunks inside of each CLC code. This can be seen in
In various embodiments, the features can include wavelet-based features. In one aspect, wavelets (continuous and discrete) are a signal processing technique which takes a ‘mother’ wave, scales it, and translates it across a given signal. Unlike sine-waves which are not localized in time, wavelets are localized in time. This allows wavelet transformations to obtain time-information in addition to frequency information. Since the wavelet is localized in time, a signal can be multiplied with the wavelet at different locations in time, this procedure being also known as a convolution, as can be seen in the representation 2310 shown in
As referenced above, the wavelets can include discrete wavelets and continuous wavelets. Discrete wavelet transformations (“DWT”) and continuous wavelet transformations (“CWT”) are taken of the raw signal and used as features. In some embodiments, for both DWT and CWT, the mother wavelet used is ‘db4’. In some embodiments, for the CWT, 128 scales of the mother wavelet are used, the resultant coefficients and frequencies of the transformation are stacked and zero-padded to create even length feature arrays. In some embodiments, for DWT, summary statistics and entropy calculations of the resultant coefficients are used instead of the pure coefficients. An example of a wavelet transform (power spectrum) of signal is shown on graph 2330 in
In various embodiments, the features can include time-based features. Referring to graph 2400 shown in
In various embodiments, the features can include raw signal features. The raw signal from the generator can be parsed into 5 vectors—voltage, current, impedance, power, and energy (V,I,Z,P,E) —which can then be stacked on top of each other and zero-padded, zeros are added to then end of each signal, to ensure that each vector for every activation is the same length. In one aspect, there can be 540 numbers per signal, where 540 is the theoretical maximum length of an activation (5.4 seconds).
As referenced above, preprocessing may result in hundreds or thousands of features that are fed into the machine learning models, which takes the inputs and generates a numeric prediction. In one aspect, with machine leaning (“ML”) models, the main idea of a sampling-based approach is to modify the distribution of events so that the rare class is well represented in the training sample. Given that bleeding cases represent only ˜10% of all cases, various class balancing techniques are applied to the machine learning model.
In the case of undersampling, a random sample is taken from the majority class, i.e., non-bleeding events. A potential problem with undersampling, however, is that some of useful non-bleeding instances may not be chosen for training and the classifier will not be optimal. In the case of oversampling, the replication of events are taken from the minority class, i.e., bleeding cases. A potential problem with oversampling, however, is that this technique results into overfitting for noisy data which will be replicated multiple times. This results into poor model generalization.
Accordingly, the present disclosure provides a hybrid approach of oversampling and undersampling, referred to herein as Synthetic Minority Oversampling Technique (“SMOTE”), which creates artificial minority class data using features space similarities. SMOTE is described in more detail in the paper titled “SMOTE: Synthetic Minority Over-sampling Technique”, by Notesh Chawla et al., which published in June 2002, which is hereby incorporated by reference in its entirety herein. The advantages of SMOTE are that it alleviates overfitting caused by random oversampling as synthetic examples are generated rather than replication of instances. Furthermore, there is no loss of information. In one aspect, referring to representation 2430 shown in
Referring now to
224 summary statistics were obtained using multiple approaches involving time based, algorithmic, wavelet, and raw features. Principal Component Analysis (“PCA”) was performed for extracting hidden (potentially lower dimensional) structure from the high dimensional feature space. PCA is an orthogonal projection or transformation of the data into a (possibly lower dimensional) subspace so that the variance of the projected data is maximized. Identifying the axes is known as Principal Components Analysis, and can be obtained by using classic matrix computation tools (Eigen or Singular Value Decomposition). Reference is made to
In some aspects, for ML Models, a prediction model can be used that include a mix of linear and tree based classifiers. The model framework had an input of a dataframe of size [n,224] where 224 represents summary statistics for electric signals obtained from feature engineering. In one aspect, preprocessing is accomplished with SMOTE or PCA, as examples, both of which are described above. Tissue classification included bleeding or dry.
For logistic regression, a binary classification model that uses logistic (sigmoid) function to model probabilities was used that includes a regularized model with alpha being equal to 0.5 and beta being equal to 0.5. Hyperparameter tuning was utilized to obtain optimal values for alpha and beta. A support vector machine (“SVM”) algorithm was used to find a hyperplane in an N-dimensional space (N—the number of features) that distinctly classifies the data points. The linear SVM was fit with L2 regularization. Radial Kernel functions were utilized to model non-linearities. Referring to
Other models used were CNN models, which are deep learning classification models that can be trained to classify patterns by extracting, through learnable weights and biases that describe multiple layers of convolution, informative aspects of the signal. In one aspect, a 1D CNN model can include an input of Five [1×540] vectors that describe the electrical signal. Preprocessing can be [1×7] median filter, standard scaling. Referring to
Other models used were CNN+Dense multihead architecture models, which are deep learning classification models that can be trained to classify patterns by extracting, through learnable weights and biases that describe multiple layers of convolution, informative aspects of the signal, and combining this information with other inputs. These other inputs are processed by a neural network with a number of densely connected layers. Outputs from multiple heads are combined and processed by a final stack of dense layers.
In one aspect, the model can include an input of two [1×540] vectors that describe the electrical signal and One [1×5] vector describing the mechanical parameters. Preprocessing can be [1×7] median filter, standard scaling for the electrical signal and standard scaling for the mechanical parameter vectors.
As described above, the threshold can be chosen for a particular model to appropriately balance the sensitivity and specificity of the prediction. To optimize the efficacy of the solution in the context of surgical workflow, it is important to balance out false positives (i.e. false alarms leading to alarm fatigue) as well as false negatives (i.e. missed bleeding events leading to bleeding).
Referring now to
Accuracy of the system is determined by the proportion of true results among the total number of cases examined. The equation for accuracy is provided as the number of TP and TN designations divided by the number of total actual number of cases. Given that the data is unbalanced in this case (approximately 10% bleeding events), accuracy fails to correctly capture precision and sensitivity values for bleeding events.
The proportion of predicted positives against those that are truly positive determines precision of the system. The equation for precision is provided as the number of TP designations divided by the sum of the TP and FP designations. In this case, the classifier showed high precision values, however, it failed to predict all bleeding.
The proportion of actual positives corrected classified determines the recall of the system. The equal for recall is provided as the number of TP designations divided by the sum of TP and FN designations. Optimizing classifier model based on this metric resulted in low precision scores.
An F1 score helps obtain recall precision scores within clinical viability threshold. The F1 score is a number between 0 and 1 and is the harmonic mean of precision and recall. The equation for F1 score is provided as 2*Recall*Precision/(Recall+Precision).
An Area under Curve (“AUC”) receiver operating characteristic (“ROC”) curve indicates how well the probabilities from the positive classes are separated from the negative classes. It is plotted between True Positive Rate and False Positive Rate values, as shown in example graph 2480 in
Target metrics were developed in conversations with various surgeons across North America and APAC. While discussing acceptable false positive (alarm fatigue) and false negative rates (missed bleeding events) with surgeons, a confusion matrix 2490 shown in
After all the discussions were complete, the responses were aggregated to determine a clinical viability threshold. It was determined that the tolerance for “alarm fatigue”, i.e. false positives, was roughly 2-4× higher than “missed bleeding events”, i.e. false negatives.
As referenced above, information must be communicated to the surgeon so they can decide on the appropriate course of action once the quality of the seal has been predicted. Communicating the prediction of seal quality can be done in many ways, and must strike the balance between being distracting or obtrusive to the surgeon, but should also minimize the likelihood of missing information.
In one aspect, it would be advantageous to display a non-obtrusive message on a monitor, such as monitor 14, a primary lap monitor, or any other monitor or display described elsewhere herein, when a poor seal is predicted, and to display nothing when a good seal is predicted. Considering good seals occur most of the time, not displaying seal quality information in these instances would help minimize information overload and distraction.
In various embodiments, visual methods (on-screen displays 2540, 2550, such as those shown in
A first embodiment of a seal quality prediction system for an advanced bipolar device 2600 is provided in
A second embodiment of a seal quality prediction system for an advanced bipolar device 2600 is provided in
In various embodiments, the system can also provide audible means for communicating seal quality information. In some embodiments, an audio feedback module, such as any suitable audio feedback module described elsewhere herein, can emit a first sound effect during the sealing phase of device activation, a secondary sound effect in case of a high quality seal, and a third sound effect for a poor quality seal. Various other embodiments are envisioned where no sound effect is emitted in the case of a high seal quality so as to not distract the surgeon. Rather, the audio feedback module only provides feedback in the event of a poor quality seal.
In various embodiments, the energy device, such as instrument 112, surgical instrument 790, or surgical instruments 1204, 1206, 1208 can be designed with a haptic device, such as a piezoelectric motor, which buzzes or clicks upon a poor seal quality prediction. Various embodiments are envisioned where the haptic device provides haptic feedback in the event of a high quality seal. Various other embodiments are envisioned where the haptic device does not provide haptic feedback in the event of a high quality seal so as to not distract the surgeon.
The entire disclosures of U.S. Pat. Nos. 10,624,691, 10,842,523, 11,291,510, 11,311,342, 11,259,830, 11,304,699, 11,109,866, 11,298,129, 11,229,437, 11,241,235 and U.S. Patent Application Publication Nos. 2019/0206562, 2019/0200981, 2019/0208641, 2019/0201594, 2019/0201045, 2019/0200844, 2019/0201136, 2019/0206569, 2019/0201137, 2019/0125459, 2019/0125458, 2019/0125455, 2019/0125454, 2019/0274706, 2019/0201046, 2019/0201047, 2019/0104919, 2019/0125361, 2019/0200977, 2019/0298350, 2019/0206564, 2019/0206565, 2020/0100830, 2020/0078070, 2020/0078076, 2020/0078106, 2020/0100825, 2021/0196334, 2021/0196354, 2021/0196302, 2020/0345353, 2022/0031315 are hereby incorporated by reference in their entireties herein.
Although various devices have been described herein in connection with certain embodiments, modifications and variations to those embodiments may be implemented. Particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics illustrated or described in connection with one embodiment may be combined in whole or in part, with the features, structures or characteristics of one ore more other embodiments without limitation. Also, where materials are disclosed for certain components, other materials may be used. Furthermore, according to various embodiments, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to perform a given function or functions. The foregoing description and following claims are intended to cover all such modification and variations.
The devices disclosed herein can be designed to be disposed of after a single use, or they can be designed to be used multiple times. In either case, however, a device can be reconditioned for reuse after at least one use. Reconditioning can include any combination of the steps including, but not limited to, the disassembly of the device, followed by cleaning or replacement of particular pieces of the device, and subsequent reassembly of the device. In particular, a reconditioning facility and/or surgical team can disassemble a device and, after cleaning and/or replacing particular parts of the device, the device can be reassembled for subsequent use. Those skilled in the art will appreciate that reconditioning of a device can utilize a variety of techniques for disassembly, cleaning/replacement, and reassembly. Use of such techniques, and the resulting reconditioned device, are all within the scope of the present application.
The devices disclosed herein may be processed before surgery. First, a new or used instrument may be obtained and, when necessary, cleaned. The instrument may then be sterilized. In one sterilization technique, the instrument is placed in a closed and sealed container, such as a plastic or TYVEK bag. The container and instrument may then be placed in a field of radiation that can penetrate the container, such as gamma radiation, x-rays, and/or high-energy electrons. The radiation may kill bacteria on the instrument and in the container. The sterilized instrument may then be stored in the sterile container. The sealed container may keep the instrument sterile until it is opened in a medical facility. A device may also be sterilized using any other technique known in the art, including but not limited to beta radiation, gamma radiation, ethylene oxide, plasma peroxide, and/or steam.
While this invention has been described as having exemplary designs, the present invention may be further modified within the spirit and scope of the disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the invention using its general principles.
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” or “control system” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor including 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 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.
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 “to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.
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.
The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a system that “comprises,” “has,” “includes” or “contains” one or more elements possesses those one or more elements, but is not limited to possessing only those one or more elements. Likewise, an element of a system, device, or apparatus that “comprises,” “has,” “includes” or “contains” one or more features possesses those one or more features, but is not limited to possessing only those one or more features.
The term “substantially”, “about”, or “approximately” as used in the present disclosure, unless otherwise specified, means an acceptable error for a particular value as determined by one of ordinary skill in the art, which depends in part on how the value is measured or determined. In certain embodiments, the term “substantially”, “about”, or “approximately” means within 1, 2, 3, or 4 standard deviations. In certain embodiments, the term “substantially”, “about”, or “approximately” means within 50%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, or 0.05% of a given value or range.
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 No. 63/357,177, titled ADVANCED BIPOLAR SEAL QUALITY PREDICTION, filed on Jun. 30, 2022, the disclosure of which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63357177 | Jun 2022 | US |
