Smart blade and power pulsing

Information

  • Patent Grant
  • 11337746
  • Patent Number
    11,337,746
  • Date Filed
    Thursday, September 27, 2018
    5 years ago
  • Date Issued
    Tuesday, May 24, 2022
    a year ago
Abstract
An ultrasonic device may include an electromechanical ultrasonic system defined by a predetermined resonant frequency, the electromechanical ultrasonic system including an ultrasonic transducer coupled to an ultrasonic blade. A method of controlling energy delivered to the ultrasonic device may include determining an impedance of the ultrasonic transducer during a transection process, analyzing the impedance of the ultrasonic transducer, profiling the ultrasonic blade based on the impedance, and adjusting a power delivered to the transducer during the transection process based on the profile of the blade. The method may further include pulsing, the power delivered to the ultrasonic transducer, determining changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses, and adjusting power delivered to the ultrasonic transducer based on the tissue changes throughout the transection. An ultrasonic instrument may include components configured to effect the method.
Description
BACKGROUND

In a surgical environment, smart energy devices may be needed in a smart energy architecture environment. Ultrasonic surgical devices, such as ultrasonic scalpels, are finding increasingly widespread applications in surgical procedures by virtue of their unique performance characteristics. Depending upon specific device configurations and operational parameters, ultrasonic surgical devices can provide substantially simultaneous transection of tissue and homeostasis by coagulation, desirably minimizing patient trauma. An ultrasonic surgical device may comprise a handpiece containing an ultrasonic transducer, and an instrument coupled to the ultrasonic transducer having a distally-mounted end effector (e.g., a blade tip) to cut and seal tissue. In some cases, the instrument may be permanently affixed to the handpiece. In other cases, the instrument may be detachable from the handpiece, as in the case of a disposable instrument or an interchangeable instrument. The end effector transmits ultrasonic energy to tissue brought into contact with the end effector to realize cutting and sealing action. Ultrasonic surgical devices of this nature can be configured for open surgical use, laparoscopic, or endoscopic surgical procedures including robotic-assisted procedures.


Ultrasonic energy cuts and coagulates tissue using temperatures lower than those used in electrosurgical procedures and can be transmitted to the end effector by an ultrasonic generator in communication with the handpiece. Vibrating at high frequencies (e.g., 55,500 cycles per second), the ultrasonic blade denatures protein in the tissue to form a sticky coagulum. Pressure exerted on tissue by the blade surface collapses blood vessels and allows the coagulum to form a hemostatic seal. A surgeon can control the cutting speed and coagulation by the force applied to the tissue by the end effector, the time over which the force is applied, and the selected excursion level of the end effector.


The ultrasonic transducer may be modeled as an equivalent circuit comprising a first branch having a static capacitance and a second “motional” branch having a serially connected inductance, resistance and capacitance that define the electromechanical properties of a resonator. Known ultrasonic generators may include a tuning inductor for tuning out the static capacitance at a resonant frequency so that substantially all of a generator's drive signal current flows into the motional branch. Accordingly, by using a tuning inductor, the generator's drive signal current represents the motional branch current, and the generator is thus able to control its drive signal to maintain the ultrasonic transducer's resonant frequency. The tuning inductor may also transform the phase impedance plot of the ultrasonic transducer to improve the generator's frequency lock capabilities. However, the tuning inductor must be matched with the specific static capacitance of an ultrasonic transducer at the operational resonant frequency. In other words, a different ultrasonic transducer having a different static capacitance requires a different tuning inductor.


Additionally, in some ultrasonic generator architectures, the generator's drive signal exhibits asymmetrical harmonic distortion that complicates impedance magnitude and phase measurements. For example, the accuracy of impedance phase measurements may be reduced due to harmonic distortion in the current and voltage signals.


Moreover, electromagnetic interference in noisy environments decreases the ability of the generator to maintain lock on the ultrasonic transducer's resonant frequency, increasing the likelihood of invalid control algorithm inputs.


Electrosurgical devices for applying electrical energy to tissue in order to treat and/or destroy the tissue are also finding increasingly widespread applications in surgical procedures. An electrosurgical device may comprise a handpiece and an instrument having a distally-mounted end effector (e.g., one or more electrodes). The end effector can be positioned against the tissue such that electrical current is introduced into the tissue. Electrosurgical devices can be configured for bipolar or monopolar operation. During bipolar operation, current is introduced into and returned from the tissue by active and return electrodes, respectively, of the end effector. During monopolar operation, current is introduced into the tissue by an active electrode of the end effector and returned through a return electrode (e.g., a grounding pad) separately located on a patient's body. Heat generated by the current flowing through the tissue may form hemostatic seals within the tissue and/or between tissues and thus may be particularly useful for sealing blood vessels, for example. The end effector of an electrosurgical device may also comprise a cutting member that is movable relative to the tissue and the electrodes to transect the tissue.


Electrical energy applied by an electrosurgical device can be transmitted to the instrument by a generator in communication with the handpiece. The electrical energy may be in the form of radio frequency (RF) energy. RF energy is a form of electrical energy that may be in the frequency range of 300 kHz to 1 MHz, as described in EN60601-2-2:2009+A11:2011, Definition 201.3.218—HIGH FREQUENCY. For example, the frequencies in monopolar RF applications are typically restricted to less than 5 MHz. However, in bipolar RF applications, the frequency can be almost any value. Frequencies above 200 kHz are typically used for monopolar applications in order to avoid the unwanted stimulation of nerves and muscles which would result from the use of low frequency current. Lower frequencies may be used for bipolar techniques if a risk analysis shows the possibility of neuromuscular stimulation has been mitigated to an acceptable level. Normally, frequencies above 5 MHz are not used in order to minimize the problems associated with high frequency leakage currents. It is generally recognized that 10 mA is the lower threshold of thermal effects on tissue.


During its operation, an electrosurgical device can transmit low frequency RF energy through tissue, which causes ionic agitation, or friction, in effect resistive heating, thereby increasing the temperature of the tissue. Because a sharp boundary may be created between the affected tissue and the surrounding tissue, surgeons can operate with a high level of precision and control, without sacrificing un-targeted adjacent tissue. The low operating temperatures of RF energy may be useful for removing, shrinking, or sculpting soft tissue while simultaneously sealing blood vessels. RF energy may work particularly well on connective tissue, which is primarily comprised of collagen and shrinks when contacted by heat.


Due to their unique drive signal, sensing and feedback needs, ultrasonic and electrosurgical devices have generally required different generators. Additionally, in cases where the instrument is disposable or interchangeable with a handpiece, ultrasonic and electrosurgical generators are limited in their ability to recognize the particular instrument configuration being used and to optimize control and diagnostic processes accordingly. Moreover, capacitive coupling between the non-isolated and patient-isolated circuits of the generator, especially in cases where higher voltages and frequencies are used, may result in exposure of a patient to unacceptable levels of leakage current.


Furthermore, due to their unique drive signal, sensing and feedback needs, ultrasonic and electrosurgical devices have generally required different user interfaces for the different generators. In such conventional ultrasonic and electrosurgical devices, one user interface is configured for use with an ultrasonic instrument whereas a different user interface may be configured for use with an electrosurgical instrument. Such user interfaces include hand and/or foot activated user interfaces such as hand activated switches and/or foot activated switches. As various aspects of combined generators for use with both ultrasonic and electrosurgical instruments are contemplated in the subsequent disclosure, additional user interfaces that are configured to operate with both ultrasonic and/or electrosurgical instrument generators also are contemplated.


Additional user interfaces for providing feedback, whether to the user or other machine, are contemplated within the subsequent disclosure to provide feedback indicating an operating mode or status of either an ultrasonic and/or electrosurgical instrument. Providing user and/or machine feedback for operating a combination ultrasonic and/or electrosurgical instrument will require providing sensory feedback to a user and electrical/mechanical/electro-mechanical feedback to a machine. Feedback devices that incorporate visual feedback devices (e.g., an LCD display screen, LED indicators), audio feedback devices (e.g., a speaker, a buzzer) or tactile feedback devices (e.g., haptic actuators) for use in combined ultrasonic and/or electrosurgical instruments are contemplated in the subsequent disclosure.


Other electrical surgical instruments include, without limitation, irreversible and/or reversible electroporation, and/or microwave technologies, among others. Accordingly, the techniques disclosed herein are applicable to ultrasonic, bipolar or monopolar RF (electrosurgical), irreversible and/or reversible electroporation, and/or microwave based surgical instruments, among others.


SUMMARY

An aspect of an ultrasonic device may include an electromechanical ultrasonic system defined by a predetermined resonant frequency, the electromechanical ultrasonic system further including an ultrasonic transducer coupled to an ultrasonic blade. An aspect of a method of controlling energy delivered to the ultrasonic device may include determining, by a processor or control circuit, an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process, analyzing, by the processor or control circuit, the impedance of the ultrasonic transducer, profiling, by the processor or control circuit, the ultrasonic blade based on the impedance of the ultrasonic transducer, and adjusting, by the processor or control circuit, a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


In one aspect, the method further includes pulsing, by the processor or control circuit, the power delivered to the ultrasonic transducer, determining, by the processor or control circuit, changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses, and adjusting, by the processor or control circuit, power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


In one aspect of the method, determining, by the processor or control circuit, changes in tissue characteristics may include measuring, by the processor or control circuit, a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
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=



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g



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t
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receiving, by the processor or control circuit, a complex impedance measurement data point, comparing, by the processor or control circuit, the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern, classifying, by the processor or control circuit, the complex impedance measurement data point based on a result of the comparison analysis, and assigning, by the processor or control circuit, a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the method, receiving, by the processor or control circuit, a complex impedance measurement data point may include receiving, by the processor or control circuit, a complex impedance measurement data point corresponding to a clamp arm pad and adjusting, by the processor or control circuit, power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


In one aspect of the method, determining, by the processor or control circuit, changes in tissue characteristics may include applying, by a drive circuit, a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency, sweeping, by a processor or control circuit, the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system, measuring and recording, by the processor or control circuit, impedance/admittance circle variables Re, Ge, Xe, and Be, comparing, by the processor or control circuit, measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref, and determining, by the processor or control circuit, a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the method, determining, by the processor or control circuit, changes in tissue characteristics may include determining, by the processor or control circuit, changes in a tissue thickness.


An aspect of an ultrasonic surgical instrument may include an ultrasonic electromechanical system comprising an ultrasonic transducer coupled to an ultrasonic blade via an ultrasonic waveguide and a generator configured to supply power to the ultrasonic transducer. In one aspect, the generator may include a control circuit configured to determine an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process, analyze the impedance of the ultrasonic transducer, profile the ultrasonic blade based on the impedance of the ultrasonic transducer, and adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


In one aspect of the ultrasonic surgical instrument, the generator may include a control circuit further configured to pulse the power delivered to the ultrasonic transducer, determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses, and adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


In one aspect of the ultrasonic surgical instrument, the generator may include a control circuit further configured to measure a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V

g








(
t
)




I
g



(
t
)




,





receive a complex impedance measurement data point, compare the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern, classify the complex impedance measurement data point based on a result of the comparison analysis, and assign a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the ultrasonic surgical instrument, the generator may include a control circuit further configured to receive a complex impedance measurement data point corresponding to a clamp arm pad, and adjust a power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


In one aspect of the ultrasonic surgical instrument, the generator may include a control circuit further configured to cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency, sweep the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system, measure and record impedance/admittance circle variables Re, Ge, Xe, and Be, compare measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref, and determine a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the ultrasonic surgical instrument, the generator may include a control circuit further configured to determine changes in a tissue thickness.


An aspect of a generator for an ultrasonic surgical instrument may include a control circuit configured to determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process, analyze the impedance of the ultrasonic transducer, profile the ultrasonic blade based on the impedance of the ultrasonic transducer, and adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


In one aspect of the ultrasonic surgical instrument, the control circuit is further configured to pulse the power delivered to the ultrasonic transducer, determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses, and adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


In one aspect of the ultrasonic surgical instrument, the control circuit is further configured to measure a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V

g








(
t
)




I
g



(
t
)




,





receive a complex impedance measurement data point, compare the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern, classify the complex impedance measurement data point based on a result of the comparison analysis, and assign a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the ultrasonic surgical instrument, the control circuit is further configured to receive a complex impedance measurement data point corresponding to a clamp arm pad and adjust a power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


In one aspect of the ultrasonic surgical instrument, the control circuit is further configured to cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency, sweep the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system, measure and record impedance/admittance circle variables Re, Ge, Xe, and Be, compare measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref, and determine a state or condition of the end effector based on the result of the comparison analysis.


In one aspect of the ultrasonic surgical instrument, the control circuit is further configured to determine changes in a tissue thickness.


An aspect of an ultrasonic surgical system may include a processor and a non-transitory memory. In an aspect, the non-transitory memory may include instructions that, when executed by the processor, cause the processor to determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process, analyze the impedance of the ultrasonic transducer, profile the ultrasonic blade based on the impedance of the ultrasonic transducer, and adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


In one aspect of the ultrasonic surgical system, the non-transitory memory may include instructions that, when executed by the processor, further cause the processor to pulse the power delivered to the ultrasonic transducer, determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses, and adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.





FIGURES

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.



FIG. 1 is a system configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub, in accordance with at least one aspect of the present disclosure.



FIG. 2 illustrates an example of a generator, in accordance with at least one aspect of the present disclosure.



FIG. 3 is a surgical system comprising a generator and various surgical instruments usable therewith, in accordance with at least one aspect of the present disclosure.



FIG. 4 is an end effector, in accordance with at least one aspect of the present disclosure.



FIG. 5 is a diagram of the surgical system of FIG. 3, in accordance with at least one aspect of the present disclosure.



FIG. 6 is a model illustrating motional branch current, in accordance with at least one aspect of the present disclosure.



FIG. 7 is a structural view of a generator architecture, in accordance with at least one aspect of the present disclosure.



FIGS. 8A-8C are functional views of a generator architecture, in accordance with at least one aspect of the present disclosure.



FIGS. 9A-9B are structural and functional aspects of a generator, in accordance with at least one aspect of the present disclosure.



FIG. 10 illustrates a control circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.



FIG. 11 illustrates a combinational logic circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.



FIG. 12 illustrates a sequential logic circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.



FIG. 13 illustrates one aspect of a fundamental architecture for a digital synthesis circuit such as a direct digital synthesis (DDS) circuit configured to generate a plurality of wave shapes for the electrical signal waveform for use in a surgical instrument, in accordance with at least one aspect of the present disclosure.



FIG. 14 illustrates one aspect of direct digital synthesis (DDS) circuit configured to generate a plurality of wave shapes for the electrical signal waveform for use in surgical instrument, in accordance with at least one aspect of the present disclosure.



FIG. 15 illustrates one cycle of a discrete time digital electrical signal waveform, in accordance with at least one aspect of the present disclosure of an analog waveform (shown superimposed over a discrete time digital electrical signal waveform for comparison purposes), in accordance with at least one aspect of the present disclosure.



FIG. 16 is a diagram of a control system in accordance with one aspect of this disclosure.



FIG. 17 illustrates a proportional-integral-derivative (PID) controller feedback control system in accordance with one aspect of this disclosure.



FIG. 18 is an alternative system for controlling the frequency of an ultrasonic electromechanical system and detecting the impedance thereof, in accordance with at least one aspect of the present disclosure.



FIG. 19 is a spectra of the same ultrasonic device with a variety of different states and conditions of the end effector where phase and magnitude of the impedance of an ultrasonic transducer are plotted as a function of frequency, in accordance with at least one aspect of the present disclosure.



FIG. 20 is a graphical representation of a plot of a set of 3D training data S, where ultrasonic transducer impedance magnitude and phase are plotted as a function of frequency, in accordance with at least one aspect of the present disclosure.



FIG. 21 is a logic flow diagram depicting a control program or a logic configuration to determine jaw conditions based on the complex impedance characteristic pattern (fingerprint), in accordance with at least one aspect of the present disclosure.



FIG. 22 is a circle plot of complex impedance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure.



FIG. 23 is a circle plot of complex admittance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure.



FIG. 24 is a circle plot of complex admittance for a 55.5 kHz ultrasonic piezoelectric transducer.



FIG. 25 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw open and no loading where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.



FIG. 26 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw clamped on dry chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.



FIG. 27 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw tip clamped on moist chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.



FIG. 28 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw fully clamped on moist chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.



FIG. 29 is a graphical display of an impedance analyzer showing impedance/admittance plots where frequency is swept from 48 kHz to 62 kHz to capture multiple resonances of an ultrasonic device with the jaw open where the rectangular overlay shown in broken line is to help see the circles, in accordance with at least one aspect of the present disclosure.



FIG. 30 is a logic flow diagram of a process depicting a control program or a logic configuration to determine jaw conditions based on estimates of the radius and offsets of an impedance/admittance circle, in accordance with at least one aspect of the present disclosure.



FIG. 31 is a logic flow diagram of a process depicting a control program or a logic configuration to monitor the impedance of an ultrasonic transducer to profile an ultrasonic blade and deliver power to the ultrasonic blade on the profile according to one aspect of the resent disclosure.



FIGS. 32A-32D is a series of graphical representations monitoring the impedance of an ultrasonic transducer to profile an ultrasonic blade and deliver power to the ultrasonic blade on the profile according to one aspect of the resent disclosure, where



FIG. 32A is a graphical representation of the initial impedance of the ultrasonic transducer as a function of time,



FIG. 32B is a graphical representation of power delivered to the ultrasonic blade as a function of time based on the initial impedance,



FIG. 32C is a graphical representation of a new impedance of the ultrasonic transducer as a function of time, and



FIG. 32D is a graphical representation of adjusted power delivered to the ultrasonic blade based on the new impedance.





DESCRIPTION

Applicant of the present patent application also owns the following contemporaneously-filed U.S. patent applications, each of which is herein incorporated by reference in its entirety:

    • U.S. patent application Ser. No. 16/144,335, titled METHODS FOR CONTROLLING TEMPERATURE IN ULTRASONIC DEVICE, now U.S. Pat. No. 11,259,830;
    • U.S. patent application Ser. No. 16/144,345, titled ULTRASONIC SEALING ALGORITHM WITH TEMPERATURE CONTROL, now U.S. Patent Application Publication No. 2019/0274718;
    • U.S. patent application Ser. No. 16/144,351, titled APPLICATION OF SMART ULTRASONIC BLADE TECHNOLOGY, now U.S. Patent Application Publication No. 2019/0274705;
    • U.S. patent application Ser. No. 16/144,455, titled SMART BLADE TECHNOLOGY TO CONTROL BLADE INSTABILITY, now U.S. Patent Application Publication No. 2019/0274712; and
    • U.S. patent application Ser. No. 16/144,483, titled START TEMPERATURE OF BLADE, now U.S. Patent Application Publication No. 2019/0274720.


Applicant of the present patent application also owns the following contemporaneously-filed U.S. patent applications, each of which is herein incorporated by reference in its entirety:

    • U.S. patent application Ser. No. 16/144,383, titled METHODS FOR ESTIMATING AND CONTROLLING STATE OF ULTRASONIC END EFFECTOR, now U.S. Patent Application Publication No. 2019/0274706;
    • U.S. patent application Ser. No. 16/144,391, titled IN-THE-JAW CLASSIFIER BASED ON MODEL, now U.S. Patent Application Publication No. 2019/0274719;
    • U.S. patent application Ser. No. 16/144,397, titled APPLICATION OF SMART BLADE TECHNOLOGY, now U.S. Patent Application Publication No. 2019/0274707;
    • U.S. patent application Ser. No. 16/144,418, titled ADJUSTMENT OF COMPLEX IMPEDANCE TO COMPENSATE FOR LOST POWER IN AN ARTICULATING ULTRASONIC DEVICE, now U.S. Patent Application Publication No. 2019/0274662;
    • U.S. patent application Ser. No. 16/144,427, titled USING SPECTROSCOPY TO DETERMINE DEVICE USE STATE IN COMBO INSTRUMENT, now U.S. Patent Application Publication No. 2019/0274710;
    • U.S. patent application Ser. No. 16/144,434, titled VESSEL SENSING FOR ADAPTIVE ADVANCED HEMOSTASIS, now U.S. Patent Application Publication No. 2019/0274711;
    • U.S. patent application Ser. No. 16/144,460, titled CALCIFIED VESSEL IDENTIFICATION, now U.S. Patent Application Publication No. 2019/0274713;
    • U.S. patent application Ser. No. 16/144,472, titled DETECTION OF LARGE VESSELS DURING PARENCHYMAL DISSECTION USING A SMART BLADE, now U.S. Patent Application Publication No. 2019/0274749;
    • U.S. patent application Ser. No. 16/144,478, titled SMART BLADE APPLICATION FOR REUSABLE AND DISPOSABLE DEVICES, now U.S. Patent Application Publication No. 2019/0274714;
    • U.S. patent application Ser. No. 16/144,486, titled LIVE TIME TISSUE CLASSIFICATION USING ELECTRICAL PARAMETERS, now U.S. Patent Application Publication No. 2019/0274750; and
    • U.S. patent application Ser. No. 16/144,508, titled FINE DISSECTION MODE FOR TISSUE CLASSIFICATION, now U.S. Patent Application Publication No. 2019/0274752.


Applicant of the present application owns the following U.S. patent applications, filed on Sep. 10, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application Ser. No. 62/729,177, titled AUTOMATED DATA SCALING, ALIGNMENT, AND ORGANIZING BASED ON PREDEFINED PARAMETERS WITHIN A SURGICAL NETWORK BEFORE TRANSMISSION;
    • U.S. provisional Patent Application Ser. No. 62/729,182, titled SENSING THE PATIENT POSITION AND CONTACT UTILIZING THE MONO-POLAR RETURN PAD ELECTRODE TO PROVIDE SITUATIONAL AWARENESS TO THE HUB;
    • U.S. Provisional Patent Application Ser. No. 62/729,184, titled POWERED SURGICAL TOOL WITH A PREDEFINED ADJUSTABLE CONTROL ALGORITHM FOR CONTROLLING AT LEAST ONE END-EFFECTOR PARAMETER AND A MEANS FOR LIMITING THE ADJUSTMENT;
    • U.S. Provisional Patent Application Ser. No. 62/729,183, titled SURGICAL NETWORK RECOMMENDATIONS FROM REAL TIME ANALYSIS OF PROCEDURE VARIABLES AGAINST A BASELINE HIGHLIGHTING DIFFERENCES FROM THE OPTIMAL SOLUTION;
    • U.S. Provisional Patent Application Ser. No. 62/729,191, titled A CONTROL FOR A SURGICAL NETWORK OR SURGICAL NETWORK CONNECTED DEVICE THAT ADJUSTS ITS FUNCTION BASED ON A SENSED SITUATION OR USAGE;
    • U.S. Provisional Patent Application Ser. No. 62/729,176, titled INDIRECT COMMAND AND CONTROL OF A FIRST OPERATING ROOM SYSTEM THROUGH THE USE OF A SECOND OPERATING ROOM SYSTEM WITHIN A STERILE FIELD WHERE THE SECOND OPERATING ROOM SYSTEM HAS PRIMARY AND SECONDARY OPERATING MODES;
    • U.S. Provisional Patent Application Ser. No. 62/729,186, titled WIRELESS PAIRING OF A SURGICAL DEVICE WITH ANOTHER DEVICE WITHIN A STERILE SURGICAL FIELD BASED ON THE USAGE AND SITUATIONAL AWARENESS OF DEVICES; and
    • U.S. Provisional Patent Application Ser. No. 62/729,185, titled POWERED STAPLING DEVICE THAT IS CAPABLE OF ADJUSTING FORCE, ADVANCEMENT SPEED, AND OVERALL STROKE OF CUTTING MEMBER OF THE DEVICE BASED ON SENSED PARAMETER OF FIRING OR CLAMPING.


Applicant of the present application owns the following U.S. Patent Applications, filed on Aug. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. patent application Ser. No. 16/115,214, titled ESTIMATING STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;
    • U.S. patent application Ser. No. 16/115,205, titled TEMPERATURE CONTROL OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;
    • U.S. patent application Ser. No. 16/115,233, titled RADIO FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL SIGNALS;
    • U.S. patent application Ser. No. 16/115,208, titled CONTROLLING AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO TISSUE LOCATION;
    • U.S. patent application Ser. No. 16/115,220, titled CONTROLLING ACTIVATION OF AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO THE PRESENCE OF TISSUE;
    • U.S. patent application Ser. No. 16/115,232, titled DETERMINING TISSUE COMPOSITION VIA AN ULTRASONIC SYSTEM;
    • U.S. patent application Ser. No. 16/115,239, titled DETERMINING THE STATE OF AN ULTRASONIC ELECTROMECHANICAL SYSTEM ACCORDING TO FREQUENCY SHIFT;
    • U.S. patent application Ser. No. 16/115,247, titled DETERMINING THE STATE OF AN ULTRASONIC END EFFECTOR;
    • U.S. patent application Ser. No. 16/115,211, titled SITUATIONAL AWARENESS OF ELECTROSURGICAL SYSTEMS;
    • U.S. patent application Ser. No. 16/115,226, titled MECHANISMS FOR CONTROLLING DIFFERENT ELECTROMECHANICAL SYSTEMS OF AN ELECTROSURGICAL INSTRUMENT;
    • U.S. patent application Ser. No. 16/115,240, titled DETECTION OF END EFFECTOR EMERSION IN LIQUID;
    • U.S. patent application Ser. No. 16/115,249, titled INTERRUPTION OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;
    • U.S. patent application Ser. No. 16/115,256, titled INCREASING RADIO FREQUENCY TO CREATE PAD-LESS MONOPOLAR LOOP;
    • U.S. patent application Ser. No. 16/115,223, titled BIPOLAR COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE BASED ON ENERGY MODALITY; and
    • U.S. patent application Ser. No. 16/115,238, titled ACTIVATION OF ENERGY DEVICES.


Applicant of the present application owns the following U.S. Patent Applications, filed on Aug. 23, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application No. 62/721,995, titled CONTROLLING AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO TISSUE LOCATION;
    • U.S. Provisional Patent Application No. 62/721,998, titled SITUATIONAL AWARENESS OF ELECTROSURGICAL SYSTEMS;
    • U.S. Provisional Patent Application No. 62/721,999, titled INTERRUPTION OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;
    • U.S. Provisional Patent Application No. 62/721,994, titled BIPOLAR COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE BASED ON ENERGY MODALITY; and
    • U.S. Provisional Patent Application No. 62/721,996, titled RADIO FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL SIGNALS.


Applicant of the present application owns the following U.S. Patent Applications, filed on Jun. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application No. 62/692,747, titled SMART ACTIVATION OF AN ENERGY DEVICE BY ANOTHER DEVICE;
    • U.S. Provisional Patent Application No. 62/692,748, titled SMART ENERGY ARCHITECTURE; and
    • U.S. Provisional Patent Application No. 62/692,768, titled SMART ENERGY DEVICES.


Applicant of the present application owns the following U.S. Patent Applications, filed on Jun. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. patent application Ser. No. 16/024,090, titled CAPACITIVE COUPLED RETURN PATH PAD WITH SEPARABLE ARRAY ELEMENTS;
    • U.S. patent application Ser. No. 16/024,057, titled CONTROLLING A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE PARAMETERS;
    • U.S. patent application Ser. No. 16/024,067, titled SYSTEMS FOR ADJUSTING END EFFECTOR PARAMETERS BASED ON PERIOPERATIVE INFORMATION;
    • U.S. patent application Ser. No. 16/024,075, titled SAFETY SYSTEMS FOR SMART POWERED SURGICAL STAPLING;
    • U.S. patent application Ser. No. 16/024,083, titled SAFETY SYSTEMS FOR SMART POWERED SURGICAL STAPLING;
    • U.S. patent application Ser. No. 16/024,094, titled SURGICAL SYSTEMS FOR DETECTING END EFFECTOR TISSUE DISTRIBUTION IRREGULARITIES;
    • U.S. patent application Ser. No. 16/024,138, titled SYSTEMS FOR DETECTING PROXIMITY OF SURGICAL END EFFECTOR TO CANCEROUS TISSUE;
    • U.S. patent application Ser. No. 16/024,150, titled SURGICAL INSTRUMENT CARTRIDGE SENSOR ASSEMBLIES;
    • U.S. patent application Ser. No. 16/024,160, titled VARIABLE OUTPUT CARTRIDGE SENSOR ASSEMBLY;
    • U.S. patent application Ser. No. 16/024,124, titled SURGICAL INSTRUMENT HAVING A FLEXIBLE ELECTRODE;
    • U.S. patent application Ser. No. 16/024,132, titled SURGICAL INSTRUMENT HAVING A FLEXIBLE CIRCUIT;
    • U.S. patent application Ser. No. 16/024,141, titled SURGICAL INSTRUMENT WITH A TISSUE MARKING ASSEMBLY;
    • U.S. patent application Ser. No. 16/024,162, titled SURGICAL SYSTEMS WITH PRIORITIZED DATA TRANSMISSION CAPABILITIES;
    • U.S. patent application Ser. No. 16/024,066, titled SURGICAL EVACUATION SENSING AND MOTOR CONTROL;
    • U.S. patent application Ser. No. 16/024,096, titled SURGICAL EVACUATION SENSOR ARRANGEMENTS;
    • U.S. patent application Ser. No. 16/024,116, titled SURGICAL EVACUATION FLOW PATHS;
    • U.S. patent application Ser. No. 16/024,149, titled SURGICAL EVACUATION SENSING AND GENERATOR CONTROL;
    • U.S. patent application Ser. No. 16/024,180, titled SURGICAL EVACUATION SENSING AND DISPLAY;
    • U.S. patent application Ser. No. 16/024,245, titled COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM;
    • U.S. patent application Ser. No. 16/024,258, titled SMOKE EVACUATION SYSTEM INCLUDING A SEGMENTED CONTROL CIRCUIT FOR INTERACTIVE SURGICAL PLATFORM;
    • U.S. patent application Ser. No. 16/024,265, titled SURGICAL EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR COMMUNICATION BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE; and
    • U.S. patent application Ser. No. 16/024,273, titled DUAL IN-SERIES LARGE AND SMALL DROPLET FILTERS.


Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Jun. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application Ser. No. 62/691,228, titled A METHOD OF USING REINFORCED FLEX CIRCUITS WITH MULTIPLE SENSORS WITH ELECTROSURGICAL DEVICES;
    • U.S. Provisional Patent Application Ser. No. 62/691,227, titled CONTROLLING A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE PARAMETERS;
    • U.S. Provisional Patent Application Ser. No. 62/691,230, titled SURGICAL INSTRUMENT HAVING A FLEXIBLE ELECTRODE;
    • U.S. Provisional Patent Application Ser. No. 62/691,219, titled SURGICAL EVACUATION SENSING AND MOTOR CONTROL;
    • U.S. Provisional Patent Application Ser. No. 62/691,257, titled COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM;
    • U.S. Provisional Patent Application Ser. No. 62/691,262, titled SURGICAL EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR COMMUNICATION BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE; and
    • U.S. Provisional Patent Application Ser. No. 62/691,251, titled DUAL IN-SERIES LARGE AND SMALL DROPLET FILTERS.


Applicant of the present application owns the following U.S. Provisional Patent Application, filed on Apr. 19, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application Ser. No. 62/659,900, titled METHOD OF HUB COMMUNICATION.


Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Mar. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application No. 62/650,898 filed on Mar. 30, 2018, titled CAPACITIVE COUPLED RETURN PATH PAD WITH SEPARABLE ARRAY ELEMENTS;
    • U.S. Provisional Patent Application Ser. No. 62/650,887, titled SURGICAL SYSTEMS WITH OPTIMIZED SENSING CAPABILITIES;
    • U.S. Provisional Patent Application Ser. No. 62/650,882, titled SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM; and
    • U.S. Provisional Patent Application Ser. No. 62/650,877, titled SURGICAL SMOKE EVACUATION SENSING AND CONTROLS


Applicant of the present application owns the following U.S. Patent Applications, filed on Mar. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. patent application Ser. No. 15/940,641, titled INTERACTIVE SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION CAPABILITIES;
    • U.S. patent application Ser. No. 15/940,648, titled INTERACTIVE SURGICAL SYSTEMS WITH CONDITION HANDLING OF DEVICES AND DATA CAPABILITIES;
    • U.S. patent application Ser. No. 15/940,656, titled SURGICAL HUB COORDINATION OF CONTROL AND COMMUNICATION OF OPERATING ROOM DEVICES;
    • U.S. patent application Ser. No. 15/940,666, titled SPATIAL AWARENESS OF SURGICAL HUBS IN OPERATING ROOMS;
    • U.S. patent application Ser. No. 15/940,670, titled COOPERATIVE UTILIZATION OF DATA DERIVED FROM SECONDARY SOURCES BY INTELLIGENT SURGICAL HUBS;
    • U.S. patent application Ser. No. 15/940,677, titled SURGICAL HUB CONTROL ARRANGEMENTS;
    • U.S. patent application Ser. No. 15/940,632, titled DATA STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE ANONYMIZED RECORD;
    • U.S. patent application Ser. No. 15/940,640, titled COMMUNICATION HUB AND STORAGE DEVICE FOR STORING PARAMETERS AND STATUS OF A SURGICAL DEVICE TO BE SHARED WITH CLOUD BASED ANALYTICS SYSTEMS;
    • U.S. patent application Ser. No. 15/940,645, titled SELF DESCRIBING DATA PACKETS GENERATED AT AN ISSUING INSTRUMENT;
    • U.S. patent application Ser. No. 15/940,649, titled DATA PAIRING TO INTERCONNECT A DEVICE MEASURED PARAMETER WITH AN OUTCOME;
    • U.S. patent application Ser. No. 15/940,654, titled SURGICAL HUB SITUATIONAL AWARENESS;
    • U.S. patent application Ser. No. 15/940,663, titled SURGICAL SYSTEM DISTRIBUTED PROCESSING;
    • U.S. patent application Ser. No. 15/940,668, titled AGGREGATION AND REPORTING OF SURGICAL HUB DATA;
    • U.S. patent application Ser. No. 15/940,671, titled SURGICAL HUB SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING THEATER;
    • U.S. patent application Ser. No. 15/940,686, titled DISPLAY OF ALIGNMENT OF STAPLE CARTRIDGE TO PRIOR LINEAR STAPLE LINE;
    • U.S. patent application Ser. No. 15/940,700, titled STERILE FIELD INTERACTIVE CONTROL DISPLAYS;
    • U.S. patent application Ser. No. 15/940,629, titled COMPUTER IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;
    • U.S. patent application Ser. No. 15/940,704, titled USE OF LASER LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE PROPERTIES OF BACK SCATTERED LIGHT;
    • U.S. patent application Ser. No. 15/940,722, titled CHARACTERIZATION OF TISSUE IRREGULARITIES THROUGH THE USE OF MONO-CHROMATIC LIGHT REFRACTIVITY; and
    • U.S. patent application Ser. No. 15/940,742, titled DUAL CMOS ARRAY IMAGING.
    • U.S. patent application Ser. No. 15/940,636, titled ADAPTIVE CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;
    • U.S. patent application Ser. No. 15/940,653, titled ADAPTIVE CONTROL PROGRAM UPDATES FOR SURGICAL HUBS;
    • U.S. patent application Ser. No. 15/940,660, titled CLOUD-BASED MEDICAL ANALYTICS FOR CUSTOMIZATION AND RECOMMENDATIONS TO A USER;
    • U.S. patent application Ser. No. 15/940,679, titled CLOUD-BASED MEDICAL ANALYTICS FOR LINKING OF LOCAL USAGE TRENDS WITH THE RESOURCE ACQUISITION BEHAVIORS OF LARGER DATA SET;
    • U.S. patent application Ser. No. 15/940,694, titled CLOUD-BASED MEDICAL ANALYTICS FOR MEDICAL FACILITY SEGMENTED INDIVIDUALIZATION OF INSTRUMENT FUNCTION;
    • U.S. patent application Ser. No. 15/940,634, titled CLOUD-BASED MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION TRENDS AND REACTIVE MEASURES;
    • U.S. patent application Ser. No. 15/940,706, titled DATA HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK; and
    • U.S. patent application Ser. No. 15/940,675, titled CLOUD INTERFACE FOR COUPLED SURGICAL DEVICES.
    • U.S. patent application Ser. No. 15/940,627, titled DRIVE ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,637, titled COMMUNICATION ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,642, titled CONTROLS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,676, titled AUTOMATIC TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,680, titled CONTROLLERS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,683, titled COOPERATIVE SURGICAL ACTIONS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. patent application Ser. No. 15/940,690, titled DISPLAY ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS; and
    • U.S. patent application Ser. No. 15/940,711, titled SENSING ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.


Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Mar. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application Ser. No. 62/649,302, titled INTERACTIVE SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION CAPABILITIES;
    • U.S. Provisional Patent Application Ser. No. 62/649,294, titled DATA STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE ANONYMIZED RECORD;
    • U.S. Provisional Patent Application Ser. No. 62/649,300, titled SURGICAL HUB SITUATIONAL AWARENESS;
    • U.S. Provisional Patent Application Ser. No. 62/649,309, titled SURGICAL HUB SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING THEATER;
    • U.S. Provisional Patent Application Ser. No. 62/649,310, titled COMPUTER IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;
    • U.S. Provisional Patent Application Ser. No. 62/649,291, titled USE OF LASER LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE PROPERTIES OF BACK SCATTERED LIGHT;
    • U.S. Provisional Patent Application Ser. No. 62/649,296, titled ADAPTIVE CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;
    • U.S. Provisional Patent Application Ser. No. 62/649,333, titled CLOUD-BASED MEDICAL ANALYTICS FOR CUSTOMIZATION AND RECOMMENDATIONS TO A USER;
    • U.S. Provisional Patent Application Ser. No. 62/649,327, titled CLOUD-BASED MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION TRENDS AND REACTIVE MEASURES;
    • U.S. Provisional Patent Application Ser. No. 62/649,315, titled DATA HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK;
    • U.S. Provisional Patent Application Ser. No. 62/649,313, titled CLOUD INTERFACE FOR COUPLED SURGICAL DEVICES;
    • U.S. Provisional Patent Application Ser. No. 62/649,320, titled DRIVE ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;
    • U.S. Provisional Patent Application Ser. No. 62/649,307, titled AUTOMATIC TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS; and
    • U.S. Provisional Patent Application Ser. No. 62/649,323, titled SENSING ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.


Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Dec. 28, 2017, the disclosure of each of which is herein incorporated by reference in its entirety:

    • U.S. Provisional Patent Application Serial No. U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM;
    • U.S. Provisional Patent Application Ser. No. 62/611,340, titled CLOUD-BASED MEDICAL ANALYTICS; and
    • U.S. Provisional Patent Application Ser. No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM.


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.


Various aspects are directed to improved ultrasonic surgical devices, electrosurgical devices and generators for use therewith. Aspects of the ultrasonic surgical devices can be configured for transecting and/or coagulating tissue during surgical procedures, for example. Aspects of the electrosurgical devices can be configured for transecting, coagulating, scaling, welding and/or desiccating tissue during surgical procedures, for example.


Adaptive Ultrasonic Blade Control Algorithms

In various aspects smart ultrasonic energy devices may comprise adaptive algorithms to control the operation of the ultrasonic blade. In one aspect, the ultrasonic blade adaptive control algorithms are configured to identify tissue type and adjust device parameters. In one aspect, the ultrasonic blade control algorithms are configured to parameterize tissue type. An algorithm to detect the collagen/elastic ratio of tissue to tune the amplitude of the distal tip of the ultrasonic blade is described in the following section of the present disclosure. Various aspects of smart ultrasonic energy devices are described herein in connection with FIGS. 1-2, for example. Accordingly, the following description of adaptive ultrasonic blade control algorithms should be read in conjunction with FIGS. 1-2 and the description associated therewith.


In certain surgical procedures it would be desirable to employ adaptive ultrasonic blade control algorithms. In one aspect, adaptive ultrasonic blade control algorithms may be employed to adjust the parameters of the ultrasonic device based on the type of tissue in contact with the ultrasonic blade. In one aspect, the parameters of the ultrasonic device may be adjusted based on the location of the tissue within the jaws of the ultrasonic end effector, for example, the location of the tissue between the clamp arm and the ultrasonic blade. The impedance of the ultrasonic transducer may be employed to differentiate what percentage of the tissue is located in the distal or proximal end of the end effector. The reactions of the ultrasonic device may be based on the tissue type or compressibility of the tissue. In another aspect, the parameters of the ultrasonic device may be adjusted based on the identified tissue type or parameterization. For example, the mechanical displacement amplitude of the distal tip of the ultrasonic blade may be tuned based on the ration of collagen to elastin tissue detected during the tissue identification procedure. The ratio of collagen to elastin tissue may be detected used a variety of techniques including infrared (IR) surface reflectance and emissivity. The force applied to the tissue by the clamp arm and/or the stroke of the clamp arm to produce gap and compression. Electrical continuity across a jaw equipped with electrodes may be employed to determine what percentage of the jaw is covered with tissue.



FIG. 1 is a system 800 configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub, in accordance with at least one aspect of the present disclosure. In one aspect, the generator module 240 is configured to execute the adaptive ultrasonic blade control algorithm(s) 802 as described herein. In another aspect, the device/instrument 235 is configured to execute the adaptive ultrasonic blade control algorithm(s) 804 as described herein with reference to FIGS. 19-32. In another aspect, both the device/instrument 235 and the device/instrument 235 are configured to execute the adaptive ultrasonic blade control algorithms 802, 804 as described herein with reference to FIGS. 19-32.


The generator module 240 may comprise a patient isolated stage in communication with a non-isolated stage via a power transformer. A secondary winding of the power transformer is contained in the isolated stage and may comprise a tapped configuration (e.g., a center-tapped or a non-center-tapped configuration) to define drive signal outputs for delivering drive signals to different surgical instruments, such as, for example, an ultrasonic surgical instrument, an RF electrosurgical instrument, and a multifunction surgical instrument which includes ultrasonic and RF energy modes that can be delivered alone or simultaneously. In particular, the drive signal outputs may output an ultrasonic drive signal (e.g., a 420V root-mean-square (RMS) drive signal) to an ultrasonic surgical instrument 241, and the drive signal outputs may output an RF electrosurgical drive signal (e.g., a 100V RMS drive signal) to an RF electrosurgical instrument 241. Aspects of the generator module 240 are described herein with reference to FIGS. 7-12.


The generator module 240 or the device/instrument 235 or both are coupled to the modular control tower 236 connected to multiple operating theater devices such as, for example, intelligent surgical instruments, robots, and other computerized devices located in the operating theater. In some aspects, a surgical data network may include a modular communication hub configured to connect modular devices located in one or more operating theaters of a healthcare facility, or any room in a healthcare facility specially equipped for surgical operations, to a cloud-based system (e.g., the cloud 204 that may include a remote server 213 coupled to a storage device).


Modular devices located in the operating theater may be coupled to the modular communication hub. The network hub and/or the network switch may be coupled to a network router to connect the devices to the cloud 204 or a local computer system. Data associated with the devices may be transferred to cloud-based computers via the router for remote data processing and manipulation. Data associated with the devices may also be transferred to a local computer system for local data processing and manipulation. Modular devices located in the same operating theater also may be coupled to a network switch. The network switch may be coupled to the network hub and/or the network router to connect to the devices to the cloud 204. Data associated with the devices may be transferred to the cloud 204 via the network router for data processing and manipulation. Data associated with the devices may also be transferred to the local computer system for local 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 and/or computer system 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 and/or computer system 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 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.



FIG. 1 further illustrates some aspects of a computer-implemented interactive surgical system comprising a modular communication hub that may include the system 800 configured to execute adaptive ultrasonic blade control algorithms in a surgical data network. The surgical system may include at least one surgical hub in communication with a cloud 204 that may include a remote server 213. In one aspect, the computer-implemented interactive surgical system comprises a modular control tower 236 connected to multiple operating theater devices such as, for example, intelligent surgical instruments, robots, and other computerized devices located in the operating theater. The modular control tower 236 may comprise a modular communication hub coupled to a computer system. In some aspects, the modular control tower 236 is coupled to an imaging module that is coupled to an endoscope, a generator module 240 that is coupled to an energy device 241, and a smart device/instrument 235 optionally coupled to a display 237. The operating theater devices are coupled to cloud computing resources and data storage via the modular control tower 236. A robot hub 222 also may be connected to the modular control tower 236 and to the cloud computing resources. The devices/instruments 235, visualization systems 208, among others, may be coupled to the modular control tower 236 via wired or wireless communication standards or protocols, as described herein. The modular control tower 236 may be coupled to a hub display 215 (e.g., monitor, screen) to display and overlay images received from the imaging module, device/instrument display, and/or other visualization systems 208. The hub display 215 also may display data received from devices connected to the modular control tower in conjunction with images and overlaid images.


Generator Hardware


FIG. 2 illustrates an example of a generator 900, which is one form of a generator configured to couple to an ultrasonic instrument and further configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub as shown in FIG. 1. The generator 900 is configured to deliver multiple energy modalities to a surgical instrument. The generator 900 provides RF and ultrasonic signals for delivering energy to a surgical instrument either independently or simultaneously. The RF and ultrasonic signals may be provided alone or in combination and may be provided simultaneously. As noted above, at least one generator output can deliver multiple energy modalities (e.g., ultrasonic, bipolar or monopolar RF, irreversible and/or reversible electroporation, and/or microwave energy, among others) through a single port, and these signals can be delivered separately or simultaneously to the end effector to treat tissue. 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 906 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 FIG. 2 shows a single return path RETURN may be provided for two or more energy modalities, in other aspects, multiple return paths RETURNn may be provided for each energy modality ENERGYn. Thus, as described herein, the ultrasonic transducer impedance may be measured by dividing the output of the first voltage sensing circuit 912 by the current sensing circuit 914 and the tissue impedance may be measured by dividing the output of the second voltage sensing circuit 924 by the current sensing circuit 914.


As shown in FIG. 2, the generator 900 comprising at least one output port can include a power transformer 908 with a single output and with multiple taps to provide power in the form of one or more energy modalities, such as ultrasonic, bipolar or monopolar RF, irreversible and/or reversible electroporation, and/or microwave energy, among others, for example, to the end effector depending on the type of treatment of tissue being performed. For example, the generator 900 can deliver energy with higher voltage and lower current to drive an ultrasonic transducer, with lower voltage and higher current to drive RF electrodes for sealing tissue, or with a coagulation waveform for spot coagulation using either monopolar or bipolar RF electrosurgical electrodes. The output waveform from the generator 900 can be steered, switched, or filtered to provide the frequency to the end effector of the surgical instrument. The connection of an ultrasonic transducer to the generator 900 output would be preferably located between the output labeled ENERGY1 and RETURN as shown in FIG. 2. In one example, a connection of RF bipolar electrodes to the generator 900 output would be preferably located between the output labeled ENERGY2 and RETURN. In the case of monopolar output, the preferred connections would be active electrode (e.g., pencil or other probe) to the ENERGY2 output and a suitable return pad connected to the RETURN output.


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 FIG. 3, for example) that are receivable within a surgical hub and the surgical devices or instruments that can be connected to the various modules in order to connect or pair with the corresponding surgical hub. The modular devices include, for example, intelligent surgical instruments, medical imaging devices, suction/irrigation devices, smoke evacuators, energy generators, ventilators, insufflators, and displays. The modular devices described herein can be controlled by control algorithms. The control algorithms can be executed on the modular device itself, on the surgical hub to which the particular modular device is paired, or on both the modular device and the surgical hub (e.g., via a distributed computing architecture). In some exemplifications, the modular devices' control algorithms control the devices based on data sensed by the modular device itself (i.e., by sensors in, on, or connected to the modular device). This data can be related to the patient being operated on (e.g., tissue properties or insufflation pressure) or the modular device itself (e.g., the rate at which a knife is being advanced, motor current, or energy levels). For example, a control algorithm for a surgical stapling and cutting instrument can control the rate at which the instrument's motor drives its knife through tissue according to resistance encountered by the knife as it advances.



FIG. 3 illustrates one form of a surgical system 1000 comprising a generator 1100 and various surgical instruments 1104, 1106, 1108 usable therewith, where the surgical instrument 1104 is an ultrasonic surgical instrument, the surgical instrument 1106 is an RF electrosurgical instrument, and the multifunction surgical instrument 1108 is a combination ultrasonic/RF electrosurgical instrument. The generator 1100 is configurable for use with a variety of surgical instruments. According to various forms, the generator 1100 may be configurable for use with different surgical instruments of different types including, for example, ultrasonic surgical instruments 1104, RF electrosurgical instruments 1106, and multifunction surgical instruments 1108 that integrate RF and ultrasonic energies delivered simultaneously from the generator 1100. Although in the form of FIG. 3 the generator 1100 is shown separate from the surgical instruments 1104, 1106, 1108 in one form, the generator 1100 may be formed integrally with any of the surgical instruments 1104, 1106, 1108 to form a unitary surgical system. The generator 1100 comprises an input device 1110 located on a front panel of the generator 1100 console. The input device 1110 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. The generator 1100 may be configured for wired or wireless communication.


The generator 1100 is configured to drive multiple surgical instruments 1104, 1106, 1108. The first surgical instrument is an ultrasonic surgical instrument 1104 and comprises a handpiece 1105 (HP), an ultrasonic transducer 1120, a shaft 1126, and an end effector 1122. The end effector 1122 comprises an ultrasonic blade 1128 acoustically coupled to the ultrasonic transducer 1120 and a clamp arm 1140. The handpiece 1105 comprises a trigger 1143 to operate the clamp arm 1140 and a combination of the toggle buttons 1134a, 1134b, 1134c to energize and drive the ultrasonic blade 1128 or other function. The toggle buttons 1134a, 1134b, 1134c can be configured to energize the ultrasonic transducer 1120 with the generator 1100.


The generator 1100 also is configured to drive a second surgical instrument 1106. The second surgical instrument 1106 is an RF electrosurgical instrument and comprises a handpiece 1107 (HP), a shaft 1127, and an end effector 1124. The end effector 1124 comprises electrodes in clamp arms 1142a, 1142b and return through an electrical conductor portion of the shaft 1127. The electrodes are coupled to and energized by a bipolar energy source within the generator 1100. The handpiece 1107 comprises a trigger 1145 to operate the clamp arms 1142a, 1142b and an energy button 1135 to actuate an energy switch to energize the electrodes in the end effector 1124.


The generator 1100 also is configured to drive a multifunction surgical instrument 1108. The multifunction surgical instrument 1108 comprises a handpiece 1109 (HP), a shaft 1129, and an end effector 1125. The end effector 1125 comprises an ultrasonic blade 1149 and a clamp arm 1146. The ultrasonic blade 1149 is acoustically coupled to the ultrasonic transducer 1120. The handpiece 1109 comprises a trigger 1147 to operate the clamp arm 1146 and a combination of the toggle buttons 1137a, 1137b, 1137c to energize and drive the ultrasonic blade 1149 or other function. The toggle buttons 1137a, 1137b, 1137c can be configured to energize the ultrasonic transducer 1120 with the generator 1100 and energize the ultrasonic blade 1149 with a bipolar energy source also contained within the generator 1100.


The generator 1100 is configurable for use with a variety of surgical instruments. According to various forms, the generator 1100 may be configurable for use with different surgical instruments of different types including, for example, the ultrasonic surgical instrument 1104, the RF electrosurgical instrument 1106, and the multifunction surgical instrument 1108 that integrates RF and ultrasonic energies delivered simultaneously from the generator 1100. Although in the form of FIG. 3 the generator 1100 is shown separate from the surgical instruments 1104, 1106, 1108, in another form the generator 1100 may be formed integrally with any one of the surgical instruments 1104, 1106, 1108 to form a unitary surgical system. As discussed above, the generator 1100 comprises an input device 1110 located on a front panel of the generator 1100 console. The input device 1110 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. The generator 1100 also may comprise one or more output devices 1112. Further aspects of generators for digitally generating electrical signal waveforms and surgical instruments are described in US patent publication US-2017-0086914-A1, which is herein incorporated by reference in its entirety.



FIG. 4 is an end effector 1122 of the example ultrasonic device 1104, in accordance with at least one aspect of the present disclosure. The end effector 1122 may comprise a blade 1128 that may be coupled to the ultrasonic transducer 1120 via a wave guide. When driven by the ultrasonic transducer 1120, the blade 1128 may vibrate and, when brought into contact with tissue, may cut and/or coagulate the tissue, as described herein. According to various aspects, and as illustrated in FIG. 4, the end effector 1122 may also comprise a clamp arm 1140 that may be configured for cooperative action with the blade 1128 of the end effector 1122. With the blade 1128, the clamp arm 1140 may comprise a set of jaws. The clamp arm 1140 may be pivotally connected at a distal end of a shaft 1126 of the instrument portion 1104. The clamp arm 1140 may include a clamp arm tissue pad 1163, which may be formed from TEFLON® or other suitable low-friction material. The pad 1163 may be mounted for cooperation with the blade 1128, with pivotal movement of the clamp arm 1140 positioning the clamp pad 1163 in substantially parallel relationship to, and in contact with, the blade 1128. By this construction, a tissue bite to be clamped may be grasped between the tissue pad 1163 and the blade 1128. The tissue pad 1163 may be provided with a sawtooth-like configuration including a plurality of axially spaced, proximally extending gripping teeth 1161 to enhance the gripping of tissue in cooperation with the blade 1128. The clamp arm 1140 may transition from the open position shown in FIG. 4 to a closed position (with the clamp arm 1140 in contact with or proximity to the blade 1128) in any suitable manner. For example, the handpiece 1105 may comprise a jaw closure trigger. When actuated by a clinician, the jaw closure trigger may pivot the clamp arm 1140 in any suitable manner.


The generator 1100 may be activated to provide the drive signal to the ultrasonic transducer 1120 in any suitable manner. For example, the generator 1100 may comprise a foot switch 1430 (FIG. 5) coupled to the generator 1100 via a footswitch cable 1432. A clinician may activate the ultrasonic transducer 1120, and thereby the ultrasonic transducer 1120 and blade 1128, by depressing the foot switch 1430. In addition, or instead of the foot switch 1430, some aspects of the device 1104 may utilize one or more switches positioned on the handpiece 1105 that, when activated, may cause the generator 1100 to activate the ultrasonic transducer 1120. In one aspect, for example, the one or more switches may comprise a pair of toggle buttons 1134, 1134a, 1134b (FIG. 3), for example, to determine an operating mode of the device 1104. When the toggle button 1134a is depressed, for example, the ultrasonic generator 1100 may provide a maximum drive signal to the ultrasonic transducer 1120, causing it to produce maximum ultrasonic energy output. Depressing toggle button 1134b may cause the ultrasonic generator 1100 to provide a user-selectable drive signal to the ultrasonic transducer 1120, causing it to produce less than the maximum ultrasonic energy output. The device 1104 additionally or alternatively may comprise a second switch to, for example, indicate a position of a jaw closure trigger for operating the jaws via the clamp arm 1140 of the end effector 1122. Also, in some aspects, the ultrasonic generator 1100 may be activated based on the position of the jaw closure trigger, (e.g., as the clinician depresses the jaw closure trigger to close the jaws via the clamp arm 1140, ultrasonic energy may be applied).


Additionally or alternatively, the one or more switches may comprise a toggle button 1134 that, when depressed, causes the generator 1100 to provide a pulsed output (FIG. 3). The pulses may be provided at any suitable frequency and grouping, for example. In certain aspects, the power level of the pulses may be the power levels associated with toggle buttons 1134a, 1134b (maximum, less than maximum), for example.


It will be appreciated that a device 1104 may comprise any combination of the toggle buttons 1134a, 1134b, 1134 (FIG. 3). For example, the device 1104 could be configured to have only two toggle buttons: a toggle button 1134a for producing maximum ultrasonic energy output and a toggle button 1134 for producing a pulsed output at either the maximum or less than maximum power level per. In this way, the drive signal output configuration of the generator 1100 could be five continuous signals, or any discrete number of individual pulsed signals (1, 2, 3, 4, or 5). In certain aspects, the specific drive signal configuration may be controlled based upon, for example, EEPROM settings in the generator 1100 and/or user power level selection(s).


In certain aspects, a two-position switch may be provided as an alternative to a toggle button 1134 (FIG. 3). For example, a device 1104 may include a toggle button 1134a for producing a continuous output at a maximum power level and a two-position toggle button 1134b. In a first detented position, toggle button 1134b may produce a continuous output at a less than maximum power level, and in a second detented position the toggle button 1134b may produce a pulsed output (e.g., at either a maximum or less than maximum power level, depending upon the EEPROM settings).


In some aspects, the RF electrosurgical end effector 1124, 1125 (FIG. 3) may also comprise a pair of electrodes. The electrodes may be in communication with the generator 1100, for example, via a cable. The electrodes may be used, for example, to measure an impedance of a tissue bite present between the clamp arm 1142a, 1146 and the blade 1142b, 1149. The generator 1100 may provide a signal (e.g., a non-therapeutic signal) to the electrodes. The impedance of the tissue bite may be found, for example, by monitoring the current, voltage, etc. of the signal.


In various aspects, the generator 1100 may comprise several separate functional elements, such as modules and/or blocks, as shown in FIG. 5, a diagram of the surgical system 1000 of FIG. 3. Different functional elements or modules may be configured for driving the different kinds of surgical devices 1104, 1106, 1108. For example an ultrasonic generator module may drive an ultrasonic device, such as the ultrasonic device 1104. An electrosurgery/RF generator module may drive the electrosurgical device 1106. The modules may generate respective drive signals for driving the surgical devices 1104, 1106, 1108. In various aspects, the ultrasonic generator module and/or the electrosurgery/RF generator module each may be formed integrally with the generator 1100. Alternatively, one or more of the modules may be provided as a separate circuit module electrically coupled to the generator 1100. (The modules are shown in phantom to illustrate this option.) Also, in some aspects, the electrosurgery/RF generator module may be formed integrally with the ultrasonic generator module, or vice versa.


In accordance with the described aspects, the ultrasonic generator module may produce a drive signal or signals of particular voltages, currents, and frequencies (e.g. 55,500 cycles per second, or Hz). The drive signal or signals may be provided to the ultrasonic device 1104, and specifically to the transducer 1120, which may operate, for example, as described above. In one aspect, the generator 1100 may be configured to produce a drive signal of a particular voltage, current, and/or frequency output signal that can be stepped with high resolution, accuracy, and repeatability.


In accordance with the described aspects, the electrosurgery/RF generator module may generate a drive signal or signals with output power sufficient to perform bipolar electrosurgery using radio frequency (RF) energy. In bipolar electrosurgery applications, the drive signal may be provided, for example, to the electrodes of the electrosurgical device 1106, for example, as described above. Accordingly, the generator 1100 may be configured for therapeutic purposes by applying electrical energy to the tissue sufficient for treating the tissue (e.g., coagulation, cauterization, tissue welding, etc.).


The generator 1100 may comprise an input device 2150 (FIG. 8B) located, for example, on a front panel of the generator 1100 console. The input device 2150 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. In operation, the user can program or otherwise control operation of the generator 1100 using the input device 2150. The input device 2150 may comprise any suitable device that generates signals that can be used by the generator (e.g., by one or more processors contained in the generator) to control the operation of the generator 1100 (e.g., operation of the ultrasonic generator module and/or electrosurgery/RF generator module). In various aspects, the input device 2150 includes one or more of: buttons, switches, thumbwheels, keyboard, keypad, touch screen monitor, pointing device, remote connection to a general purpose or dedicated computer. In other aspects, the input device 2150 may comprise a suitable user interface, such as one or more user interface screens displayed on a touch screen monitor, for example. Accordingly, by way of the input device 2150, the user can set or program various operating parameters of the generator, such as, for example, current (I), voltage (V), frequency (f), and/or period (T) of a drive signal or signals generated by the ultrasonic generator module and/or electrosurgery/RF generator module.


The generator 1100 may also comprise an output device 2140 (FIG. 8B) located, for example, on a front panel of the generator 1100 console. The output device 2140 includes one or more devices for providing a sensory feedback to a user. Such devices may comprise, for example, visual feedback devices (e.g., an LCD display screen, LED indicators), audio feedback devices (e.g., a speaker, a buzzer) or tactile feedback devices (e.g., haptic actuators).


Although certain modules and/or blocks of the generator 1100 may be described by way of example, it can be appreciated that a greater or lesser number of modules and/or blocks may be used and still fall within the scope of the aspects. Further, although various aspects may be described in terms of modules and/or blocks to facilitate description, such modules and/or blocks may be implemented by one or more hardware components, e.g., processors, Digital Signal Processors (DSPs), Programmable Logic Devices (PLDs), Application Specific Integrated Circuits (ASICs), circuits, registers and/or software components, e.g., programs, subroutines, logic and/or combinations of hardware and software components.


In one aspect, the ultrasonic generator drive module and electrosurgery/RF drive module 1110 (FIG. 3) may comprise one or more embedded applications implemented as firmware, software, hardware, or any combination thereof. The modules may comprise various executable modules such as software, programs, data, drivers, application program interfaces (APIs), and so forth. The firmware may be stored in nonvolatile memory (NVM), such as in bit-masked read-only memory (ROM) or flash memory. In various implementations, storing the firmware in ROM may preserve flash memory. The NVM may comprise other types of memory including, for example, programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or battery backed random-access memory (RAM) such as dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), and/or synchronous DRAM (SDRAM).


In one aspect, the modules comprise a hardware component implemented as a processor for executing program instructions for monitoring various measurable characteristics of the devices 1104, 1106, 1108 and generating a corresponding output drive signal or signals for operating the devices 1104, 1106, 1108. In aspects in which the generator 1100 is used in conjunction with the device 1104, the drive signal may drive the ultrasonic transducer 1120 in cutting and/or coagulation operating modes. Electrical characteristics of the device 1104 and/or tissue may be measured and used to control operational aspects of the generator 1100 and/or provided as feedback to the user. In aspects in which the generator 1100 is used in conjunction with the device 1106, the drive signal may supply electrical energy (e.g., RF energy) to the end effector 1124 in cutting, coagulation and/or desiccation modes. Electrical characteristics of the device 1106 and/or tissue may be measured and used to control operational aspects of the generator 1100 and/or provided as feedback to the user. In various aspects, as previously discussed, the hardware components may be implemented as DSP, PLD, ASIC, circuits, and/or registers. In one aspect, the processor may be configured to store and execute computer software program instructions to generate the step function output signals for driving various components of the devices 1104, 1106, 1108, such as the ultrasonic transducer 1120 and the end effectors 1122, 1124, 1125.


An electromechanical ultrasonic system includes an ultrasonic transducer, a waveguide, and an ultrasonic blade. The electromechanical ultrasonic system has an initial resonant frequency defined by the physical properties of the ultrasonic transducer, the waveguide, and the ultrasonic blade. The ultrasonic transducer is excited by an alternating voltage Vg(t) and current Ig(t) signal equal to the resonant frequency of the electromechanical ultrasonic system. When the electromechanical ultrasonic system is at resonance, the phase difference between the voltage Vg(t) and current Ig(t) signals is zero. Stated another way, at resonance the inductive impedance is equal to the capacitive impedance. As the ultrasonic blade heats up, the compliance of the ultrasonic blade (modeled as an equivalent capacitance) causes the resonant frequency of the electromechanical ultrasonic system to shift. Thus, the inductive impedance is no longer equal to the capacitive impedance causing a mismatch between the drive frequency and the resonant frequency of the electromechanical ultrasonic system. The system is now operating “off-resonance.” The mismatch between the drive frequency and the resonant frequency is manifested as a phase difference between the voltage Vg(t) and current Ig(t) signals applied to the ultrasonic transducer. The generator electronics can easily monitor the phase difference between the voltage Vg(t) and current Ig(t) signals and can continuously adjust the drive frequency until the phase difference is once again zero. At this point, the new drive frequency is equal to the new resonant frequency of the electromechanical ultrasonic system. The change in phase and/or frequency can be used as an indirect measurement of the ultrasonic blade temperature.


As shown in FIG. 6, the electromechanical properties of the ultrasonic transducer may be modeled as an equivalent circuit comprising a first branch having a static capacitance and a second “motional” branch having a serially connected inductance, resistance and capacitance that define the electromechanical properties of a resonator. Known ultrasonic generators may include a tuning inductor for tuning out the static capacitance at a resonant frequency so that substantially all of generator's drive signal current flows into the motional branch. Accordingly, by using a tuning inductor, the generator's drive signal current represents the motional branch current, and the generator is thus able to control its drive signal to maintain the ultrasonic transducer's resonant frequency. The tuning inductor may also transform the phase impedance plot of the ultrasonic transducer to improve the generator's frequency lock capabilities. However, the tuning inductor must be matched with the specific static capacitance of an ultrasonic transducer at the operational resonance frequency. In other words, a different ultrasonic transducer having a different static capacitance requires a different tuning inductor.



FIG. 6 illustrates an equivalent circuit 1500 of an ultrasonic transducer, such as the ultrasonic transducer 1120, according to one aspect. The circuit 1500 comprises a first “motional” branch having a serially connected inductance Ls, resistance Rs and capacitance Cs that define the electromechanical properties of the resonator, and a second capacitive branch having a static capacitance C0. Drive current Ig(t) may be received from a generator at a drive voltage Vg(t), with motional current Im(t) flowing through the first branch and current Ig(t)−Im(t) flowing through the capacitive branch. Control of the electromechanical properties of the ultrasonic transducer may be achieved by suitably controlling Ig(t) and Vg(t). As explained above, known generator architectures may include a tuning inductor Lt (shown in phantom in FIG. 6) in a parallel resonance circuit for tuning out the static capacitance C0 at a resonant frequency so that substantially all of the generator's current output Ig(t) flows through the motional branch. In this way, control of the motional branch current Im(t) is achieved by controlling the generator current output Ig(t). The tuning inductor Lt is specific to the static capacitance C0 of an ultrasonic transducer, however, and a different ultrasonic transducer having a different static capacitance requires a different tuning inductor Lt. Moreover, because the tuning inductor Lt is matched to the nominal value of the static capacitance C0 at a single resonant frequency, accurate control of the motional branch current Im(t) is assured only at that frequency. As frequency shifts down with transducer temperature, accurate control of the motional branch current is compromised.


Various aspects of the generator 1100 may not rely on a tuning inductor Lt to monitor the motional branch current Im(t). Instead, the generator 1100 may use the measured value of the static capacitance C0 in between applications of power for a specific ultrasonic surgical device 1104 (along with drive signal voltage and current feedback data) to determine values of the motional branch current Im(t) on a dynamic and ongoing basis (e.g., in real-time). Such aspects of the generator 1100 are therefore able to provide virtual tuning to simulate a system that is tuned or resonant with any value of static capacitance C0 at any frequency, and not just at a single resonant frequency dictated by a nominal value of the static capacitance C0.



FIG. 7 is a simplified block diagram of one aspect of the generator 1100 for providing inductorless tuning as described above, among other benefits. FIGS. 8A-8C illustrate an architecture of the generator 1100 of FIG. 7 according to one aspect. With reference to FIG. 7, the generator 1100 may comprise a patient isolated stage 1520 in communication with a non-isolated stage 1540 via a power transformer 1560. A secondary winding 1580 of the power transformer 1560 is contained in the isolated stage 1520 and may comprise a tapped configuration (e.g., a center-tapped or non-center tapped configuration) to define drive signal outputs 1600a, 1600b, 1600c for outputting drive signals to different surgical devices, such as, for example, an ultrasonic surgical device 1104 and an electrosurgical device 1106. In particular, drive signal outputs 1600a, 1600b, 1600c may output a drive signal (e.g., a 420V RMS drive signal) to an ultrasonic surgical device 1104, and drive signal outputs 1600a, 1600b, 1600c may output a drive signal (e.g., a 100V RMS drive signal) to an electrosurgical device 1106, with output 1600b corresponding to the center tap of the power transformer 1560. The non-isolated stage 1540 may comprise a power amplifier 1620 having an output connected to a primary winding 1640 of the power transformer 1560. In certain aspects the power amplifier 1620 may comprise a push-pull amplifier, for example. The non-isolated stage 1540 may further comprise a programmable logic device 1660 for supplying a digital output to a digital-to-analog converter (DAC) 1680, which in turn supplies a corresponding analog signal to an input of the power amplifier 1620. In certain aspects the programmable logic device 1660 may comprise a field-programmable gate array (FPGA), for example. The programmable logic device 1660, by virtue of controlling the power amplifier's 1620 input via the DAC 1680, 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 1600a, 1600b, 1600c. In certain aspects and as discussed below, the programmable logic device 1660, in conjunction with a processor (e.g., processor 1740 discussed below), may implement a number of digital signal processing (DSP)-based and/or other control algorithms to control parameters of the drive signals output by the generator 1100.


Power may be supplied to a power rail of the power amplifier 1620 by a switch-mode regulator 1700. In certain aspects the switch-mode regulator 1700 may comprise an adjustable buck regulator 1170, for example. As discussed above, the non-isolated stage 1540 may further comprise a processor 1740, which in one aspect may comprise a DSP processor such as an ADSP-21469 SHARC DSP, available from Analog Devices, Norwood, Mass., for example. In certain aspects the processor 1740 may control operation of the switch-mode power converter 1700 responsive to voltage feedback data received from the power amplifier 1620 by the processor 1740 via an analog-to-digital converter (ADC) 1760. In one aspect, for example, the processor 1740 may receive as input, via the ADC 1760, the waveform envelope of a signal (e.g., an RF signal) being amplified by the power amplifier 1620. The processor 1740 may then control the switch-mode regulator 1700 (e.g., via a pulse-width modulated (PWM) output) such that the rail voltage supplied to the power amplifier 1620 tracks the waveform envelope of the amplified signal. By dynamically modulating the rail voltage of the power amplifier 1620 based on the waveform envelope, the efficiency of the power amplifier 1620 may be significantly improved relative to a fixed rail voltage amplifier scheme. The processor 1740 may be configured for wired or wireless communication.


In certain aspects and as discussed in further detail in connection with FIGS. 9A-9B, the programmable logic device 1660, in conjunction with the processor 1740, may implement a direct digital synthesizer (DDS) control scheme to control the waveform shape, frequency and/or amplitude of drive signals output by the generator 1100. In one aspect, for example, the programmable logic device 1660 may implement a DDS control algorithm 2680 (FIG. 9A) by recalling waveform samples stored in a dynamically-updated look-up 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 the ultrasonic transducer 1120, 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 1100 is impacted by various sources of distortion present in the output drive circuit (e.g., the power transformer 1560, the power amplifier 1620), 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 processor 1740, 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 aspect, 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 aspects, 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 1540 may further comprise an ADC 1780 and an ADC 1800 coupled to the output of the power transformer 1560 via respective isolation transformers 1820, 1840 for respectively sampling the voltage and current of drive signals output by the generator 1100. In certain aspects, the ADCs 1780, 1800 may be configured to sample at high speeds (e.g., 80 Msps) to enable oversampling of the drive signals. In one aspect, for example, the sampling speed of the ADCs 1780, 1800 may enable approximately 200× (depending on drive frequency) oversampling of the drive signals. In certain aspects, the sampling operations of the ADCs 1780, 1800 may be performed by a single ADC receiving input voltage and current signals via a two-way multiplexer. The use of high-speed sampling in aspects of the generator 1100 may enable, among other things, calculation of the complex current flowing through the motional branch (which may be used in certain aspects 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 ADCs 1780, 1800 may be received and processed (e.g., FIFO buffering, multiplexing) by the programmable logic device 1660 and stored in data memory for subsequent retrieval by, for example, the processor 1740. 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 aspects, 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 programmable logic device 1660 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 aspects, 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 aspect, for example, voltage and current feedback data may be used to determine impedance phase, e.g., the phase difference between the voltage and current drive signals. 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 processor 1740, for example, with the frequency control signal being supplied as input to a DDS control algorithm implemented by the programmable logic device 1660.


The impedance phase may be determined through Fourier analysis. In one aspect, the phase difference between the generator voltage Vg(t) and generator current Ig(t) driving signals may be determined using the Fast Fourier Transform (FFT) or the Discrete Fourier Transform (DFT) as follows:








V
g



(
t
)


=


A
1



cos


(


2

π






f
0


t

+

φ
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Evaluating the Fourier Transform at the frequency of the sinusoid yields:








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Other approaches include weighted least-squares estimation, Kalman filtering, and space-vector-based techniques. Virtually all of the processing in an FFT or DFT technique may be performed in the digital domain with the aid of the 2-channel high speed ADC 1780, 1800, for example. In one technique, the digital signal samples of the voltage and current signals are Fourier transformed with an FFT or a DFT. The phase angle φ at any point in time can be calculated by:

φ=2πft+φ0

where φ is the phase angle, f is the frequency, t is time, and φ0 is the phase at t=0.


Another technique for determining the phase difference between the voltage Vg(t) and current Ig(t) signals is the zero-crossing method and produces highly accurate results. For voltage Vg(t) and current Ig(t) signals having the same frequency, each negative to positive zero-crossing of voltage signal Vg(t) triggers the start of a pulse, while each negative to positive zero-crossing of current signal Ig(t) triggers the end of the pulse. The result is a pulse train with a pulse width proportional to the phase angle between the voltage signal and the current signal. In one aspect, the pulse train may be passed through an averaging filter to yield a measure of the phase difference. Furthermore, if the positive to negative zero crossings also are used in a similar manner, and the results averaged, any effects of DC and harmonic components can be reduced. In one implementation, the analog voltage Vg(t) and current Ig(t) signals are converted to digital signals that are high if the analog signal is positive and low if the analog signal is negative. High accuracy phase estimates require sharp transitions between high and low. In one aspect, a Schmitt trigger along with an RC stabilization network may be employed to convert the analog signals into digital signals. In other aspects, an edge triggered RS flip-flop and ancillary circuitry may be employed. In yet another aspect, the zero-crossing technique may employ an eXclusive OR (XOR) gate.


Other techniques for determining the phase difference between the voltage and current signals include Lissajous figures and monitoring the image; methods such as the three-voltmeter method, the crossed-coil method, vector voltmeter and vector impedance methods; and using phase standard instruments, phase-locked loops, and other techniques as described in Phase Measurement, Peter O'Shea, 2000 CRC Press LLC, <http://www.engnetbase.com>, which is incorporated herein by reference.


In another aspect, 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 aspects, control of the current amplitude may be implemented by control algorithm, such as, for example, a proportional-integral-derivative (PID) control algorithm, in the processor 1740. 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 programmable logic device 1660 and/or the full-scale output voltage of the DAC 1680 (which supplies the input to the power amplifier 1620) via a DAC 1860.


The non-isolated stage 1540 may further comprise a processor 1900 for providing, among other things, user interface (UI) functionality. In one aspect, the processor 1900 may comprise an Atmel AT91 SAM9263 processor having an ARM 926EJ-S core, available from Atmel Corporation, San Jose, Calif., for example. Examples of UI functionality supported by the processor 1900 may include audible and visual user feedback, communication with peripheral devices (e.g., via a Universal Serial Bus (USB) interface), communication with a foot switch 1430, communication with an input device 2150 (e.g., a touch screen display) and communication with an output device 2140 (e.g., a speaker). The processor 1900 may communicate with the processor 1740 and the programmable logic device (e.g., via a serial peripheral interface (SPI) bus). Although the processor 1900 may primarily support UI functionality, it may also coordinate with the processor 1740 to implement hazard mitigation in certain aspects. For example, the processor 1900 may be programmed to monitor various aspects of user input and/or other inputs (e.g., touch screen inputs 2150, foot switch 1430 inputs, temperature sensor inputs 2160) and may disable the drive output of the generator 1100 when an erroneous condition is detected.


In certain aspects, both the processor 1740 (FIGS. 7, 8A) and the processor 1900 (FIGS. 7, 8B) may determine and monitor the operating state of the generator 1100. For processor 1740, the operating state of the generator 1100 may dictate, for example, which control and/or diagnostic processes are implemented by the processor 1740. For processor 1900, the operating state of the generator 1100 may dictate, for example, which elements of a user interface (e.g., display screens, sounds) are presented to a user. The processors 1740, 1900 may independently maintain the current operating state of the generator 1100 and recognize and evaluate possible transitions out of the current operating state. The processor 1740 may function as the master in this relationship and determine when transitions between operating states are to occur. The processor 1900 may be aware of valid transitions between operating states and may confirm if a particular transition is appropriate. For example, when the processor 1740 instructs the processor 1900 to transition to a specific state, the processor 1900 may verify that the requested transition is valid. In the event that a requested transition between states is determined to be invalid by the processor 1900, the processor 1900 may cause the generator 1100 to enter a failure mode.


The non-isolated stage 1540 may further comprise a controller 1960 (FIGS. 7, 8B) for monitoring input devices 2150 (e.g., a capacitive touch sensor used for turning the generator 1100 on and off, a capacitive touch screen). In certain aspects, the controller 1960 may comprise at least one processor and/or other controller device in communication with the processor 1900. In one aspect, for example, the controller 1960 may comprise a processor (e.g., a Mega168 8-bit controller available from Atmel) configured to monitor user input provided via one or more capacitive touch sensors. In one aspect, the controller 1960 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 aspects, when the generator 1100 is in a “power off” state, the controller 1960 may continue to receive operating power (e.g., via a line from a power supply of the generator 1100, such as the power supply 2110 (FIG. 7) discussed below). In this way, the controller 1960 may continue to monitor an input device 2150 (e.g., a capacitive touch sensor located on a front panel of the generator 1100) for turning the generator 1100 on and off. When the generator 1100 is in the “power off” state, the controller 1960 may wake the power supply (e.g., enable operation of one or more DC/DC voltage converters 2130 (FIG. 7) of the power supply 2110) if activation of the “on/off” input device 2150 by a user is detected. The controller 1960 may therefore initiate a sequence for transitioning the generator 1100 to a “power on” state. Conversely, the controller 1960 may initiate a sequence for transitioning the generator 1100 to the “power off” state if activation of the “on/off” input device 2150 is detected when the generator 1100 is in the “power on” state. In certain aspects, for example, the controller 1960 may report activation of the “on/off” input device 2150 to the processor 1900, which in turn implements the necessary process sequence for transitioning the generator 1100 to the “power off” state. In such aspects, the controller 1960 may have no independent ability for causing the removal of power from the generator 1100 after its “power on” state has been established.


In certain aspects, the controller 1960 may cause the generator 1100 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 aspects, the isolated stage 1520 may comprise an instrument interface circuit 1980 to, for example, provide a communication interface between a control circuit of a surgical device (e.g., a control circuit comprising handpiece switches) and components of the non-isolated stage 1540, such as, for example, the programmable logic device 1660, the processor 1740 and/or the processor 1900. The instrument interface circuit 1980 may exchange information with components of the non-isolated stage 1540 via a communication link that maintains a suitable degree of electrical isolation between the stages 1520, 1540, such as, for example, an infrared (IR)-based communication link. Power may be supplied to the instrument interface circuit 1980 using, for example, a low-dropout voltage regulator powered by an isolation transformer driven from the non-isolated stage 1540.


In one aspect, the instrument interface circuit 1980 may comprise a programmable logic device 2000 (e.g., an FPGA) in communication with a signal conditioning circuit 2020 (FIG. 7 and FIG. 8C). The signal conditioning circuit 2020 may be configured to receive a periodic signal from the programmable logic device 2000 (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 device control circuit (e.g., by using a conductive pair in a cable that connects the generator 1100 to the surgical device) and monitored to determine a state or configuration of the control circuit. For example, 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 discernible based on the one or more characteristics. In one aspect, for example, the signal conditioning circuit 2020 may comprise an ADC for generating samples of a voltage signal appearing across inputs of the control circuit resulting from passage of interrogation signal therethrough. The programmable logic device 2000 (or a component of the non-isolated stage 1540) may then determine the state or configuration of the control circuit based on the ADC samples.


In one aspect, the instrument interface circuit 1980 may comprise a first data circuit interface 2040 to enable information exchange between the programmable logic device 2000 (or other element of the instrument interface circuit 1980) and a first data circuit disposed in or otherwise associated with a surgical device. In certain aspects, for example, a first data circuit 2060 may be disposed in a cable integrally attached to a surgical device handpiece, or in an adaptor for interfacing a specific surgical device type or model with the generator 1100. In certain aspects, the first data circuit may comprise a non-volatile storage device, such as an electrically erasable programmable read-only memory (EEPROM) device. In certain aspects and referring again to FIG. 7, the first data circuit interface 2040 may be implemented separately from the programmable logic device 2000 and comprise suitable circuitry (e.g., discrete logic devices, a processor) to enable communication between the programmable logic device 2000 and the first data circuit. In other aspects, the first data circuit interface 2040 may be integral with the programmable logic device 2000.


In certain aspects, the first data circuit 2060 may store information pertaining to the particular surgical device 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 device has been used, and/or any other type of information. This information may be read by the instrument interface circuit 1980 (e.g., by the programmable logic device 2000), transferred to a component of the non-isolated stage 1540 (e.g., to programmable logic device 1660, processor 1740 and/or processor 1900) for presentation to a user via an output device 2140 and/or for controlling a function or operation of the generator 1100. Additionally, any type of information may be communicated to first data circuit 2060 for storage therein via the first data circuit interface 2040 (e.g., using the programmable logic device 2000). Such information may comprise, for example, an updated number of operations in which the surgical device 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., instrument 1106 may be detachable from handpiece 1107) to promote instrument interchangeability and/or disposability. In such cases, known 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 device instruments to address this issue is problematic from a compatibility standpoint, however. For example, it may be impractical to design a surgical device to maintain backward compatibility with generators that lack the requisite data reading functionality due to, for example, differing signal schemes, design complexity and cost. Other aspects of instruments 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 devices with current generator platforms.


Additionally, aspects of the generator 1100 may enable communication with instrument-based data circuits. For example, the generator 1100 may be configured to communicate with a second data circuit (e.g., a data circuit) contained in an instrument (e.g., instrument 1104, 1106 or 1108) of a surgical device. The instrument interface circuit 1980 may comprise a second data circuit interface 2100 to enable this communication. In one aspect, the second data circuit interface 2100 may comprise a tri-state digital interface, although other interfaces may also be used. In certain aspects, the second data circuit may generally be any circuit for transmitting and/or receiving data. In one aspect, 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. Additionally or alternatively, any type of information may be communicated to the second data circuit for storage therein via the second data circuit interface 2100 (e.g., using the programmable logic device 2000). 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 aspects, the second data circuit may transmit data acquired by one or more sensors (e.g., an instrument-based temperature sensor). In certain aspects, the second data circuit may receive data from the generator 1100 and provide an indication to a user (e.g., an LED indication or other visible indication) based on the received data.


In certain aspects, the second data circuit and the second data circuit interface 2100 may be configured such that communication between the programmable logic device 2000 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 1100). In one aspect, 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 2020 to a control circuit in a handpiece. In this way, design changes or modifications to the surgical device that might otherwise be necessary are minimized or reduced. Moreover, because different types of communications can be implemented over a common physical channel (either with or without frequency-band separation), 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 device instrument.


In certain aspects, the isolated stage 1520 may comprise at least one blocking capacitor 2960-1 (FIG. 8C) connected to the drive signal output 1600b 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 aspect, a second blocking capacitor 2960-2 may be provided in series with the blocking capacitor 2960-1, with current leakage from a point between the blocking capacitors 2960-1, 2960-2 being monitored by, for example, an ADC 2980 for sampling a voltage induced by leakage current. The samples may be received by the programmable logic device 2000, for example. Based on changes in the leakage current (as indicated by the voltage samples in the aspect of FIG. 7), the generator 1100 may determine when at least one of the blocking capacitors 2960-1, 2960-2 has failed. Accordingly, the aspect of FIG. 7 may provide a benefit over single-capacitor designs having a single point of failure.


In certain aspects, the non-isolated stage 1540 may comprise a power supply 2110 for outputting DC power at a suitable voltage and current. The power supply may comprise, for example, a 400 W power supply for outputting a 48 VDC system voltage. As discussed above, the power supply 2110 may further comprise one or more DC/DC voltage converters 2130 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 1100. As discussed above in connection with the controller 1960, one or more of the DC/DC voltage converters 2130 may receive an input from the controller 1960 when activation of the “on/off” input device 2150 by a user is detected by the controller 1960 to enable operation of, or wake, the DC/DC voltage converters 2130.



FIGS. 9A-9B illustrate certain functional and structural aspects of one aspect of the generator 1100. Feedback indicating current and voltage output from the secondary winding 1580 of the power transformer 1560 is received by the ADCs 1780, 1800, respectively. As shown, the ADCs 1780, 1800 may be implemented as a 2-channel ADC and may sample the feedback signals at a high speed (e.g., 80 Msps) to enable oversampling (e.g., approximately 200× oversampling) of the drive signals. The current and voltage feedback signals may be suitably conditioned in the analog domain (e.g., amplified, filtered) prior to processing by the ADCs 1780, 1800. Current and voltage feedback samples from the ADCs 1780, 1800 may be individually buffered and subsequently multiplexed or interleaved into a single data stream within block 2120 of the programmable logic device 1660. In the aspect of FIGS. 9A-9B, the programmable logic device 1660 comprises an FPGA.


The multiplexed current and voltage feedback samples may be received by a parallel data acquisition port (PDAP) implemented within block 2144 of the processor 1740. The PDAP may comprise a packing unit for implementing any of a number of methodologies for correlating the multiplexed feedback samples with a memory address. In one aspect, for example, feedback samples corresponding to a particular LUT sample output by the programmable logic device 1660 may be stored at one or more memory addresses that are correlated or indexed with the LUT address of the LUT sample. In another aspect, feedback samples corresponding to a particular LUT sample output by the programmable logic device 1660 may be stored, along with the LUT address of the LUT sample, at a common memory location. In any event, the feedback samples may be stored such that the address of the LUT sample from which a particular set of feedback samples originated may be subsequently ascertained. As discussed above, synchronization of the LUT sample addresses and the feedback samples in this way contributes to the correct timing and stability of the pre-distortion algorithm. A direct memory access (DMA) controller implemented at block 2166 of the processor 1740 may store the feedback samples (and any LUT sample address data, where applicable) at a designated memory location 2180 of the processor 1740 (e.g., internal RAM).


Block 2200 of the processor 1740 may implement a pre-distortion algorithm for pre-distorting or modifying the LUT samples stored in the programmable logic device 1660 on a dynamic, ongoing basis. As discussed above, pre-distortion of the LUT samples may compensate for various sources of distortion present in the output drive circuit of the generator 1100. The pre-distorted LUT samples, when processed through the drive circuit, will therefore result in a drive signal having the desired waveform shape (e.g., sinusoidal) for optimally driving the ultrasonic transducer.


At block 2220 of the pre-distortion algorithm, the current through the motional branch of the ultrasonic transducer is determined. The motional branch current may be determined using Kirchhoff's Current Law based on, for example, the current and voltage feedback samples stored at memory location 2180 (which, when suitably scaled, may be representative of Ig and Vg in the model of FIG. 6 discussed above), a value of the ultrasonic transducer static capacitance C0 (measured or known a priori) and a known value of the drive frequency. A motional branch current sample for each set of stored current and voltage feedback samples associated with a LUT sample may be determined.


At block 2240 of the pre-distortion algorithm, each motional branch current sample determined at block 2220 is compared to a sample of a desired current waveform shape to determine a difference, or sample amplitude error, between the compared samples. For this determination, the sample of the desired current waveform shape may be supplied, for example, from a waveform shape LUT 2260 containing amplitude samples for one cycle of a desired current waveform shape. The particular sample of the desired current waveform shape from the LUT 2260 used for the comparison may be dictated by the LUT sample address associated with the motional branch current sample used in the comparison. Accordingly, the input of the motional branch current to block 2240 may be synchronized with the input of its associated LUT sample address to block 2240. The LUT samples stored in the programmable logic device 1660 and the LUT samples stored in the waveform shape LUT 2260 may therefore be equal in number. In certain aspects, the desired current waveform shape represented by the LUT samples stored in the waveform shape LUT 2260 may be a fundamental sine wave. Other waveform shapes may be desirable. For example, it is contemplated that a fundamental sine wave for driving main longitudinal motion of an ultrasonic transducer superimposed with one or more other drive signals at other frequencies, such as a third order harmonic for driving at least two mechanical resonances for beneficial vibrations of transverse or other modes, could be used.


Each value of the sample amplitude error determined at block 2240 may be transmitted to the LUT of the programmable logic device 1660 (shown at block 2280 in FIG. 9A) along with an indication of its associated LUT address. Based on the value of the sample amplitude error and its associated address (and, optionally, values of sample amplitude error for the same LUT address previously received), the LUT 2280 (or other control block of the programmable logic device 1660) may pre-distort or modify the value of the LUT sample stored at the LUT address such that the sample amplitude error is reduced or minimized. It will be appreciated that such pre-distortion or modification of each LUT sample in an iterative manner across the entire range of LUT addresses will cause the waveform shape of the generator's output current to match or conform to the desired current waveform shape represented by the samples of the waveform shape LUT 2260.


Current and voltage amplitude measurements, power measurements and impedance measurements may be determined at block 2300 of the processor 1740 based on the current and voltage feedback samples stored at memory location 2180. Prior to the determination of these quantities, the feedback samples may be suitably scaled and, in certain aspects, processed through a suitable filter 2320 to remove noise resulting from, for example, the data acquisition process and induced harmonic components. The filtered voltage and current samples may therefore substantially represent the fundamental frequency of the generator's drive output signal. In certain aspects, the filter 2320 may be a finite impulse response (FIR) filter applied in the frequency domain. Such aspects may use the Fast Fourier Transform (FFT) of the output drive signal current and voltage signals. In certain aspects, the resulting frequency spectrum may be used to provide additional generator functionality. In one aspect, for example, the ratio of the second and/or third order harmonic component relative to the fundamental frequency component may be used as a diagnostic indicator.


At block 2340 (FIG. 9B), a root mean square (RMS) calculation may be applied to a sample size of the current feedback samples representing an integral number of cycles of the drive signal to generate a measurement Irms representing the drive signal output current.


At block 2360, a root mean square (RMS) calculation may be applied to a sample size of the voltage feedback samples representing an integral number of cycles of the drive signal to determine a measurement Vrms representing the drive signal output voltage.


At block 2380, the current and voltage feedback samples may be multiplied point by point, and a mean calculation is applied to samples representing an integral number of cycles of the drive signal to determine a measurement Pr of the generator's real output power.


At block 2400, measurement Pa of the generator's apparent output power may be determined as the product Vrms·Irms.


At block 2420, measurement Zm of the load impedance magnitude may be determined as the quotient Vrms/Irms.


In certain aspects, the quantities Irms, Vrms, Pr, Pa and Zm determined at blocks 2340, 2360, 2380, 2400 and 2420 may be used by the generator 1100 to implement any of a number of control and/or diagnostic processes. In certain aspects, any of these quantities may be communicated to a user via, for example, an output device 2140 integral with the generator 1100 or an output device 2140 connected to the generator 1100 through a suitable communication interface (e.g., a USB interface). Various diagnostic processes may include, without limitation, handpiece integrity, instrument integrity, instrument attachment integrity, instrument overload, approaching instrument overload, frequency lock failure, over-voltage condition, over-current condition, over-power condition, voltage sense failure, current sense failure, audio indication failure, visual indication failure, short circuit condition, power delivery failure, or blocking capacitor failure, for example.


Block 2440 of the processor 1740 may implement a phase control algorithm for determining and controlling the impedance phase of an electrical load (e.g., the ultrasonic transducer) driven by the generator 1100. As discussed above, by controlling the frequency of the drive signal to minimize or reduce the difference between the determined impedance phase and an impedance phase setpoint (e.g., 0°), the effects of harmonic distortion may be minimized or reduced, and the accuracy of the phase measurement increased.


The phase control algorithm receives as input the current and voltage feedback samples stored in the memory location 2180. Prior to their use in the phase control algorithm, the feedback samples may be suitably scaled and, in certain aspects, processed through a suitable filter 2460 (which may be identical to filter 2320) to remove noise resulting from the data acquisition process and induced harmonic components, for example. The filtered voltage and current samples may therefore substantially represent the fundamental frequency of the generator's drive output signal.


At block 2480 of the phase control algorithm, the current through the motional branch of the ultrasonic transducer is determined. This determination may be identical to that described above in connection with block 2220 of the pre-distortion algorithm. The output of block 2480 may thus be, for each set of stored current and voltage feedback samples associated with a LUT sample, a motional branch current sample.


At block 2500 of the phase control algorithm, impedance phase is determined based on the synchronized input of motional branch current samples determined at block 2480 and corresponding voltage feedback samples. In certain aspects, the impedance phase is determined as the average of the impedance phase measured at the rising edge of the waveforms and the impedance phase measured at the falling edge of the waveforms.


At block 2520 of the of the phase control algorithm, the value of the impedance phase determined at block 2220 is compared to phase setpoint 2540 to determine a difference, or phase error, between the compared values.


At block 2560 (FIG. 9A) of the phase control algorithm, based on a value of phase error determined at block 2520 and the impedance magnitude determined at block 2420, a frequency output for controlling the frequency of the drive signal is determined. The value of the frequency output may be continuously adjusted by the block 2560 and transferred to a DDS control block 2680 (discussed below) in order to maintain the impedance phase determined at block 2500 at the phase setpoint (e.g., zero phase error). In certain aspects, the impedance phase may be regulated to a 0° phase setpoint. In this way, any harmonic distortion will be centered about the crest of the voltage waveform, enhancing the accuracy of phase impedance determination.


Block 2580 of the processor 1740 may implement an algorithm for modulating the current amplitude of the drive signal in order to control the drive signal current, voltage and power in accordance with user specified setpoints, or in accordance with requirements specified by other processes or algorithms implemented by the generator 1100. Control of these quantities may be realized, for example, by scaling the LUT samples in the LUT 2280 and/or by adjusting the full-scale output voltage of the DAC 1680 (which supplies the input to the power amplifier 1620) via a DAC 1860. Block 2600 (which may be implemented as a PID controller in certain aspects) may receive, as input, current feedback samples (which may be suitably scaled and filtered) from the memory location 2180. The current feedback samples may be compared to a “current demand” Id value dictated by the controlled variable (e.g., current, voltage or power) to determine if the drive signal is supplying the necessary current. In aspects in which drive signal current is the control variable, the current demand Id may be specified directly by a current setpoint 2620A (Isp). For example, an RMS value of the current feedback data (determined as in block 2340) may be compared to user-specified RMS current setpoint Isp to determine the appropriate controller action. If, for example, the current feedback data indicates an RMS value less than the current setpoint Isp, LUT scaling and/or the full-scale output voltage of the DAC 1680 may be adjusted by the block 2600 such that the drive signal current is increased. Conversely, block 2600 may adjust LUT scaling and/or the full-scale output voltage of the DAC 1680 to decrease the drive signal current when the current feedback data indicates an RMS value greater than the current setpoint Isp.


In aspects in which the drive signal voltage is the control variable, the current demand Id may be specified indirectly, for example, based on the current required to maintain a desired voltage setpoint 2620B (Vsp) given the load impedance magnitude Zm measured at block 2420 (e.g. Id=Vsp/Zm). Similarly, in aspects in which drive signal power is the control variable, the current demand Id may be specified indirectly, for example, based on the current required to maintain a desired power setpoint 2620C (Psp) given the voltage Vrms measured at blocks 2360 (e.g. Id=Psp/Vrms).


Block 2680 (FIG. 9A) may implement a DDS control algorithm for controlling the drive signal by recalling LUT samples stored in the LUT 2280. In certain aspects, the DDS control algorithm may be a numerically-controlled oscillator (NCO) algorithm for generating samples of a waveform at a fixed clock rate using a point (memory location)-skipping technique. The NCO algorithm may implement a phase accumulator, or frequency-to-phase converter, that functions as an address pointer for recalling LUT samples from the LUT 2280. In one aspect, the phase accumulator may be a D step size, modulo N phase accumulator, where D is a positive integer representing a frequency control value, and N is the number of LUT samples in the LUT 2280. A frequency control value of D=1, for example, may cause the phase accumulator to sequentially point to every address of the LUT 2280, resulting in a waveform output replicating the waveform stored in the LUT 2280. When D>1, the phase accumulator may skip addresses in the LUT 2280, resulting in a waveform output having a higher frequency. Accordingly, the frequency of the waveform generated by the DDS control algorithm may therefore be controlled by suitably varying the frequency control value. In certain aspects, the frequency control value may be determined based on the output of the phase control algorithm implemented at block 2440. The output of block 2680 may supply the input of DAC 1680, which in turn supplies a corresponding analog signal to an input of the power amplifier 1620.


Block 2700 of the processor 1740 may implement a switch-mode converter control algorithm for dynamically modulating the rail voltage of the power amplifier 1620 based on the waveform envelope of the signal being amplified, thereby improving the efficiency of the power amplifier 1620. In certain aspects, characteristics of the waveform envelope may be determined by monitoring one or more signals contained in the power amplifier 1620. In one aspect, for example, characteristics of the waveform envelope may be determined by monitoring the minima of a drain voltage (e.g., a MOSFET drain voltage) that is modulated in accordance with the envelope of the amplified signal. A minima voltage signal may be generated, for example, by a voltage minima detector coupled to the drain voltage. The minima voltage signal may be sampled by ADC 1760, with the output minima voltage samples being received at block 2720 of the switch-mode converter control algorithm. Based on the values of the minima voltage samples, block 2740 may control a PWM signal output by a PWM generator 2760, which, in turn, controls the rail voltage supplied to the power amplifier 1620 by the switch-mode regulator 1700. In certain aspects, as long as the values of the minima voltage samples are less than a minima target 2780 input into block 2720, the rail voltage may be modulated in accordance with the waveform envelope as characterized by the minima voltage samples. When the minima voltage samples indicate low envelope power levels, for example, block 2740 may cause a low rail voltage to be supplied to the power amplifier 1620, with the full rail voltage being supplied only when the minima voltage samples indicate maximum envelope power levels. When the minima voltage samples fall below the minima target 2780, block 2740 may cause the rail voltage to be maintained at a minimum value suitable for ensuring proper operation of the power amplifier 1620.



FIG. 10 illustrates a control circuit 500 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The control circuit 500 can be configured to implement various processes described herein. The control circuit 500 may comprise a microcontroller comprising one or more processors 502 (e.g., microprocessor, microcontroller) coupled to at least one memory circuit 504. The memory circuit 504 stores machine-executable instructions that, when executed by the processor 502, cause the processor 502 to execute machine instructions to implement various processes described herein. The processor 502 may be any one of a number of single-core or multicore processors known in the art. The memory circuit 504 may comprise volatile and non-volatile storage media. The processor 502 may include an instruction processing unit 506 and an arithmetic unit 508. The instruction processing unit may be configured to receive instructions from the memory circuit 504 of this disclosure.



FIG. 11 illustrates a combinational logic circuit 510 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The combinational logic circuit 510 can be configured to implement various processes described herein. The combinational logic circuit 510 may comprise a finite state machine comprising a combinational logic 512 configured to receive data associated with the surgical instrument or tool at an input 514, process the data by the combinational logic 512, and provide an output 516.



FIG. 12 illustrates a sequential logic circuit 520 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The sequential logic circuit 520 or the combinational logic 522 can be configured to implement various processes described herein. The sequential logic circuit 520 may comprise a finite state machine. The sequential logic circuit 520 may comprise a combinational logic 522, at least one memory circuit 524, and a clock 529, for example. The at least one memory circuit 524 can store a current state of the finite state machine. In certain instances, the sequential logic circuit 520 may be synchronous or asynchronous. The combinational logic 522 is configured to receive data associated with the surgical instrument or tool from an input 526, process the data by the combinational logic 522, and provide an output 528. In other aspects, the circuit may comprise a combination of a processor (e.g., processor 502, FIG. 13) and a finite state machine to implement various processes herein. In other aspects, the finite state machine may comprise a combination of a combinational logic circuit (e.g., combinational logic circuit 510, FIG. 14) and the sequential logic circuit 520.


In one aspect, the ultrasonic or high-frequency current generators of the surgical system 1000 may be configured to generate the electrical signal waveform digitally such that the desired using a predetermined number of phase points stored in a lookup table to digitize the wave shape. The phase points may be stored in a table defined in a memory, a field programmable gate array (FPGA), or any suitable non-volatile memory. FIG. 13 illustrates one aspect of a fundamental architecture for a digital synthesis circuit such as a direct digital synthesis (DDS) circuit 4100 configured to generate a plurality of wave shapes for the electrical signal waveform. The generator software and digital controls may command the FPGA to scan the addresses in the lookup table 4104 which in turn provides varying digital input values to a DAC circuit 4108 that feeds a power amplifier. The addresses may be scanned according to a frequency of interest. Using such a lookup table 4104 enables generating various types of wave shapes that can be fed into tissue or into a transducer, an RF electrode, multiple transducers simultaneously, multiple RF electrodes simultaneously, or a combination of RF and ultrasonic instruments. Furthermore, multiple lookup tables 4104 that represent multiple wave shapes can be created, stored, and applied to tissue from a generator.


The waveform signal may be configured to control at least one of an output current, an output voltage, or an output power of an ultrasonic transducer and/or an RF electrode, or multiples thereof (e.g. two or more ultrasonic transducers and/or two or more RF electrodes). Further, where the surgical instrument comprises an ultrasonic components, the waveform signal may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, a generator may be configured to provide a waveform signal to at least one surgical instrument wherein the waveform signal corresponds to at least one wave shape of a plurality of wave shapes in a table. Further, the waveform signal provided to the two surgical instruments may comprise two or more wave shapes. The table may comprise information associated with a plurality of wave shapes and the table may be stored within the generator. In one aspect or example, the table may be a direct digital synthesis table, which may be stored in an FPGA of the generator. The table may be addressed by anyway that is convenient for categorizing wave shapes. According to one aspect, the table, which may be a direct digital synthesis table, is addressed according to a frequency of the waveform signal. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in the table.


The analog electrical signal waveform may be configured to control at least one of an output current, an output voltage, or an output power of an ultrasonic transducer and/or an RF electrode, or multiples thereof (e.g., two or more ultrasonic transducers and/or two or more RF electrodes). Further, where the surgical instrument comprises ultrasonic components, the analog electrical signal waveform may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, the generator circuit may be configured to provide an analog electrical signal waveform to at least one surgical instrument wherein the analog electrical signal waveform corresponds to at least one wave shape of a plurality of wave shapes stored in a lookup table 4104. Further, the analog electrical signal waveform provided to the two surgical instruments may comprise two or more wave shapes. The lookup table 4104 may comprise information associated with a plurality of wave shapes and the lookup table 4104 may be stored either within the generator circuit or the surgical instrument. In one aspect or example, the lookup table 4104 may be a direct digital synthesis table, which may be stored in an FPGA of the generator circuit or the surgical instrument. The lookup table 4104 may be addressed by anyway that is convenient for categorizing wave shapes. According to one aspect, the lookup table 4104, which may be a direct digital synthesis table, is addressed according to a frequency of the desired analog electrical signal waveform. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in the lookup table 4104.


With the widespread use of digital techniques in instrumentation and communications systems, a digitally-controlled method of generating multiple frequencies from a reference frequency source has evolved and is referred to as direct digital synthesis. The basic architecture is shown in FIG. 13. In this simplified block diagram, a DDS circuit is coupled to a processor, controller, or a logic device of the generator circuit and to a memory circuit located in the generator circuit of the surgical system 1000. The DDS circuit 4100 comprises an address counter 4102, lookup table 4104, a register 4106, a DAC circuit 4108, and a filter 4112. A stable clock fc is received by the address counter 4102 and the register 4106 drives a programmable-read-only-memory (PROM) which stores one or more integral number of cycles of a sinewave (or other arbitrary waveform) in a lookup table 4104. As the address counter 4102 steps through memory locations, values stored in the lookup table 4104 are written to the register 4106, which is coupled to the DAC circuit 4108. The corresponding digital amplitude of the signal at the memory location of the lookup table 4104 drives the DAC circuit 4108, which in turn generates an analog output signal 4110. The spectral purity of the analog output signal 4110 is determined primarily by the DAC circuit 4108. The phase noise is basically that of the reference clock fc. The first analog output signal 4110 output from the DAC circuit 4108 is filtered by the filter 4112 and a second analog output signal 4114 output by the filter 4112 is provided to an amplifier having an output coupled to the output of the generator circuit. The second analog output signal has a frequency fout.


Because the DDS circuit 4100 is a sampled data system, issues involved in sampling must be considered: quantization noise, aliasing, filtering, etc. For instance, the higher order harmonics of the DAC circuit 4108 output frequencies fold back into the Nyquist bandwidth, making them unfilterable, whereas, the higher order harmonics of the output of phase-locked-loop (PLL) based synthesizers can be filtered. The lookup table 4104 contains signal data for an integral number of cycles. The final output frequency fout can be changed changing the reference clock frequency fc or by reprogramming the PROM.


The DDS circuit 4100 may comprise multiple lookup tables 4104 where the lookup table 4104 stores a waveform represented by a predetermined number of samples, wherein the samples define a predetermined shape of the waveform. Thus multiple waveforms having a unique shape can be stored in multiple lookup tables 4104 to provide different tissue treatments based on instrument settings or tissue feedback. Examples of waveforms include high crest factor RF electrical signal waveforms for surface tissue coagulation, low crest factor RF electrical signal waveform for deeper tissue penetration, and electrical signal waveforms that promote efficient touch-up coagulation. In one aspect, the DDS circuit 4100 can create multiple wave shape lookup tables 4104 and during a tissue treatment procedure (e.g., “on-the-fly” or in virtual real time based on user or sensor inputs) switch between different wave shapes stored in separate lookup tables 4104 based on the tissue effect desired and/or tissue feedback. Accordingly, switching between wave shapes can be based on tissue impedance and other factors, for example. In other aspects, the lookup tables 4104 can store electrical signal waveforms shaped to maximize the power delivered into the tissue per cycle (i.e., trapezoidal or square wave). In other aspects, the lookup tables 4104 can store wave shapes synchronized in such way that they make maximizing power delivery by the multifunction surgical instrument of surgical system 1000 while delivering RF and ultrasonic drive signals. In yet other aspects, the lookup tables 4104 can store electrical signal waveforms to drive ultrasonic and RF therapeutic, and/or sub-therapeutic, energy simultaneously while maintaining ultrasonic frequency lock. Custom wave shapes specific to different instruments and their tissue effects can be stored in the non-volatile memory of the generator circuit or in the non-volatile memory (e.g., EEPROM) of the surgical system 1000 and be fetched upon connecting the multifunction surgical instrument to the generator circuit. An example of an exponentially damped sinusoid, as used in many high crest factor “coagulation” waveforms is shown in FIG. 15.


A more flexible and efficient implementation of the DDS circuit 4100 employs a digital circuit called a Numerically Controlled Oscillator (NCO). A block diagram of a more flexible and efficient digital synthesis circuit such as a DDS circuit 4200 is shown in FIG. 14. In this simplified block diagram, a DDS circuit 4200 is coupled to a processor, controller, or a logic device of the generator and to a memory circuit located either in the generator or in any of the surgical instruments of surgical system 1000. The DDS circuit 4200 comprises a load register 4202, a parallel delta phase register 4204, an adder circuit 4216, a phase register 4208, a lookup table 4210 (phase-to-amplitude converter), a DAC circuit 4212, and a filter 4214. The adder circuit 4216 and the phase register 4208 form part of a phase accumulator 4206. A clock frequency fc is applied to the phase register 4208 and a DAC circuit 4212. The load register 4202 receives a tuning word that specifies output frequency as a fraction of the reference clock frequency signal fc. The output of the load register 4202 is provided to the parallel delta phase register 4204 with a tuning word M.


The DDS circuit 4200 includes a sample clock that generates the clock frequency fc, the phase accumulator 4206, and the lookup table 4210 (e.g., phase to amplitude converter). The content of the phase accumulator 4206 is updated once per clock cycle fc. When time the phase accumulator 4206 is updated, the digital number, M, stored in the parallel delta phase register 4204 is added to the number in the phase register 4208 by the adder circuit 4216. Assuming that the number in the parallel delta phase register 4204 is 00 . . . 01 and that the initial contents of the phase accumulator 4206 is 00 . . . 00. The phase accumulator 4206 is updated by 00 . . . 01 per clock cycle. If the phase accumulator 4206 is 32-bits wide, 232 clock cycles (over 4 billion) are required before the phase accumulator 4206 returns to 00 . . . 00, and the cycle repeats.


A truncated output 4218 of the phase accumulator 4206 is provided to a phase-to amplitude converter lookup table 4210 and the output of the lookup table 4210 is coupled to a DAC circuit 4212. The truncated output 4218 of the phase accumulator 4206 serves as the address to a sine (or cosine) lookup table. An address in the lookup table corresponds to a phase point on the sinewave from 0° to 360°. The lookup table 4210 contains the corresponding digital amplitude information for one complete cycle of a sinewave. The lookup table 4210 therefore maps the phase information from the phase accumulator 4206 into a digital amplitude word, which in turn drives the DAC circuit 4212. The output of the DAC circuit is a first analog signal 4220 and is filtered by a filter 4214. The output of the filter 4214 is a second analog signal 4222, which is provided to a power amplifier coupled to the output of the generator circuit.


In one aspect, the electrical signal waveform may be digitized into 1024 (210) phase points, although the wave shape may be digitized is any suitable number of 2n phase points ranging from 256 (28) to 281,474,976,710,656 (248), where n is a positive integer, as shown in TABLE 1. The electrical signal waveform may be expressed as Ann), where a normalized amplitude An at a point n is represented by a phase angle θn is referred to as a phase point at point n. The number of discrete phase points n determines the tuning resolution of the DDS circuit 4200 (as well as the DDS circuit 4100 shown in FIG. 13).


TABLE 1 specifies the electrical signal waveform digitized into a number of phase points.












TABLE 1







N
Number of Phase Points 2n



















8
256



10
1,024



12
4,096



14
16,384



16
65,536



18
262,144



20
1,048,576



22
4,194,304



24
16,777,216



26
67,108,864



28
268,435,456



32
4,294,967,296



48
281,474,976,710,656










The generator circuit algorithms and digital control circuits scan the addresses in the lookup table 4210, which in turn provides varying digital input values to the DAC circuit 4212 that feeds the filter 4214 and the power amplifier. The addresses may be scanned according to a frequency of interest. Using the lookup table enables generating various types of shapes that can be converted into an analog output signal by the DAC circuit 4212, filtered by the filter 4214, amplified by the power amplifier coupled to the output of the generator circuit, and fed to the tissue in the form of RF energy or fed to an ultrasonic transducer and applied to the tissue in the form of ultrasonic vibrations which deliver energy to the tissue in the form of heat. The output of the amplifier can be applied to an RF electrode, multiple RF electrodes simultaneously, an ultrasonic transducer, multiple ultrasonic transducers simultaneously, or a combination of RF and ultrasonic transducers, for example. Furthermore, multiple wave shape tables can be created, stored, and applied to tissue from a generator circuit.


With reference back to FIG. 13, for n=32, and M=1, the phase accumulator 4206 steps through 232 possible outputs before it overflows and restarts. The corresponding output wave frequency is equal to the input clock frequency divided by 232. If M=2, then the phase register 1708 “rolls over” twice as fast, and the output frequency is doubled. This can be generalized as follows.


For a phase accumulator 4206 configured to accumulate n-bits (n generally ranges from 24 to 32 in most DDS systems, but as previously discussed n may be selected from a wide range of options), there are 2n possible phase points. The digital word in the delta phase register, M, represents the amount the phase accumulator is incremented per clock cycle. If fc is the clock frequency, then the frequency of the output sinewave is equal to:







f
0

=


M
·

f
c



2
n






The above equation is known as the DDS “tuning equation.” Note that the frequency resolution of the system is equal to








f
o


2
n


.





For n=32, the resolution is greater than one part in four billion. In one aspect of the DDS circuit 4200, not all of the bits out of the phase accumulator 4206 are passed on to the lookup table 4210, but are truncated, leaving only the first 13 to 15 most significant bits (MSBs), for example. This reduces the size of the lookup table 4210 and does not affect the frequency resolution. The phase truncation only adds a small but acceptable amount of phase noise to the final output.


The electrical signal waveform may be characterized by a current, voltage, or power at a predetermined frequency. Further, where any one of the surgical instruments of surgical system 1000 comprises ultrasonic components, the electrical signal waveform may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, the generator circuit may be configured to provide an electrical signal waveform to at least one surgical instrument wherein the electrical signal waveform is characterized by a predetermined wave shape stored in the lookup table 4210 (or lookup table 4104FIG. 13). Further, the electrical signal waveform may be a combination of two or more wave shapes. The lookup table 4210 may comprise information associated with a plurality of wave shapes. In one aspect or example, the lookup table 4210 may be generated by the DDS circuit 4200 and may be referred to as a direct digital synthesis table. DDS works by first storing a large repetitive waveform in onboard memory. A cycle of a waveform (sine, triangle, square, arbitrary) can be represented by a predetermined number of phase points as shown in TABLE 1 and stored into memory. Once the waveform is stored into memory, it can be generated at very precise frequencies. The direct digital synthesis table may be stored in a non-volatile memory of the generator circuit and/or may be implemented with a FPGA circuit in the generator circuit. The lookup table 4210 may be addressed by any suitable technique that is convenient for categorizing wave shapes. According to one aspect, the lookup table 4210 is addressed according to a frequency of the electrical signal waveform. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in a memory or as part of the lookup table 4210.


In one aspect, the generator circuit may be configured to provide electrical signal waveforms to at least two surgical instruments simultaneously. The generator circuit also may be configured to provide the electrical signal waveform, which may be characterized two or more wave shapes, via an output channel of the generator circuit to the two surgical instruments simultaneously. For example, in one aspect the electrical signal waveform comprises a first electrical signal to drive an ultrasonic transducer (e.g., ultrasonic drive signal), a second RF drive signal, and/or a combination thereof. In addition, an electrical signal waveform may comprise a plurality of ultrasonic drive signals, a plurality of RF drive signals, and/or a combination of a plurality of ultrasonic and RF drive signals.


In addition, a method of operating the generator circuit according to the present disclosure comprises generating an electrical signal waveform and providing the generated electrical signal waveform to any one of the surgical instruments of surgical system 1000, where generating the electrical signal waveform comprises receiving information associated with the electrical signal waveform from a memory. The generated electrical signal waveform comprises at least one wave shape. Furthermore, providing the generated electrical signal waveform to the at least one surgical instrument comprises providing the electrical signal waveform to at least two surgical instruments simultaneously.


The generator circuit as described herein may allow for the generation of various types of direct digital synthesis tables. Examples of wave shapes for RF/Electrosurgery signals suitable for treating a variety of tissue generated by the generator circuit include RF signals with a high crest factor (which may be used for surface coagulation in RF mode), a low crest factor RF signals (which may be used for deeper tissue penetration), and waveforms that promote efficient touch-up coagulation. The generator circuit also may generate multiple wave shapes employing a direct digital synthesis lookup table 4210 and, on the fly, can switch between particular wave shapes based on the desired tissue effect. Switching may be based on tissue impedance and/or other factors.


In addition to traditional sine/cosine wave shapes, the generator circuit may be configured to generate wave shape(s) that maximize the power into tissue per cycle (i.e., trapezoidal or square wave). The generator circuit may provide wave shape(s) that are synchronized to maximize the power delivered to the load when driving RF and ultrasonic signals simultaneously and to maintain ultrasonic frequency lock, provided that the generator circuit includes a circuit topology that enables simultaneously driving RF and ultrasonic signals. Further, custom wave shapes specific to instruments and their tissue effects can be stored in a non-volatile memory (NVM) or an instrument EEPROM and can be fetched upon connecting any one of the surgical instruments of surgical system 1000 to the generator circuit.


The DDS circuit 4200 may comprise multiple lookup tables 4104 where the lookup table 4210 stores a waveform represented by a predetermined number of phase points (also may be referred to as samples), wherein the phase points define a predetermined shape of the waveform. Thus multiple waveforms having a unique shape can be stored in multiple lookup tables 4210 to provide different tissue treatments based on instrument settings or tissue feedback. Examples of waveforms include high crest factor RF electrical signal waveforms for surface tissue coagulation, low crest factor RF electrical signal waveform for deeper tissue penetration, and electrical signal waveforms that promote efficient touch-up coagulation. In one aspect, the DDS circuit 4200 can create multiple wave shape lookup tables 4210 and during a tissue treatment procedure (e.g., “on-the-fly” or in virtual real time based on user or sensor inputs) switch between different wave shapes stored in different lookup tables 4210 based on the tissue effect desired and/or tissue feedback. Accordingly, switching between wave shapes can be based on tissue impedance and other factors, for example. In other aspects, the lookup tables 4210 can store electrical signal waveforms shaped to maximize the power delivered into the tissue per cycle (i.e., trapezoidal or square wave). In other aspects, the lookup tables 4210 can store wave shapes synchronized in such way that they make maximizing power delivery by any one of the surgical instruments of surgical system 1000 when delivering RF and ultrasonic drive signals. In yet other aspects, the lookup tables 4210 can store electrical signal waveforms to drive ultrasonic and RF therapeutic, and/or sub-therapeutic, energy simultaneously while maintaining ultrasonic frequency lock. Generally, the output wave shape may be in the form of a sine wave, cosine wave, pulse wave, square wave, and the like. Nevertheless, the more complex and custom wave shapes specific to different instruments and their tissue effects can be stored in the non-volatile memory of the generator circuit or in the non-volatile memory (e.g., EEPROM) of the surgical instrument and be fetched upon connecting the surgical instrument to the generator circuit. One example of a custom wave shape is an exponentially damped sinusoid as used in many high crest factor “coagulation” waveforms, as shown in FIG. 43.



FIG. 15 illustrates one cycle of a discrete time digital electrical signal waveform 4300, in accordance with at least one aspect of the present disclosure of an analog waveform 4304 (shown superimposed over the discrete time digital electrical signal waveform 4300 for comparison purposes). The horizontal axis represents Time (t) and the vertical axis represents digital phase points. The digital electrical signal waveform 4300 is a digital discrete time version of the desired analog waveform 4304, for example. The digital electrical signal waveform 4300 is generated by storing an amplitude phase point 4302 that represents the amplitude per clock cycle Tclk over one cycle or period T0. The digital electrical signal waveform 4300 is generated over one period T0 by any suitable digital processing circuit. The amplitude phase points are digital words stored in a memory circuit. In the example illustrated in FIGS. 13, 14, the digital word is a six-bit word that is capable of storing the amplitude phase points with a resolution of 26 or 64 bits. It will be appreciated that the examples shown in FIGS. 13, 14 is for illustrative purposes and in actual implementations the resolution can be much higher. The digital amplitude phase points 4302 over one cycle T0 are stored in the memory as a string of string words in a lookup table 4104, 4210 as described in connection with FIGS. 13, 14, for example. To generate the analog version of the analog waveform 4304, the amplitude phase points 4302 are read sequentially from the memory from 0 to T0 per clock cycle Tclk and are converted by a DAC circuit 4108, 4212, also described in connection with FIGS. 13, 14. Additional cycles can be generated by repeatedly reading the amplitude phase points 4302 of the digital electrical signal waveform 4300 the from 0 to T0 for as many cycles or periods as may be desired. The smooth analog version of the analog waveform 4304 is achieved by filtering the output of the DAC circuit 4108, 4212 by a filter 4112, 4214 (FIGS. 13 and 14). The filtered analog output signal 4114, 4222 (FIGS. 13 and 14) is applied to the input of a power amplifier.



FIG. 16 is a diagram of a control system 12950 that may be implemented as a nested PID feedback controller. A PID controller is a control loop feedback mechanism (controller) to continuously calculate an error value as the difference between a desired set point and a measured process variable and applies a correction based on proportional, integral, and derivative terms (sometimes denoted P, I, and D respectively). The nested PID controller feedback control system 12950 includes a primary controller 12952, in a primary (outer) feedback loop 12954 and a secondary controller 12955 in a secondary (inner) feedback loop 12956. The primary controller 12952 may be a PID controller 12972 as shown in FIG. 17, and the secondary controller 12955 also may be a PID controller 12972 as shown in FIG. 17. The primary controller 12952 controls a primary process 12958 and the secondary controller 12955 controls a secondary process 12960. The output 12966 of the primary process 12958 is subtracted from a primary set point SP1 by a first summer 12962. The first summer 12962 produces a single sum output signal which is applied to the primary controller 12952. The output of the primary controller 12952 is the secondary set point SP2. The output 12968 of the secondary process 12960 is subtracted from the secondary set point SP2 by a second summer 12964.



FIG. 17 illustrates a PID feedback control system 12970 according to one aspect of this disclosure. The primary controller 12952 or the secondary controller 12955, or both, may be implemented as a PID controller 12972. In one aspect, the PID controller 12972 may comprise a proportional element 12974 (P), an integral element 12976 (I), and a derivative element 12978 (D). The outputs of the P, I, D elements 12974, 12976, 12978 are summed by a summer 12986, which provides the control variable μ(t) to the process 12980. The output of the process 12980 is the process variable y(t). A summer 12984 calculates the difference between a desired set point r(t) and a measured process variable y(t), received by feedback loop 12982. The PID controller 12972 continuously calculates an error value e(t) (e.g., difference between closure force threshold and measured closure force) as the difference between a desired set point r(t) (e.g., closure force threshold) and a measured process variable y(t) (e.g., velocity and direction of closure tube) and applies a correction based on the proportional, integral, and derivative terms calculated by the proportional element 12974 (P), integral element 12976 (I), and derivative element 12978 (D), respectively. The PID controller 12972 attempts to minimize the error e(t) over time by adjustment of the control variable μ(t) (e.g., velocity and direction of the closure tube).


In accordance with the PID algorithm, the “P” element 12974 accounts for present values of the error. For example, if the error is large and positive, the control output will also be large and positive. In accordance with the present disclosure, the error term e(t) is the different between the desired closure force and the measured closure force of the closure tube. The “I” element 12976 accounts for past values of the error. For example, if the current output is not sufficiently strong, the integral of the error will accumulate over time, and the controller will respond by applying a stronger action. The “D” element 12978 accounts for possible future trends of the error, based on its current rate of change. For example, continuing the P example above, when the large positive control output succeeds in bringing the error closer to zero, it also puts the process on a path to large negative error in the near future. In this case, the derivative turns negative and the D module reduces the strength of the action to prevent this overshoot.


It will be appreciated that other variables and set points may be monitored and controlled in accordance with the feedback control systems 12950, 12970. For example, the adaptive closure member velocity control algorithm described herein may measure at least two of the following parameters: firing member stroke location, firing member load, displacement of cutting element, velocity of cutting element, closure tube stroke location, closure tube load, among others.



FIG. 18 is an alternative system 132000 for controlling the frequency of an ultrasonic electromechanical system 132002 and detecting the impedance thereof, in accordance with at least one aspect of the present disclosure. The system 132000 may be incorporated into a generator. A processor 132004 coupled to a memory 132026 programs a programmable counter 132006 to tune to the output frequency f0 of the ultrasonic electromechanical system 132002. The input frequency is generated by a crystal oscillator 132008 and is input into a fixed counter 132010 to scale the frequency to a suitable value. The outputs of the fixed counter 132010 and the programmable counter 132006 are applied to a phase/frequency detector 132012. The output of the phase/frequency detector 132012 is applied to an amplifier/active filter circuit 132014 to generate a tuning voltage Vt that is applied to a voltage controlled oscillator 132016 (VCO). The VCO 132016 applies the output frequency f0 to an ultrasonic transducer portion of the ultrasonic electromechanical system 132002, shown here modeled as an equivalent electrical circuit. The voltage and current signals applied to the ultrasonic transducer are monitored by a voltage sensor 132018 and a current sensor 132020.


The outputs of the voltage and current sensors 132018, 132020 are applied to another phase/frequency detector 132022 to determine the phase angle between the voltage and current as measured by the voltage and current sensors 132018, 132020. The output of the phase/frequency detector 132022 is applied to one channel of a high speed analog to digital converter 132024 (ADC) and is provided to the processor 132004 therethrough. Optionally, the outputs of the voltage and current sensors 132018, 132020 may be applied to respective channels of the two-channel ADC 132024 and provided to the processor 132004 for zero crossing, FFT, or other algorithm described herein for determining the phase angle between the voltage and current signals applied to the ultrasonic electromechanical system 132002.


Optionally the tuning voltage Vt, which is proportional to the output frequency f0, may be fed back to the processor 132004 via the ADC 132024. This provides the processor 132004 with a feedback signal proportional to the output frequency f0 and can use this feedback to adjust and control the output frequency f0.


Estimating the State of the Jaw (Pad Burn Through, Staples, Broken Blade, Bone in Jaw, Tissue in Jaw)

A challenge with ultrasonic energy delivery is that ultrasonic acoustics applied on the wrong materials or the wrong tissue can result in device failure, for example, clamp arm pad burn through or ultrasonic blade breakage. It is also desirable to detect what is located in the jaws of an end effector of an ultrasonic device and the state of the jaws without adding additional sensors in the jaws. Locating sensors in the jaws of an ultrasonic end effector poses reliability, cost, and complexity challenges.


Ultrasonic spectroscopy smart blade algorithm techniques may be employed for estimating the state of the jaw (clamp arm pad burn through, staples, broken blade, bone in jaw, tissue in jaw, back-cutting with jaw closed, etc.) based on the impedance








Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)








of an ultrasonic transducer configured to drive an ultrasonic transducer blade, in accordance with at least one aspect of the present disclosure. The impedance Zg(t), magnitude |Z|, and phase φ are plotted as a function of frequency f.


Dynamic mechanical analysis (DMA), also known as dynamic mechanical spectroscopy or simply mechanical spectroscopy, is a technique used to study and characterize materials. A sinusoidal stress is applied to a material, and the strain in the material is measured, allowing the determination of the complex modulus of the material. The spectroscopy as applied to ultrasonic devices includes exciting the tip of the ultrasonic blade with a sweep of frequencies (compound signals or traditional frequency sweeps) and measuring the resulting complex impedance at each frequency. The complex impedance measurements of the ultrasonic transducer across a range of frequencies are used in a classifier or model to infer the characteristics of the ultrasonic end effector. In one aspect, the present disclosure provides a technique for determining the state of an ultrasonic end effector (clamp arm, jaw) to drive automation in the ultrasonic device (such as disabling power to protect the device, executing adaptive algorithms, retrieving information, identifying tissue, etc.).



FIG. 19 is a spectra 132030 of an ultrasonic device with a variety of different states and conditions of the end effector where the impedance Zg(t), magnitude |Z|, and phase φ are plotted as a function of frequency f, in accordance with at least one aspect of the present disclosure. The spectra 132030 is plotted in three-dimensional space where frequency (Hz) is plotted along the x-axis, phase (Rad) is plotted along the y-axis, and magnitude (Ohms) is plotted along the z-axis.


Spectral analysis of different jaw bites and device states produces different complex impedance characteristic patterns (fingerprints) across a range of frequencies for different conditions and states. Each state or condition has a different characteristic pattern in 3D space when plotted. These characteristic patterns can be used to estimate the condition and state of the end effector. FIG. 19 shows the spectra for air 132032, clamp arm pad 132034, chamois 132036, staple 132038, and broken blade 132040. The chamois 132036 may be used to characterize different types of tissue.


The spectra 132030 can be evaluated by applying a low-power electrical signal across the ultrasonic transducer to produce a non-therapeutic excitation of the ultrasonic blade. The low-power electrical signal can be applied in the form of a sweep or a compound Fourier series to measure the impedance








Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)








across the ultrasonic transducer at a range of frequencies in series (sweep) or in parallel (compound signal) using an FFT.


Methods of Classification of New Data

For each characteristic pattern, a parametric line can be fit to the data used for training using a polynomial, a Fourier series, or any other form of parametric equation as may be dictated by convenience. A new data point is then received and is classified by using the Euclidean perpendicular distance from the new data point to the trajectory that has been fitted to the characteristic pattern training data. The perpendicular distance of the new data point to each of the trajectories (each trajectory representing a different state or condition) is used to assign the point to a state or condition.


The probability distribution of distance of each point in the training data to the fitted curve can be used to estimate the probability of a correctly classified new data point. This essentially constructs a two-dimensional probability distribution in a plane perpendicular to the fitted trajectory at each new data point of the fitted trajectory. The new data point can then be included in the training set based on its probability of correct classification to make an adaptive, learning classifier that readily detects high-frequency changes in states but adapts to slow occurring deviations in system performance, such as a device getting dirty or the pad wearing out.



FIG. 20 is a graphical representation of a plot 132042 of a set of 3D training data set (S), where ultrasonic transducer impedance Zg(t), magnitude |Z|, and phase φ are plotted as a function of frequency f, in accordance with at least one aspect of the present disclosure. The 3D training data set (S) plot 132042 is graphically depicted in three-dimensional space where phase (Rad) is plotted along the x-axis, frequency (Hz) is plotted along the y-axis, magnitude (Ohms) is plotted along the z-axis, and a parametric Fourier series is fit to the 3D training data set (S). A methodology for classifying data is based on the 3D training data set (S0 is used to generate the plot 132042).


The parametric Fourier series fit to the 3D training data set (S) is defined by:







p


=



a


0

+




n
=
1





(




a


n


cos



n





π





t

L


+



b


n


sin



n





π





t

L



)







For a new point {right arrow over (z)}, the perpendicular distance from {right arrow over (p)} to {right arrow over (z)} is found by:






D
=




p


-

z











When


:










D



T


=
0






Then


:







D
=

D






A probability distribution of D can be used to estimate the probability of a data point {right arrow over (z)} belonging to the group S.


Control

Based on the classification of data measured before, during, or after activation of the ultrasonic transducer/ultrasonic blade, a variety of automated tasks and safety measures can be implemented. Similarly, the state of the tissue located in the end effector and temperature of the ultrasonic blade also can be inferred to some degree, and used to better inform the user of the state of the ultrasonic device or protect critical structures, etc. Temperature control of an ultrasonic blade is described in commonly owned U.S. Provisional Patent Application No. 62/640,417, filed Mar. 8, 2018, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, which is incorporated herein by reference in its entirety.


Similarly, power delivery can be reduced when there is a high probability that the ultrasonic blade is contacting the clamp arm pad (e.g., without tissue in between) or if there is a probability that the ultrasonic blade has broken or that the ultrasonic blade is touching metal (e.g., a staple). Furthermore, back-cutting can be disallowed if the jaw is closed and no tissue is detected between the ultrasonic blade and the clamp arm pad.


Integration of Other Data to Improve Classification

This system can be used in conjunction with other information provided by sensors, the user, metrics on the patient, environmental factors, etc., by combing the data from this process with the aforementioned data using probability functions and a Kalman filter. The Kalman filter determines the maximum likelihood of a state or condition occurring given a plethora of uncertain measurements of varying confidence. Since this method allows for an assignment of probability to a newly classified data point, this algorithm's information can be implemented with other measures or estimates in a Kalman filter.



FIG. 21 is a logic flow diagram 132044 depicting a control program or a logic configuration to determine jaw conditions based on the complex impedance characteristic pattern (fingerprint), in accordance with at least one aspect of the present disclosure. Prior to determining jaw conditions based on the complex impedance characteristic pattern (fingerprint), a database is populated with reference complex impedance characteristic patterns or a training data sets (S) that characterize various jaw conditions, including, without limitation, air 132032, clamp arm pad 132034, chamois 132036, staple 132038, broken blade 132040, as shown in FIG. 82, and a variety of tissue types and conditions. The chamois dry or wet, full byte or tip, may be used to characterize different types of tissue. The data points used to generate reference complex impedance characteristic patterns or a training data set (S) are obtained by applying a sub-therapeutic drive signal to the ultrasonic transducer, sweeping the driving frequency over a predetermined range of frequencies from below resonance to above resonance, measuring the complex impedance at each of the frequencies, and recording the data points. The data points are then fit to a curve using a variety of numerical methods including polynomial curve fit, Fourier series, and/or parametric equation. A parametric Fourier series fit to the reference complex impedance characteristic patterns or a training data set (S) is described herein.


Once the reference complex impedance characteristic patterns or a training data sets (S) are generated, the ultrasonic instrument measures new data points, classifies the new points, and determines whether the new data points should be added to the reference complex impedance characteristic patterns or a training data sets (S).


Turning now to the logic flow diagram of FIG. 21, in one aspect, the control circuit measures 132046 a complex impedance of an ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)




;





The control circuit receives 132048 a complex impedance measurement data point and compares 132050 the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern. The control circuit classifies 132052 the complex impedance measurement data point based on a result of the comparison analysis and assigns 132054 a state or condition of the end effector based on the result of the comparison analysis.


In one aspect, the control circuit receives the reference complex impedance characteristic pattern from a database or memory coupled to the processor. In one aspect, the control circuit generates the reference complex impedance characteristic pattern as follows. A drive circuit coupled to the control circuit applies a nontherapeutic drive signal to the ultrasonic transducer starting at an initial frequency, ending at a final frequency, and at a plurality of frequencies therebetween. The control circuit measures the impedance of the ultrasonic transducer at each frequency and stores a data point corresponding to each impedance measurement. The control circuit curve fits a plurality of data points to generate a three-dimensional curve of representative of the reference complex impedance characteristic pattern, wherein the magnitude |Z| and phase φ are plotted as a function of frequency f. The curve fitting includes a polynomial curve fit, a Fourier series, and/or a parametric equation.


In one aspect, the control circuit receives a new impedance measurement data point and classifies the new impedance measurement data point using a Euclidean perpendicular distance from the new impedance measurement data point to a trajectory that has been fitted to the reference complex impedance characteristic pattern. The control circuit estimates a probability that the new impedance measurement data point is correctly classified. The control circuit adds the new impedance measurement data point to the reference complex impedance characteristic pattern based on the probability of the estimated correct classification of the new impedance measurement data point. In one aspect, the control circuit classifies data based on a training data set (S), where the training data set (S) comprises a plurality of complex impedance measurement data, and curve fits the training data set (S) using a parametric Fourier series, wherein S is defined herein and wherein the probability distribution is used to estimate the probability of the new impedance measurement data point belonging to the group S.


State of Jaw Classifier Based on Model

There has been an existing interest in classifying matter located within the jaws of an ultrasonic device including tissue types and condition. In various aspects, it can be shown that with high data sampling and sophisticated pattern recognition this classification is possible. The approach is based on impedance as a function of frequency, where magnitude, phase, and frequency are plotted in 3D the patterns look like ribbons as shown in FIGS. 19 and 20 and the logic flow diagram of FIG. 21. This disclosure provides an alternative smart blade algorithm approach that is based on a well-established model for piezoelectric transducers.


By way of example, the equivalent electrical lumped parameter model is known to be an accurate model of the physical piezoelectric transducer. It is based on the Mittag-Leffler expansion of a tangent near a mechanical resonance. When the complex impedance or the complex admittance is plotted as an imaginary component versus a real component, circles are formed. FIG. 22 is a circle plot 132056 of complex impedance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure. FIG. 23 is a circle plot 132058 of complex admittance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure. The circles depicted in FIGS. 22 and 23 are taken from the IEEE 177 Standard, which is incorporated herein by reference in its entirety. Tables 1-4 are taken from the IEEE 177 Standard and disclosed herein for completeness.


The circle is created as the frequency is swept from below resonance to above resonance. Rather than stretching the circle out in 3D, a circle is identified and the radius (r) and offsets (a, b) of the circle are estimated. These values are then compared with established values for given conditions. These conditions may be: 1) open nothing in jaws, 2) tip bite 3) full bite and staple in jaws. If the sweep generates multiple resonances, circles of different characteristics will be present for each resonance. Each circle will be drawn out before the next if the resonances are separated. Rather than fitting a 3D curve with a series approximation, the data is fitted with a circle. The radius (r) and offsets (a, b) can be calculated using a processor programmed to execute a variety of mathematical or numerical techniques described below. These values may be estimated by capturing an image of a circle and, using image processing techniques, the radius (r) and offsets (a, b) that define the circle are estimated.



FIG. 24 is a circle plot 132060 of complex admittance for a 55.5 kHz ultrasonic piezoelectric transducer for lumped parameters inputs and outputs specified hereinbelow. Values for a lumped parameter model were used to generate the complex admittance. A moderate load was applied in the model. The obtained admittance circle generated in MathCad is shown in FIG. 24. The circle plot 132060 is formed when the frequency is swept from 54 to 58 kHz.


The lumped parameter input values are:

Co=3.0 nF
Cs=8.22 pF
Ls=1.0 H
Rs=450Ω


The outputs of the model based on the inputs are:






am
=




D
·
C

-

B
·
C




A
·
C

-

B
2



=

1.013
·

10
3









bm
=




A
·
E

-

B
·
D




A
·
C

-

B
2



=

-
954.585











rm
=




1
fpts



(



i
fpts





(



(


Zout

1
,
i


=
am

)

2

+


(


Zout

2
,
i


-
bm

)

2


)

2

2


)








=



1.012
·

10
3









The output values are used to plot the circle plot 132060 shown in FIG. 24. The circle plot 132060 has a radius (r) and the center 132062 is offset (a, b) from the origin 132064 as follows:

r=1.012*103
a=1.013*103
b=−954.585


The summations A-E specified below are needed to estimate the circle plot 132060 plot for the example given in FIG. 24, in accordance with at least one aspect of the present disclosure. Several algorithms exist to calculate a fit to a circle. A circle is defined by its radius (r) and offsets (a, b) of the center from the origin:

r2=(x−a)2+(y−b)2


The modified least squares method (Umbach and Jones) is convenient in that there a simple close formed solution for a, b, and r.







a
^

=



D





C

-
BE



A





C

-

B
2










b
^

=


AE
-
BD


AC
-

B
2










r
^

=


1
n






i
=
1

n






(


x
i

-

a
^


)

2

+


(


y
i

-

b
^


)

2









The caret symbol over the variable “a” indicates an estimate of the true value. A, B, C, D, and E are summations of various products which are calculated from the data. They are included herein for completeness as follows:











A
:=



fpts
·



i
fpts




(

Zout

1
,
i


)

2



-


(



i
fpts



(

Zout

1
,
i


)


)

2


=

5.463
·

10
10










B
:=



fpts




i
fpts



(


Zout

1
,
i


·

Zout

2
,
i



)



-

(


(



i
fpts



(

Zout

1
,
i


)


)

·

(



i
fpts



(

Zout

2
,
i


)


)


)


=

5.461
·

10
7














C
:=



fpts




i
fpts




(

Zout

2
,
i


)

2



-


(



i
fpts



(

Zout

2
,
i


)


)

2


=

5.445
·

10
10










D
:=


0.5
·

(


fpts




i
fpts



(


Zout

1
,
i


·


(

Zout

2
,
i


)

2


)



-


(



i
fpts



(

Zout

1
,
i


)


)

·

(



i
fpts




(

Zout

2
,
i


)

2


)


+

fpts




i
fpts



(

Zout

1
,
i

3

)



-


(



i
fpts



(

Zout

1
,
i


)


)

·

(



i
fpts




(

Zout

1
,
i


)

2


)



)


=

5.529
·

10
3









E
:=


0.5
·

(


fpts




i
fpts



(


Zout

2
,
i


·


(

Zout

1
,
i


)

2


)



-


(



i
fpts



(

Zout

2
,
i


)


)

·

(



i
fpts




(

Zout

1
,
i


)

2


)


+

fpts




i
fpts



(

Zout

2
,
i

3

)



-


(



i
fpts



(

Zout

2
,
i


)


)

·

(



i
fpts




(

Zout

2
,
i


)

2


)



)


=


-
5.129

·

10
13







Z1,i is a first vector of the real components referred to as conductance;


Z2,i is a second of the imaginary components referred to as susceptance; and


Z3,i is a third vector that represents the frequencies at which admittances are calculated.


This disclosure will work for ultrasonic systems and may possibly be applied to electrosurgical systems, even though electrosurgical systems do not rely on a resonance.



FIGS. 25-29 illustrate images taken from an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the end effector jaw in various open or closed configurations and loading. The circle plots in solid line depict impedance and the circle plots in broken lines depict admittance, in accordance with at least one aspect of the present disclosure. By way of example, the impedance/admittance circle plots are generated by connecting an ultrasonic device to an impedance analyzer. The display of the impedance analyzer is set to complex impedance and complex admittance, which can be selectable from the front panel of the impedance analyzer. An initial display may be obtained with the jaw of the ultrasonic end effector in an open position and the ultrasonic device in an unloaded state, as described below in connection with FIG. 25, for example. The autoscale display function of the impedance analyzer may be used to generate both the complex impedance and admittance circle plots. The same display is used for subsequent runs of the ultrasonic device with different loading conditions as shown in the subsequent FIGS. 25-29. A LabVIEW application may be employed to upload the data files. In another technique, the display images may be captured with a camera, such as a smartphone camera, like an iPhone or Android. As such, the image of the display may include some “keystone-ing” and in general may not appear to be parallel to the screen. Using this technique, the circle plot traces on the display will appear distorted in the captured image. With this approach, the material located in the jaws of the ultrasonic end effector can be classified.


The complex impedance and complex admittance are just the reciprocal of one another. No new information should be added by looking at both. Another consideration includes determining how sensitive the estimates are to noise when using complex impedance or complex admittance.


In the examples illustrated in FIGS. 25-29, the impedance analyzer is set up with a range to just capture the main resonance. By scanning over a wider range of frequencies more resonances may be encountered and multiple circle plots may be formed. An equivalent circuit of an ultrasonic transducer may be modeled by a first “motional” branch having a serially connected inductance Ls, resistance Rs and capacitance Cs that define the electromechanical properties of the resonator, and a second capacitive branch having a static capacitance C0. In the impedance/admittance plots shown in FIGS. 25-29 that follow, the values of the components of the equivalent circuit are:

Ls=L1=1.1068 H
Rs=R1=311.352Ω
Cs=C1=7.43265 pF
C0=C0=3.64026 nF


The oscillator voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div. Measurements of values that may characterize the impedance (Z) and admittance (Y) circle plots may be obtained at the locations on the circle plots as indicated by an impedance cursor and an admittance cursor.


State of Jaw: Open with No Loading


FIG. 25 is a graphical display 132066 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132068, 132070 for an ultrasonic device with the jaw open and no loading where a circle plot 132068 in solid line depicts complex impedance and a circle plot 132070 in broken line depicts complex admittance, in accordance with at least one aspect of the present disclosure. The oscillator voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 D/div and the admittance (Y) scale is 500 μS/div. Measurements of values that may characterize the complex impedance (Z) and admittance (Y) circle plots 132068, 132070 may be obtained at locations on the circle plots 132068, 132070 as indicated by the impedance cursor 132072 and the admittance cursor 132074. Thus, the impedance cursor 132072 is located at a portion of the impedance circle plot 132068 that is equivalent to about 55.55 kHz, and the admittance cursor 132074 is located at a portion of the admittance circle plot 132070 that is equivalent to about 55.29 kHz. As depicted in FIG. 25, the position of the impedance cursor 132072 corresponds to values of:

R=1.66026Ω
X=−697.309Ω


Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the position of the admittance cursor 132074 corresponds to values of:

G=64.0322 μS
B=1.63007 mS


Where G is the conductance (real value) and B is susceptance (imaginary value).


State of Jaw: Clamped on Dry Chamois


FIG. 26 is a graphical display 132076 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132078, 132080 for an ultrasonic device with the jaw of the end effector clamped on dry chamois where the impedance circle plot 132078 is shown in solid line and the admittance circle plot 132080 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 D/div and the admittance (Y) scale is 500 μS/div.


Measurements of values that may characterize the complex impedance (Z) and admittance (Y) circle pots 132078, 132080 may be obtained at locations on the circle plots 132078, 132080 as indicated by the impedance cursor 132082 and the admittance cursor 132084. Thus, the impedance cursor 132082 is located at a portion of the impedance circle plot 132078 that is equivalent to about 55.68 kHz, and the admittance cursor 132084 is located at a portion of the admittance circle plot 132080 that is equivalent to about 55.29 kHz. As depicted in FIG. 26, the position of the impedance cursor 132082 corresponds to values of:

R=434.577Ω
X=−758.772Ω


Where R is the resistance (real value) and X is the reactance (imaginary value).


Similarly, the position of the admittance cursor 132084 corresponds to values of:

G=85.1712 μS
B=1.49569 mS


Where G is the conductance (real value) and B is susceptance (imaginary value).


State of Jaw: Tip Clamped on Moist Chamois


FIG. 27 is a graphical display 132086 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132098, 132090 for an ultrasonic device with the jaw tip clamped on moist chamois where the impedance circle plot 132088 is shown in solid line and the admittance circle plot 132090 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div.


Measurements of values that may characterize the complex impedance (Z) and complex admittance (Y) circle plots 132088, 132090 may be obtained at locations on the circle plots 132088, 132090 as indicated by the impedance cursor 132092 and the admittance cursor 132094. Thus, the impedance cursor 132092 is located at a portion of the impedance circle plot 132088 that is equivalent to about 55.68 kHz, and the admittance cursor 132094 is located at a portion of the admittance circle plot 132090 that is equivalent to about 55.29 kHz. As depicted in FIG. 28, the impedance cursor 132092 corresponds to values of:

R=445.259Ω
X=−750.082Ω


Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the admittance cursor 132094 corresponds to values of:

G=96.2179 μS
B=1.50236 mS


Where G is the conductance (real value) and B is susceptance (imaginary value).


State of Jaw: Fully Clamped on Moist Chamois


FIG. 28 is a graphical display 132096 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132098, 132100 for an ultrasonic device with the jaw fully clamped on moist chamois where the impedance circle plot 132098 is shown in solid line and the admittance circle plot 132100 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div.


Measurements of values that may characterize the impedance and admittance circle plots 132098, 132100 may be obtained at locations on the circle plots 132098, 1332100 as indicated by the impedance cursor 13212 and admittance cursor 132104. Thus, the impedance cursor 132102 is located at a portion of the impedance circle plot 132098 equivalent to about 55.63 kHz, and the admittance cursor 132104 is located at a portion of the admittance circle plot 132100 equivalent to about 55.29 kHz. As depicted in FIG. 28, the impedance cursor 132102 corresponds to values of R, the resistance (real value, not shown), and X, the reactance (imaginary value, also not shown).


Similarly, the admittance cursor 132104 corresponds to values of:

G=137.272 μS
B=1.48481 mS


Where G is the conductance (real value) and B is susceptance (imaginary value).


State of Jaw: Open with No Loading


FIG. 29 is a graphical display 132106 of an impedance analyzer showing impedance (Z)/admittance (Y) circle plots where frequency is swept from 48 kHz to 62 kHz to capture multiple resonances of an ultrasonic device with the jaw open and no loading where the area designated by the rectangle 132108 shown in broken line is to help see the impedance circle plots 132110a, 132110b, 132110c shown in solid line and the admittance circle plots 132112a, 132112b, 132112c, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 48 kHz to 62 kHz. The impedance (Z) scale is 500 Ω/div and the admittance (Y) scale is 500 μS/div.


Measurements of values that may characterize the impedance and admittance circle plots 132110a-c, 132112a-c may be obtained at locations on the impedance and admittance circle plots 132110a-c, 132112a-c as indicated by the impedance cursor 132114 and the admittance cursor 132116. Thus, the impedance cursor 132114 is located at a portion of the impedance circle plots 132110a-c equivalent to about 55.52 kHz, and the admittance cursor 132116 is located at a portion of the admittance circle plot 132112a-c equivalent to about 59.55 kHz. As depicted in FIG. 29, the impedance cursor 132114 corresponds to values of:

R=1.86163 kΩ
X=−536.229Ω


Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the admittance cursor 132116 corresponds to values of:

G=649.956 μS
B=2.51975 mS


Where G is the conductance (real value) and B is susceptance (imaginary value).


Because there are only 400 samples across the sweep range of the impedance analyzer, there are only a few points about a resonance. So, the circle on the right side becomes choppy. But this is only due to the impedance analyzer and the settings used to cover multiple resonances.


When multiple resonances are present, there is more information to improve the classifier. The circle plots 132110a-c, 132112a-c fit can be calculated for each as encountered to keep the algorithm running fast. So once there is a cross of the complex admittance, which implies a circle, during the sweep, a fit can be calculated.


Benefits include in-the-jaw classifier based on data and a well-known model for ultrasonic systems. Count and characterizations of circles are well known in vision systems. So data processing is readily available. For example, a closed form solution exists to calculate the radius and axes' offsets for a circle. This technique can be relatively fast.


TABLE 2 is a list of symbols used for lumped parameter model of a piezoelectric transducer (from IEEE 177 Standard).












TABLE 2










References












Symbols
Meaning
SI Units
Equations
Tables
Figures





Bp
Equivalent parallel
mho

2




susceptance of vibrator






Co
Shunt (parallel) capacitance
farad
2, 3, 4, 8
5
1, 4



in the equivalent electric







circuit






C1
Motional capacitance in the
farad
2, 3, 4, 6,
5
1, 4



equivalent electric circuit

8, 9




f
Frequency
hertz


3


fa
Antiresonance frequency,
hertz

2, 4
2, 3



zero susceptance






fm
Frequency of maximum
hertz

2, 4
2, 3



admittance (minimum







impedance)






fn
Frequency of minimum
hertz

2, 4
2, 3



admittance (maximum







impedance)






fp
Parallel resonance frequency
hertz
2, 3
2, 4
2








(
lossless
)

=

1

2

π




L
1





C
1



C
O




C
1

+

C
O




















fr
Resonance frequency, zero
hertz

2, 4
2, 3



substance






fB
Motional (series) resonance
hertz
2, 3, 6, 7,
2, 4
2, 3, 6, 8







frequency






1
2






9, 11a, 11b, 11c, 12,







Gp
Equivalent parallel

1





conductance of vibrator






L1
Motional inductance in the
henry
8, 9
1, 4, 5




equivalent electric circuit






M
Figure of merit of a
dimensionless
10, 11a, 11b
3, 4, 5








vibrator
=

Q
r



















M
=

1


ω
s



C
O



R
1
















Q
Quality factor Q =
dimensionless
12
3
6, 8










ω
s



L
1



R
1


=


1


ω
s



C
1



R
1



=
rM














r





Capacitance





ratio





r

=


C
o


C
1






dimensionless
2, 3, 10, 11
2, 3, 4, 5
8





Ra
Impedance at zero phase
ohm


2, 3



angle near antiresonance






Re
Equivalent series resistance
ohm


1, 2



of vibrator






Rr
Impedance at fr zero phase
ohm


2, 3



angle






R1
Motional resistance in the
ohm
4, 8, 10, 11a
2, 5
1, 3, 4,



equivalent electric circuit

11b, 11c, 12

6, 7, 8


Xe
Equivalent series reactance of
ohm


1, 2



vibrator






Xo
Reactance of shunt (parallel)
ohm
1, 4, 5
5
3, 7



capacitance at series











resonance
=

1


ω
s



C
o
















X1
Reactance of motional
ohm

2
2



(series) arm of vibratorX1 =












L
1


ω


-

1

C
1


ω
















Y
Admittance of vibrator
mho
1









Y
=



G

p






+

B
p


j



=

1
z















Ym
Maximum admittance of
mho


3



vibrator






Yn
Minimum admittance of
mho


3



vibrator






Z
Impedance of vibrator
ohm
1





Z = Re + jXe






Zm
Minimum impedance of
ohm


3



vibrator






Zn
Maximum impedance of
ohm


3



vibrator







Absolute value of impedance
ohm

2
2








of





vibrator





Z

=



R
e
2

+

X
e
2

















Absolute value of impedance
ohm


2



at fm (minimum impedance)







Absolute value of impedance
ohm


2



at fn (maximum impedance)






δ
Normalized damping
dimensionless
1
2




factor δ = ωCoR1






Ω
Normalized frequency
dimensionless
1
2









factor





Ω

=



f
2

-

f
s
2




f
p
2

-

f
s
2
















ω
Circular (angular) frequency
hertz

2




ω = 2πf






ωs
Circular frequency at motional
hertz






resonance







ωs = 2πfs









TABLE 3 is a list of symbols for the transmission network (from IEEE 177 Standard).












TABLE 3










References












Symbols
Meaning
SI Units
Equations
Tables
Figures





b
Normalized compensation
dimensionless
4, 10
5









factor





1

-

1

4


π
2



f
s
2



C
o



L
o
















B
Normalized admittance factor
dimensionless
10
5



C
Normalized admittance factor
dimensionless
10
5



CA-B
Stray capacitance between
farad






the terminals A-B (FIG. 4)






CL
Load capacitance
farad
6

4


CT
Shunt capacitance
farad
4, 10
5
4



terminating transmission







circuit






CL1
Load capacitance
farad
7




CL2
Load capacitance
farad
7




e2
Output voltage of
volt


4



transmission network






fmT
Frequency of maximum
hertz
10





transmission






FsL1
Motional resonance frequency
hertz
7





of combination of vibrator and







CL1






FsL2
Motional resonance frequency
hertz
7





of combination of vibrator and







CL2






i1
Input current to transmission
ampere


4



network






L0
Compensation inductance
henry


4



shunting vibrator






MT
Figure of merit of
dimensionless
4, 10
5




transmission network







termination =












1


2
π



f
s



C
T



R
T



=


X
T


R
T












RT
Shunt resistance termination
ohm
4, 11a, 11b,
5
4, 6, 7, 8



of transmission network

11c, 12




RsL2
Standard resistor
ohm
4, 5
5
7


S
Detector sensitivity smallest
dimensionless
12

6



detectable current change /







current






x
Normalized frequency
dimensionless
12










factor





x

=




f
2


f
s
2


-
1

=

Ω
r












XA-B
Reactance of stray
ohm






capacitance CA-B






XT
Reactance of CT at the
ohm
4
5




motional resonance












frequency






X
T


=

1


2
π



f
s



C
T













xmT
Normalized frequency factor
dimensionless

5




at the frequency of maximum







transmission






ΔCL
ΔCL = CL2 − CL1
farad
6, 7




Δf
Δf1 = fsL1 − fsL2
hertz
6, 7

6, 8


Δf1
Δf1 = fsL1 − fs
hertz
6, 7




Δf2
Δf1 = fsL2 − fs
hertz
6, 7





*Refers to real roots; complex roots to be disregarded.






TABLE 4 is a list of solutions for various characteristic frequencies (from IEEE 177 Standard).









TABLE 4







SOLUTIONS FOR THE VARIOUS CHARACTERISTIC


FREQUENCIES












Charac-







teristic


Constituent

57


Frequen-


Equation for

IEEE


cies
Meaning
Condition
Frequency
Root
14.S11





fm
Frequency of
=O
−2δ2 (Ω + r) −
lower*
fm



maximum

2Ωr(1 − Ω) −





admittance

Ω2 = 0





(minimum







impedance)






fa
Motional
X1 = O
Ω = 0

fa



(series)







resonance







frequency






fr
Resonance
Xe = Bp =
Ω(1 − Ω) −
lower 
fr



frequency
O
δ2 = 0




fa
Antiresonance
Xe = Bp =
Ω(1 − Ω) −
upper 
fa



frequency
O
δ2 = 0




fp
Parallel
| = ∞ |
Ω = 1

fp



resonance
R1 = O


fp



frequency







(lossless)






fn
Frequency of
=O
−2δ2(Ω + r) −
upper*
fn



minimum

2Ωr(1 − Ω) −





admittance

Ω2 = 0





(maximum







impedance)





*Refers to real roots; complex roots to be disregarded






TABLE 5 is a list of losses of three classes of piezoelectric materials.












TABLE 5





Type of Piezoelectric





Vibrator
Q = Mr
r
Qr/r min


















Piezoelectric Ceramics
 90-500
2-40  
200


Water-Soluble
  200-50,000
3-500 
80


Piezoelectric Crystals





Quartz
104-107
100-50,000
2000





Minimum Values for the Ratio Qr/r to be Expected for Various Types of Piezoelectric Vibrators






TABLE 6 illustrates jaw conditions, estimated parameters of a circle based on real time measurements of complex impedance/admittance, radius (re) and offsets (ae and be) of the circle represented by measured variables Re, Ge, Xe, Be, and parameters of a reference circle plots, as described in FIGS. 25-29, based on real time measurements of complex impedance/admittance, radius (rr) and offsets (ar, br) of the reference circle represented by reference variables Rref, Gref, Xref, Bref. These values are then compared with established values for given conditions. These conditions may be: 1) open with nothing in jaws, 2) tip bite 3) full bite and staple in jaws. The equivalent circuit of the ultrasonic transducer was modeled as follows and the frequency was swept from 55 kHz to 56 kHz:

Ls=L1=1.1068 H
Rs=R1=311.352Ω
Cs=C1=7.43265 pF and
C0=C0=3.64026 nF










TABLE 6








Reference Circle Plot











Reference Jaw Conditions
Rref (Ω)
Gref (μS)
Xref (Ω)
Bref (mS)














Jaw open and no loading
1.66026
64.0322
−697.309
1.63007


Jaw clamped on dry
434.577
85.1712
−758.772
1.49569


chamois






Jaw tip clamped on moist
445.259
96.2179
−750.082
1.50236


chamois






Jaw fully clamped on
137.272

1.48481



moist chamois













In use, the ultrasonic generator sweeps the frequency, records the measured variables, and determines estimates Re, Ge, Xe, Be. These estimates are then compared to reference variables Rref, Gref, Xref, Bref stored in memory (e.g., stored in a look-up table) and determines the jaw conditions. The reference jaw conditions shown in TABLE 6 are examples only. Additional or fewer reference jaw conditions may be classified and stored in memory. These variables can be used to estimate the radius and offsets of the impedance/admittance circle.



FIG. 30 is a logic flow diagram 132120 of a process depicting a control program or a logic configuration to determine jaw conditions based on estimates of the radius (r) and offsets (a, b) of an impedance/admittance circle, in accordance with at least one aspect of the present disclosure. Initially a data base or lookup table is populated with reference values based on reference jaw conditions as described in connection with FIGS. 25-29 and TABLE 6. A reference jaw condition is set and the frequency is swept from a value below resonance to a value above resonance. The reference values Rref, Gref, Xref, Bref that define the corresponding impedance/admittance circle plot are stored in a database or lookup table. During use, under control of a control program or logic configuration a control circuit of the generator or instrument causes the frequency to sweep 132122 from below resonance to above resonance. The control circuit measures and records 132124 (e.g., stores in memory) the variables Re, Ge, Xe, Be that define the corresponding impedance/admittance circle plot and compares 132126 them to the reference values Rref, Gref, Xref, Bref stored in the database or lookup table. The control circuit determines 132128, e.g., estimates, the end effector jaw conditions based on the results of the comparison.


Smart Blade and Power Pulsing

During surgery with an ultrasonic shears device the power delivered to the tissue is set at a predetermined level. That predetermined level is used to transect the tissue throughout the transection procedure. Certain tissues may seal better or cut better/faster if the power delivered varies throughout the transection procedure. A solution is needed to vary the power delivered to the tissue through the blade during the transection process. In various aspects, the tissue type and changes to the tissue during the transection process may be determined using the techniques described in FIGS. 19-21 under the heading ESTIMATING THE STATE OF THE JAW (PAD BURN THROUGH, STAPLES, BROKEN BLADE, BONE IN JAW, TISSUE IN JAW and/or FIGS. 22-30 under the heading STATE OF JAW CLASSIFIER BASED ON MODEL and/or techniques for estimating the temperature of the ultrasonic blade are described in related U.S. Provisional Patent Application No. 62/640,417, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, to Nott et al, which is incorporated herein by reference in its entirety.


One solution that provides better ultrasonic transection employs the impedance feedback of the ultrasonic blade. As previously discussed, the impedance of the ultrasonic blade is related to the impedance of the electromechanical ultrasonic system and may be determined by measuring the phase angle between the voltage and current signals applied to the ultrasonic transducer as described herein. This technique may be employed to measure the magnitude and phase of the impedance of the ultrasonic transducer. The impedance of the ultrasonic transducer may be employed to profile factors that may be influencing the ultrasonic blade during use (e.g., force, temperature, vibration, force over time, etc.). This information may be employed to affect the power delivered to the ultrasonic blade during the transection process.



FIG. 31 is a logic flow diagram 132170 of a process depicting a control program or a logic configuration to monitor the impedance of an ultrasonic transducer to profile an ultrasonic blade and deliver power to the ultrasonic blade on the profile according to one aspect of the resent disclosure. According to the process, a control circuit determines 132172 (e.g., measures) the impedance (Z) of the ultrasonic transducer during a tissue transection process. The control circuit analyzes and profiles 132174 the ultrasonic blade right after the tissue is fully clamped in jaw of the end effector of the ultrasonic device based on the determined 132172 impedance (Z). The control circuit adjusts 132176 a power output level based on the profile (e.g., high power for dense tissue low power for thin tissue) of the ultrasonic blade. The control circuit controls the generator to momentarily drive the ultrasonic transducer and the ultrasonic blade and then stops. The control circuit again determines 132172 the impedance (Z) of the ultrasonic blade and profiles 132174 the ultrasonic blade based on the determined 132172 impedance (Z). The control circuit controls the generator to adjust the output power level or keep it the same based on the profile of the ultrasonic blade. The control circuit again controls the generator to momentarily drive the ultrasonic transducer and the ultrasonic blade and then stops. The process repeats and determines 132172 the impedance (Z), profiles 132174 the ultrasonic blade, and adjusts 132176 the power level until the impedance profile detected is that of the clamp arm pad and then adjusts the power to prevent the clamp arm pad from melting.


The process discussed in connection with FIG. 31 allows the ultrasonic transducer power level to be adjusted on the fly as the tissue changes from being heated and cut. Accordingly, if the tissue is initially tough and then weakens or if different layers of tissue are encountered during the transection process, the power level can be optimally adjusted to match the profile of the ultrasonic blade. This method could eliminate the need for the user to set the power level. The ultrasonic device would adapt and choose the right power level based on current tissue conditions and transection process.


This technique provides intelligent control for power level setting based on tissue feedback. This technique may eliminate the need for power settings on the generator and may lead to faster transection times. In one aspect, in an ultrasonic transection medical device including a jaw with an ultrasonic blade, the impedance of the ultrasonically driven blade is used to profile the ultrasonic blade characteristics (force, heat, vibration, etc.) and that profile is used to influence the power output of the transducer during the transection process. Power may be pulsed on and off so that the tissue changes can be read for feedback in between pulses to adjust the power during the transection process.



FIGS. 32A-32D is a series of graphical representations of the impedance of an ultrasonic transducer to profile an ultrasonic blade and deliver power to the ultrasonic blade based on the profile, in accordance with at least one aspect of the present disclosure. FIG. 32A is a graphical representation 132180 of ultrasonic transducer impedance versus time. The generator control circuit reads the initial impedance Z1 which is based on the contents of the jaw and applies a pulsed power P1 to the ultrasonic transducer as shown in FIG. 32B, which is a graphical depiction 132182 of pulsed power versus time. FIG. 32C is a graphical representation 132184 of a new impedance Z2 versus time. The control circuit of the generator reads the new impedance Z2 and applies pulsed power P2 to the ultrasonic transducer to meet the new tissue condition as plotted in FIG. 32D, which is a graphical representation 132186 of pulsed power P2 versus time.


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.


Various aspects of the subject matter described herein are set out in the following numbered examples:


Example 1

A method of controlling energy delivered to an ultrasonic device, the ultrasonic device comprising an electromechanical ultrasonic system defined by a predetermined resonant frequency, the electromechanical ultrasonic system comprising an ultrasonic transducer coupled to an ultrasonic blade, the method comprising:


determining, by a processor or control circuit, an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process;


analyzing, by the processor or control circuit, the impedance of the ultrasonic transducer;


profiling, by the processor or control circuit, the ultrasonic blade based on the impedance of the ultrasonic transducer; and


adjusting, by the processor or control circuit, a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


Example 2

The method of Example 1, further comprising:


pulsing, by the processor or control circuit, the power delivered to the ultrasonic transducer;


determining, by the processor or control circuit, changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses; and


adjusting, by the processor or control circuit, power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


Example 3

The method of Example 2, wherein determining, by the processor or control circuit, changes in tissue characteristics comprises:


measuring, by the processor or control circuit, a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)




;




receiving, by the processor or control circuit, a complex impedance measurement data point;


comparing, by the processor or control circuit, the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern;


classifying, by the processor or control circuit, the complex impedance measurement data point based on a result of the comparison analysis; and


assigning, by the processor or control circuit, a state or condition of the end effector based on the result of the comparison analysis.


Example 4

The method of Example 3, wherein receiving, by the processor or control circuit, a complex impedance measurement data point comprises receiving, by the processor or control circuit, a complex impedance measurement data point corresponding to a clamp arm pad; and


adjusting, by the processor or control circuit, power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


Example 5

The method of Example 2, wherein determining, by the processor or control circuit, changes in tissue characteristics comprises:


applying, by a drive circuit, a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;


sweeping, by a processor or control circuit, the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system;


measuring and recording, by the processor or control circuit, impedance/admittance circle variables Re, Ge, Xe, and Be;


comparing, by the processor or control circuit, measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; and


determining, by the processor or control circuit, a state or condition of the end effector based on the result of the comparison analysis.


Example 6

The method of one or more of Examples 2 through 5, wherein determining, by the processor or control circuit, changes in tissue characteristics comprises determining, by the processor or control circuit, changes in a tissue thickness.


Example 7

An ultrasonic surgical instrument comprising:


an ultrasonic electromechanical system comprising an ultrasonic transducer coupled to an ultrasonic blade via an ultrasonic waveguide; and


a generator configured to supply power to the ultrasonic transducer, wherein the generator comprises a control circuit configured to:

    • determine an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process;
    • analyze the impedance of the ultrasonic transducer;
    • profile the ultrasonic blade based on the impedance of the ultrasonic transducer; and
    • adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


Example 8

The ultrasonic surgical instrument of Example 7, wherein the generator comprises a control circuit further configured to:


pulse the power delivered to the ultrasonic transducer;


determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses; and


adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


Example 9

The ultrasonic surgical instrument of Example 8, wherein the generator comprises a control circuit further configured to:


measure a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)




;




receive a complex impedance measurement data point;


compare the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern;


classify the complex impedance measurement data point based on a result of the comparison analysis; and


assign a state or condition of the end effector based on the result of the comparison analysis.


Example 10

The ultrasonic surgical instrument of Example 9, wherein the generator comprises a control circuit further configured to receive a complex impedance measurement data point corresponding to a clamp arm pad, and


adjust a power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


Example 11

The ultrasonic surgical instrument of any one or more of Examples 8 through 10, wherein the generator comprises a control circuit further configured to:


cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;


sweep the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system;


measure and record impedance/admittance circle variables Re, Ge, Xe, and Be;


compare measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; and


determine a state or condition of the end effector based on the result of the comparison analysis.


Example 12

The ultrasonic surgical instrument of any one or more of Examples 8 through 11, wherein the generator comprises a control circuit further configured to determine changes in a tissue thickness.


Example 13

A generator for an ultrasonic surgical instrument, the generator comprising:


a control circuit configured to:

    • determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process;
    • analyze the impedance of the ultrasonic transducer;
    • profile the ultrasonic blade based on the impedance of the ultrasonic transducer; and
    • adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


Example 14

The ultrasonic surgical instrument of Example 13, wherein the control circuit is further configured to:


pulse the power delivered to the ultrasonic transducer;


determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses; and


adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.


Example 15

The ultrasonic surgical instrument of Example 14, wherein the control circuit further is configured to:


measure a complex impedance of the ultrasonic transducer, wherein the complex impedance is defined as









Z
g



(
t
)


=



V
g



(
t
)




I
g



(
t
)




;




receive a complex impedance measurement data point;


compare the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern;


classify the complex impedance measurement data point based on a result of the comparison analysis; and


assign a state or condition of the end effector based on the result of the comparison analysis.


Example 16

The ultrasonic surgical instrument of Example 15, wherein the control circuit is further configured to receive a complex impedance measurement data point corresponding to a clamp arm pad, and


adjust a power delivered to the ultrasonic transducer to a power value to prevent the clamp arm pad from melting.


Example 17

The ultrasonic surgical instrument of any one or more of Examples 14 through 16, wherein the control circuit is further configured to:


cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;


sweep the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system;


measure and record impedance/admittance circle variables Re, Ge, Xe, and Be;


compare measured impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; and


determine a state or condition of the end effector based on the result of the comparison analysis.


Example 18

The ultrasonic surgical instrument of any one or more of Examples 14 through 17, wherein the control circuit is further configured to determine changes in a tissue thickness.


Example 19

An ultrasonic surgical system, comprising:


a processor and a non-transitory memory, wherein the non-transitory memory comprises instructions that, when executed by the processor, cause the processor to:

    • determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process;
    • analyze the impedance of the ultrasonic transducer;
    • profile the ultrasonic blade based on the impedance of the ultrasonic transducer; and
    • adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade.


Example 20

The ultrasonic surgical system of Example 19, wherein the non-transitory memory comprises instructions that, when executed by the processor, further cause the processor to:


pulse the power delivered to the ultrasonic transducer;


determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics is determined between pulses; and


adjust power delivered to the ultrasonic transducer based on the tissue changes throughout the transection.

Claims
  • 1. A method of controlling energy delivered to an ultrasonic device, the ultrasonic device comprising an electromechanical ultrasonic system defined by a predetermined resonant frequency, the electromechanical ultrasonic system comprising an ultrasonic transducer coupled to an ultrasonic blade, the method comprising: determining, by a processor or control circuit, an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process;analyzing, by the processor or control circuit, the impedance of the ultrasonic transducer;profiling, by the processor or control circuit, the ultrasonic blade based on the impedance of the ultrasonic transducer;adjusting, by the processor or control circuit, a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade;pulsing, by the processor or control circuit, the power delivered to the ultrasonic transducer;determining, by the processor or control circuit, changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics are determined between pulses; andadjusting, by the processor or control circuit, power delivered to the ultrasonic transducer based on the changes in tissue characteristics throughout the transection process,wherein determining, by the processor or control circuit, changes in tissue characteristics comprises: applying, by a drive circuit, a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;sweeping, by the processor or control circuit, the frequency of the drive signal from below resonance to above resonance of the electromechanical ultrasonic system;measuring an impedance magnitude and an impedance phase of the ultrasonic transducer while the drive signal is swept by the processor or control circuit;calculating and recording, using the measured impedance magnitude and impedance phase, by the processor or control circuit, impedance/admittance circle variables Re, Ge, Xe, and Be;comparing, by the processor or control circuit, the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; anddetermining, by the processor or control circuit, a state or condition of the end effector based on the result of the comparing of the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to the reference impedance/admittance circle variables Rref, Gref, Xref, and Bref.
  • 2. The method of claim 1, wherein determining, by the processor or control circuit, changes in tissue characteristics comprises determining, by the processor or control circuit, changes in a tissue thickness.
  • 3. An ultrasonic surgical instrument comprising: an ultrasonic electromechanical system comprising an ultrasonic transducer coupled to an ultrasonic blade via an ultrasonic waveguide; anda generator configured to supply power to the ultrasonic transducer, wherein the generator comprises a control circuit configured to: determine an impedance of the ultrasonic transducer coupled to the ultrasonic blade during a transection process;analyze the impedance of the ultrasonic transducer;profile the ultrasonic blade based on the impedance of the ultrasonic transducer;adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade;pulse the power delivered to the ultrasonic transducer;determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics are determined between pulses;adjust power delivered to the ultrasonic transducer based on the changes in tissue characteristics throughout the transection process;cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;sweep the frequency of the drive signal from below resonance to above resonance of the ultrasonic electromechanical system;measure an impedance magnitude and an impedance phase of the ultrasonic transducer while the drive signal is swept by the control circuit;calculate and record, using the measured impedance magnitude and impedance phase, impedance/admittance circle variables Re, Ge, Xe, and Be;compare the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; anddetermine a state or condition of the end effector based on the result of the comparison of the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to the reference impedance/admittance circle variables Rref, Gref, Xref, and Bref.
  • 4. The ultrasonic surgical instrument of claim 3, wherein the changes in the tissue characteristics comprise changes in a tissue thickness.
  • 5. A generator for an ultrasonic surgical instrument, the generator comprising: a control circuit configured to: determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process;analyze the impedance of the ultrasonic transducer;profile the ultrasonic blade based on the impedance of the ultrasonic transducer;adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade;pulse the power delivered to the ultrasonic transducer;determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics are determined between pulses;adjust power delivered to the ultrasonic transducer based on the changes in tissue characteristics throughout the transection process;cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;sweep the frequency of the drive signal from below resonance to above resonance of the ultrasonic surgical instrument;measure an impedance magnitude and an impedance phase of the ultrasonic transducer while the drive signal is swept by the control circuit;calculate and record, using the measured impedance magnitude and impedance phase, impedance/admittance circle variables Re, Ge, Xe, and Be;compare the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; anddetermine a state or condition of the end effector based on a result of the comparison of the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to the reference impedance/admittance circle variables Rref, Gref, Xref, and Bref.
  • 6. The generator for the ultrasonic surgical instrument of claim 5, wherein the changes in the tissue characteristics comprise changes in a tissue thickness.
  • 7. An ultrasonic surgical system, comprising: a processor and a non-transitory memory, wherein the non-transitory memory comprises instructions that, when executed by the processor, cause the processor to: determine an impedance of an ultrasonic transducer coupled to an ultrasonic blade during a transection process;analyze the impedance of the ultrasonic transducer;profile the ultrasonic blade based on the impedance of the ultrasonic transducer;adjust a power delivered to the ultrasonic transducer during the transection process based on the profile of the ultrasonic blade;pulse the power delivered to the ultrasonic transducer;determine changes in tissue characteristics of tissue located in an end effector, wherein the changes in tissue characteristics are determined between pulses; andadjust power delivered to the ultrasonic transducer based on the changes in tissue characteristics throughout the transection process;cause a drive circuit to apply a drive signal to the ultrasonic transducer, wherein the drive signal is a periodic signal defined by a magnitude and frequency;sweep the frequency of the drive signal from below resonance to above resonance of the ultrasonic surgical system;measure an impedance magnitude and an impedance phase of the ultrasonic transducer while the drive signal is swept by the processor;calculate and record, using the measured impedance magnitude and impedance phase, impedance/admittance circle variables Re, Ge, Xe, and Be;compare the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to reference impedance/admittance circle variables Rref, Gref, Xref, and Bref; anddetermine a state or condition of the end effector based on a result of the comparison of the calculated impedance/admittance circle variables Re, Ge, Xe, and Be to the reference impedance/admittance circle variables Rref, Gref, Xref, and Bref.
CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 62/640,415, titled ESTIMATING STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR, filed Mar. 8, 2018, the disclosure of which is herein incorporated by reference in its entirety.

US Referenced Citations (2337)
Number Name Date Kind
1853416 Hall Apr 1932 A
2222125 Stehlik Nov 1940 A
3082426 Miles Mar 1963 A
3503396 Pierie et al. Mar 1970 A
3584628 Green Jun 1971 A
3626457 Duerr et al. Dec 1971 A
3633584 Farrell Jan 1972 A
3759017 Young Sep 1973 A
3863118 Lander et al. Jan 1975 A
3898545 Coppa et al. Aug 1975 A
3912121 Steffen Oct 1975 A
3915271 Harper Oct 1975 A
3932812 Milligan Jan 1976 A
4041362 Ichiyanagi Aug 1977 A
4052649 Greenwell et al. Oct 1977 A
4087730 Goles May 1978 A
4157859 Terry Jun 1979 A
4171700 Farin Oct 1979 A
4202722 Paquin May 1980 A
4412539 Jarvik Nov 1983 A
4448193 Ivanov May 1984 A
4523695 Braun et al. Jun 1985 A
4608160 Zoch Aug 1986 A
4614366 North et al. Sep 1986 A
4633874 Chow et al. Jan 1987 A
4701193 Robertson et al. Oct 1987 A
4735603 Goodson et al. Apr 1988 A
4788977 Farin et al. Dec 1988 A
4849752 Bryant Jul 1989 A
D303787 Messenger et al. Oct 1989 S
4892244 Fox et al. Jan 1990 A
4976173 Yang Dec 1990 A
5010341 Huntley et al. Apr 1991 A
5026387 Thomas Jun 1991 A
5035692 Lyon et al. Jul 1991 A
5042460 Sakurai et al. Aug 1991 A
5047043 Kubota et al. Sep 1991 A
5084057 Green et al. Jan 1992 A
5100402 Fan Mar 1992 A
D327061 Soren et al. Jun 1992 S
5129570 Schulze et al. Jul 1992 A
5151102 Kamiyama et al. Sep 1992 A
5156315 Green et al. Oct 1992 A
5158585 Saho et al. Oct 1992 A
5171247 Hughett et al. Dec 1992 A
5189277 Boisvert et al. Feb 1993 A
5197962 Sansom et al. Mar 1993 A
5204669 Dorfe et al. Apr 1993 A
5242474 Herbst et al. Sep 1993 A
5253793 Green et al. Oct 1993 A
5271543 Grant et al. Dec 1993 A
RE34519 Fox et al. Jan 1994 E
5275323 Schulze et al. Jan 1994 A
5318516 Cosmescu Jun 1994 A
5318563 Malis et al. Jun 1994 A
5322055 Davison et al. Jun 1994 A
5342349 Kaufman Aug 1994 A
5364003 Williamson, IV Nov 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
5415335 Knodell, Jr. May 1995 A
5417699 Klein et al. May 1995 A
5439468 Schulze et al. Aug 1995 A
5445304 Plyley et al. Aug 1995 A
5462545 Wang et al. Oct 1995 A
5465895 Knodel et al. Nov 1995 A
5467911 Tsuruta et al. Nov 1995 A
5474566 Alesi et al. Dec 1995 A
5485947 Olson et al. Jan 1996 A
5496315 Weaver et al. Mar 1996 A
5496317 Goble et al. Mar 1996 A
5503320 Webster et al. Apr 1996 A
5529235 Boiarski et al. Jun 1996 A
5531743 Nettekoven et al. Jul 1996 A
5545148 Wurster Aug 1996 A
5552685 Young et al. Sep 1996 A
5560372 Cory Oct 1996 A
5584425 Savage et al. Dec 1996 A
5610379 Muz et al. Mar 1997 A
5610811 Honda Mar 1997 A
5613966 Makower et al. Mar 1997 A
5624452 Yates Apr 1997 A
D379346 Mieki May 1997 S
5626587 Bishop et al. May 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
5693042 Boiarski et al. Dec 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
5718359 Palmer et al. Feb 1998 A
5724468 Leone et al. Mar 1998 A
5725536 Oberlin et al. Mar 1998 A
5725542 Yoon Mar 1998 A
5735445 Vidal et al. Apr 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
5775331 Raymond et al. Jul 1998 A
5797537 Oberlin et al. Aug 1998 A
5800350 Coppleson et al. Sep 1998 A
D399561 Ellingson Oct 1998 S
5817093 Williamson, IV et al. Oct 1998 A
5820009 Melling et al. Oct 1998 A
5833690 Yates et al. Nov 1998 A
5836849 Mathiak et al. Nov 1998 A
5836869 Kudo et al. Nov 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
5987346 Benaron 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
6039734 Goble 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
6102907 Smethers et al. Aug 2000 A
6109500 Alli et al. Aug 2000 A
6113598 Baker Sep 2000 A
6126592 Proch et al. Oct 2000 A
6126658 Baker Oct 2000 A
6131789 Schulze et al. Oct 2000 A
6155473 Tompkins et al. Dec 2000 A
6214000 Fleenor et al. Apr 2001 B1
6258105 Hart et al. Jul 2001 B1
6269411 Reasoner Jul 2001 B1
6273887 Yamauchi et al. Aug 2001 B1
6301495 Gueziec et al. Oct 2001 B1
6302881 Farin Oct 2001 B1
6308089 von der Ruhr et al. Oct 2001 B1
6325808 Bernard et al. Dec 2001 B1
6325811 Messerly Dec 2001 B1
6331181 Tierney et al. Dec 2001 B1
6341164 Dilkie et al. Jan 2002 B1
6391102 Bodden et al. May 2002 B1
6434416 Mizoguchi et al. Aug 2002 B1
6443973 Whitman Sep 2002 B1
6451015 Rittman, III et al. Sep 2002 B1
6454781 Witt et al. Sep 2002 B1
6457625 Tormala et al. Oct 2002 B1
6461352 Morgan et al. Oct 2002 B2
6466817 Kaula et al. Oct 2002 B1
6480796 Wiener Nov 2002 B2
6524307 Palmerton et al. Feb 2003 B1
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
6584358 Carter et al. Jun 2003 B2
6585791 Garito et al. Jul 2003 B1
6611793 Burnside et al. Aug 2003 B1
6618626 West, Jr. et al. Sep 2003 B2
6633234 Wiener et al. Oct 2003 B2
6648223 Boukhny et al. Nov 2003 B2
6678552 Pearlman Jan 2004 B2
6679899 Wiener et al. Jan 2004 B2
6685704 Greep Feb 2004 B2
6699187 Webb et al. Mar 2004 B2
6731514 Evans May 2004 B2
6742895 Robin Jun 2004 B2
6752816 Culp et al. Jun 2004 B2
6760616 Hoey et al. Jul 2004 B2
6770072 Truckai et al. Aug 2004 B1
6773444 Messerly Aug 2004 B2
6775575 Bommannan et al. 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
6793663 Kneifel et al. Sep 2004 B2
6824539 Novak Nov 2004 B2
6846308 Whitman et al. Jan 2005 B2
6849074 Chen et al. Feb 2005 B2
6852219 Hammond Feb 2005 B2
6863650 Irion Mar 2005 B1
6869430 Balbierz et al. Mar 2005 B2
6869435 Blake, III Mar 2005 B2
6911033 de Guillebon et al. Jun 2005 B2
6913471 Smith Jul 2005 B2
6937892 Leyde et al. Aug 2005 B2
6945981 Donofrio et al. Sep 2005 B2
6951559 Greep Oct 2005 B1
6962587 Johnson et al. Nov 2005 B2
6978921 Shelton, IV et al. Dec 2005 B2
6988649 Shelton, IV et al. Jan 2006 B2
7000818 Shelton, IV et al. Feb 2006 B2
7009511 Mazar et al. Mar 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
7044949 Orszulak et al. May 2006 B2
7048775 Jornitz et al. May 2006 B2
7053752 Wang et al. May 2006 B2
7055730 Ehrenfels et al. Jun 2006 B2
7073765 Newkirk Jul 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
7104949 Anderson et al. Sep 2006 B2
7118564 Ritchie et al. Oct 2006 B2
7121460 Parsons et al. Oct 2006 B1
7137980 Buysse et al. Nov 2006 B2
7140528 Shelton, IV Nov 2006 B2
7143923 Shelton, IV et al. Dec 2006 B2
7143925 Shelton, IV et al. Dec 2006 B2
7147139 Schwemberger et al. Dec 2006 B2
7155316 Sutherland et al. Dec 2006 B2
7164940 Hareyama et al. Jan 2007 B2
7169145 Isaacson et al. Jan 2007 B2
7177533 McFarlin et al. Feb 2007 B2
7182775 de Guillebon et al. Feb 2007 B2
7207472 Wukusick et al. Apr 2007 B2
7208005 Frecker et al. Apr 2007 B2
7217269 El-Galley et al. May 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
7252664 Nasab et al. Aug 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
7334717 Rethy et al. Feb 2008 B2
7343565 Ying et al. Mar 2008 B2
7344532 Goble et al. Mar 2008 B2
7353068 Tanaka et al. Apr 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
7408439 Wang et al. Aug 2008 B2
7422136 Marczyk Sep 2008 B1
7422139 Shelton, IV et al. Sep 2008 B2
7423972 Shaham et al. Sep 2008 B2
D579876 Novotney et al. Nov 2008 S
7457804 Uber, III et al. Nov 2008 B2
D583328 Chiang Dec 2008 S
7464847 Viola et al. Dec 2008 B2
7464849 Shelton, IV et al. Dec 2008 B2
7496418 Kim et al. Feb 2009 B2
D589447 Sasada et al. Mar 2009 S
7515961 Germanson et al. Apr 2009 B2
7518502 Austin et al. Apr 2009 B2
7554343 Bromfield Jun 2009 B2
7563259 Takahashi Jul 2009 B2
7568604 Ehrenfels et al. Aug 2009 B2
7575144 Ortiz et al. Aug 2009 B2
7597731 Palmerton et al. Oct 2009 B2
7617137 Kreiner et al. Nov 2009 B2
7621192 Conti et al. Nov 2009 B2
7621898 Lalomia et al. Nov 2009 B2
7631793 Rethy et al. Dec 2009 B2
7637410 Marczyk Dec 2009 B2
7637907 Blaha Dec 2009 B2
7641092 Kruszynski et al. Jan 2010 B2
7644848 Swayze et al. Jan 2010 B2
7667592 Ohyama et al. Feb 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
7721936 Shalton, IV et al. May 2010 B2
7736357 Lee, Jr. et al. Jun 2010 B2
7742176 Braunecker et al. Jun 2010 B2
7743960 Whitman et al. Jun 2010 B2
7753245 Boudreaux et al. Jul 2010 B2
7757028 Druke et al. Jul 2010 B2
7766207 Mather et al. Aug 2010 B2
7766905 Paterson et al. Aug 2010 B2
7770773 Whitman et al. Aug 2010 B2
7771429 Ballard 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
7810692 Hall et al. Oct 2010 B2
7818041 Kim et al. Oct 2010 B2
7819298 Hall et al. Oct 2010 B2
7832612 Baxter, III et al. Nov 2010 B2
7833219 Tashiro et al. Nov 2010 B2
7836085 Petakov et al. Nov 2010 B2
7837079 Holsten et al. Nov 2010 B2
7837680 Isaacson et al. Nov 2010 B2
7841980 Minosawa et al. Nov 2010 B2
7845537 Shelton, IV et al. Dec 2010 B2
7857185 Swayze et al. Dec 2010 B2
D631252 Leslie Jan 2011 S
7862560 Marion Jan 2011 B2
7862579 Ortiz et al. Jan 2011 B2
7865236 Cory et al. Jan 2011 B2
7884735 Newkirk Feb 2011 B2
7887530 Zemlok et al. Feb 2011 B2
7892337 Palmerton et al. Feb 2011 B2
7907166 Lamprecht et al. Mar 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
7927014 Dehler Apr 2011 B2
7932826 Fritchie et al. Apr 2011 B2
7942300 Rethy et al. May 2011 B2
7945065 Menzl et al. May 2011 B2
7945342 Tsai et al. May 2011 B2
7951148 McClurken May 2011 B2
7954682 Giordano et al. Jun 2011 B2
7955322 Devengenzo et al. Jun 2011 B2
7956620 Gilbert 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
8007494 Taylor et al. Aug 2011 B1
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
8016855 Whitman et al. Sep 2011 B2
8019094 Hsieh et al. Sep 2011 B2
8025199 Whitman et al. Sep 2011 B2
8027710 Dannan Sep 2011 B1
8035685 Jensen Oct 2011 B2
8038686 Huitema et al. Oct 2011 B2
8038693 Allen Oct 2011 B2
8043560 Okumoto et al. Oct 2011 B2
8054184 Cline et al. Nov 2011 B2
8054752 Druke 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
8074861 Ehrenfels et al. Dec 2011 B2
8075571 Vitali et al. Dec 2011 B2
8096459 Ortiz et al. Jan 2012 B2
8118206 Zand et al. Feb 2012 B2
8120301 Goldberg et al. Feb 2012 B2
8123764 Meade et al. Feb 2012 B2
D655678 Kobayashi et al. Mar 2012 S
8128625 Odom Mar 2012 B2
8131565 Dicks et al. Mar 2012 B2
8136712 Zingman Mar 2012 B2
D657368 Magee et al. Apr 2012 S
8147486 Honour et al. Apr 2012 B2
8155479 Hoffman et al. Apr 2012 B2
8157145 Shelton, IV et al. Apr 2012 B2
8157150 Viola et al. Apr 2012 B2
8157151 Ingmanson et al. Apr 2012 B2
8160098 Yan et al. Apr 2012 B1
8160690 Wilfley et al. Apr 2012 B2
8161977 Shelton, IV et al. Apr 2012 B2
8170396 Kuspa et al. May 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
8208707 Mendonca et al. Jun 2012 B2
8210411 Yates et al. Jul 2012 B2
8214007 Baker et al. Jul 2012 B2
8216849 Petty Jul 2012 B2
8220688 Laurent et al. Jul 2012 B2
8225643 Abboud et al. Jul 2012 B2
8225979 Farascioni et al. Jul 2012 B2
8229549 Whitman et al. Jul 2012 B2
8231042 Hessler et al. Jul 2012 B2
8239066 Jennings et al. Aug 2012 B2
8241322 Whitman et al. Aug 2012 B2
8255045 Gharib et al. Aug 2012 B2
D667838 Magee et al. Sep 2012 S
8257387 Cunningham Sep 2012 B2
8260016 Maeda et al. Sep 2012 B2
8262560 Whitman Sep 2012 B2
8292639 Achammer et al. Oct 2012 B2
8292888 Whitman Oct 2012 B2
8295902 Salahieh et al. Oct 2012 B2
8308040 Huang et al. Nov 2012 B2
8321581 Katis et al. Nov 2012 B2
8322590 Patel et al. Dec 2012 B2
8328065 Shah Dec 2012 B2
8335590 Costa et al. Dec 2012 B2
D675164 Kobayashi et al. Jan 2013 S
8343065 Bartol et al. Jan 2013 B2
8346392 Walser et al. Jan 2013 B2
8360299 Zemlok et al. Jan 2013 B2
8364222 Cook et al. Jan 2013 B2
D676392 Gassauer Feb 2013 S
8365975 Manoux et al. Feb 2013 B1
D678196 Miyauchi et al. Mar 2013 S
D678304 Yakoub et al. Mar 2013 S
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
8403944 Pain et al. Mar 2013 B2
8403945 Whitfield et al. Mar 2013 B2
8403946 Whitfield et al. Mar 2013 B2
8406859 Zuzak et al. Mar 2013 B2
8411034 Boillot et al. Apr 2013 B2
8413871 Racenet et al. Apr 2013 B2
8422035 Hinderling et al. Apr 2013 B2
8423182 Robinson et al. Apr 2013 B2
8428722 Verhoef et al. Apr 2013 B2
8429153 Birdwell 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
D687146 Juzkiw et al. Jul 2013 S
8476227 Kaplan et al. Jul 2013 B2
8489235 Moll et al. Jul 2013 B2
8499992 Whitman et al. Aug 2013 B2
8500728 Newton et al. Aug 2013 B2
8500756 Papa et al. Aug 2013 B2
8503759 Greer et al. Aug 2013 B2
8505801 Ehrenfels et al. Aug 2013 B2
8506478 Mizuyoshi Aug 2013 B2
8512325 Mathonnet Aug 2013 B2
8512365 Wiener et al. Aug 2013 B2
8515520 Brunnett et al. Aug 2013 B2
8517239 Scheib et al. Aug 2013 B2
8521331 Itkowitz Aug 2013 B2
8523043 Ullrich et al. Sep 2013 B2
8540709 Allen Sep 2013 B2
8546996 Messerly et al. Oct 2013 B2
8554697 Claus 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
8567393 Hickle et al. Oct 2013 B2
8571598 Valavi Oct 2013 B2
8573459 Smith et al. Nov 2013 B2
8573465 Shelton, IV Nov 2013 B2
8574229 Eder et al. Nov 2013 B2
8585694 Amoah et al. Nov 2013 B2
8590762 Hess et al. Nov 2013 B2
8591536 Robertson Nov 2013 B2
8595607 Nekoomaram et al. Nov 2013 B2
8596513 Olson et al. Dec 2013 B2
8596515 Okoniewski Dec 2013 B2
8604709 Jalbout 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
8620055 Barratt 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
8627993 Smith 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
8631987 Shelton, IV et al. Jan 2014 B2
8632525 Kerr et al. Jan 2014 B2
8636190 Zemlok et al. Jan 2014 B2
8636736 Yates et al. Jan 2014 B2
8641621 Razzaque et al. Feb 2014 B2
8652086 Gerg et al. Feb 2014 B2
8652121 Quick 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
8679114 Chapman 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
8688188 Heller et al. Apr 2014 B2
8690864 Hoarau Apr 2014 B2
8701962 Kostrzewski Apr 2014 B2
D704839 Juzkiw et al. May 2014 S
8719061 Birchall May 2014 B2
8720766 Hess et al. May 2014 B2
8733613 Huitema et al. May 2014 B2
8740840 Foley et al. Jun 2014 B2
8740866 Reasoner et al. Jun 2014 B2
8747238 Shelton, IV 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
8790253 Sunagawa et al. Jul 2014 B2
8794497 Zingman Aug 2014 B2
8795001 Lam et al. Aug 2014 B1
8799008 Johnson et al. Aug 2014 B2
8799009 Mellin et al. Aug 2014 B2
8800838 Shelton, IV Aug 2014 B2
8801703 Gregg et al. Aug 2014 B2
8814996 Giurgiutiu et al. Aug 2014 B2
8818556 Sanchez et al. Aug 2014 B2
8819581 Nakamura 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
D716333 Chotin et al. Oct 2014 S
8851354 Swensgard et al. Oct 2014 B2
8852174 Burbank Oct 2014 B2
8875973 Whitman Nov 2014 B2
8882662 Charles Nov 2014 B2
8886790 Harrang et al. Nov 2014 B2
8893949 Shelton, IV et al. Nov 2014 B2
8899479 Cappuzzo et al. Dec 2014 B2
8905977 Shelton et al. Dec 2014 B2
8912746 Reid et al. Dec 2014 B2
8914098 Brennan et al. Dec 2014 B2
8917513 Hazzard Dec 2014 B1
8918207 Prisco Dec 2014 B2
8920186 Shishikura 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
8936614 Allen, IV Jan 2015 B2
8945095 Blumenkranz et al. Feb 2015 B2
8945163 Voegele et al. Feb 2015 B2
8955732 Zemlok 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
8967443 McCuen Mar 2015 B2
8967455 Zhou Mar 2015 B2
8968276 Zemlok et al. Mar 2015 B2
8968296 McPherson Mar 2015 B2
8968309 Roy et al. Mar 2015 B2
8968312 Marczyk et al. Mar 2015 B2
8968337 Whitfield et al. Mar 2015 B2
8968358 Reschke Mar 2015 B2
8974429 Gordon et al. Mar 2015 B2
8979890 Boudreaux Mar 2015 B2
8986288 Konishi 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
9010611 Ross 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
D729267 Yoo et al. May 2015 S
9023032 Robinson May 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
9055870 Meador 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
9072523 Houser et al. Jul 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
9099863 Smith 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
9111548 Nandy et al. Aug 2015 B2
9113880 Zemlok et al. Aug 2015 B2
9114494 Mah Aug 2015 B1
9116597 Gulasky Aug 2015 B1
9119617 Souls et al. Sep 2015 B2
9119655 Bowling et al. Sep 2015 B2
9119657 Shelton, IV et al. Sep 2015 B2
9123155 Cunningham et al. Sep 2015 B2
9125644 Lane et al. Sep 2015 B2
9129054 Nawana et al. Sep 2015 B2
9137254 Bilbrey et al. Sep 2015 B2
9138129 Diolaiti Sep 2015 B2
9138225 Huang et al. Sep 2015 B2
9149322 Knowlton Oct 2015 B2
9155503 Cadwell Oct 2015 B2
9160853 Daddi et al. Oct 2015 B1
9161803 Yates et al. Oct 2015 B2
9168054 Turner et al. Oct 2015 B2
9168091 Janssen et al. Oct 2015 B2
9168104 Dein 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
9198711 Joseph Dec 2015 B2
9202078 Abuelsaad et al. Dec 2015 B2
9204830 Zand et al. Dec 2015 B2
9204879 Shelton, IV Dec 2015 B2
9204995 Scheller et al. Dec 2015 B2
9211120 Scheib et al. Dec 2015 B2
9216062 Duque et al. Dec 2015 B2
9218053 Komuro et al. Dec 2015 B2
9220502 Zemlok et al. Dec 2015 B2
9226689 Jacobsen et al. Jan 2016 B2
9226751 Shelton, IV et al. Jan 2016 B2
9226766 Aldridge et al. Jan 2016 B2
9226767 Stulen et al. Jan 2016 B2
9226791 McCarthy et al. Jan 2016 B2
9232883 Ozawa et al. Jan 2016 B2
9237891 Shelton, IV Jan 2016 B2
9237921 Messerly et al. Jan 2016 B2
9241728 Price et al. Jan 2016 B2
9241730 Babaev Jan 2016 B2
9241731 Boudreaux et al. Jan 2016 B2
9247996 Merana et al. Feb 2016 B1
9250172 Harris et al. Feb 2016 B2
9255907 Heanue et al. Feb 2016 B2
9265429 St. Pierre et al. Feb 2016 B2
9265585 Wingardner et al. Feb 2016 B2
9272406 Aronhalt et al. Mar 2016 B2
9277956 Zhang Mar 2016 B2
9277961 Panescu et al. Mar 2016 B2
9277969 Brannan et al. Mar 2016 B2
9280884 Schultz et al. Mar 2016 B1
9282962 Schmid et al. Mar 2016 B2
9282974 Shelton, IV Mar 2016 B2
9283045 Rhee et al. Mar 2016 B2
9283054 Morgan et al. Mar 2016 B2
9289211 Williams 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
9302213 Manahan 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
9320563 Brustad et al. Apr 2016 B2
9325732 Stickle et al. Apr 2016 B1
9326767 Koch et al. May 2016 B2
9326770 Shelton, IV 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
9336385 Spencer et al. May 2016 B1
9341704 Picard et al. May 2016 B2
9345481 Hall et al. May 2016 B2
9345490 Ippisch May 2016 B2
9345546 Toth et al. May 2016 B2
9345900 Wu et al. 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
9370400 Parihar 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
9414940 Stein et al. Aug 2016 B2
9419018 Sasagawa et al. Aug 2016 B2
9421014 Ingmanson et al. Aug 2016 B2
9433470 Choi Sep 2016 B2
9439622 Case et al. Sep 2016 B2
9439668 Timm et al. Sep 2016 B2
9439736 Olson Sep 2016 B2
9445764 Gross et al. Sep 2016 B2
9445813 Shelton, IV et al. Sep 2016 B2
9450701 Do et al. Sep 2016 B2
9451949 Gorek et al. Sep 2016 B2
9451958 Shelton, IV et al. Sep 2016 B2
9463022 Swayze et al. Oct 2016 B2
9463646 Payne et al. Oct 2016 B2
9468438 Baber et al. Oct 2016 B2
9474565 Shikhman et al. Oct 2016 B2
D772252 Myers et al. Nov 2016 S
9480492 Aranyi et al. Nov 2016 B2
9485475 Speier et al. Nov 2016 B2
9486271 Dunning Nov 2016 B2
9492146 Kostrzewski et al. Nov 2016 B2
9492237 Kang et al. Nov 2016 B2
9493807 Little et al. Nov 2016 B2
9498182 Case et al. Nov 2016 B2
9498215 Duque et al. Nov 2016 B2
9498231 Haider et al. Nov 2016 B2
9516239 Blanquart et al. Dec 2016 B2
9519753 Gerdeman et al. Dec 2016 B1
9522003 Weir et al. Dec 2016 B2
9526407 Hoeg et al. Dec 2016 B2
9526499 Kostrzewski et al. Dec 2016 B2
9526587 Zhao et al. Dec 2016 B2
9532827 Morgan et al. Jan 2017 B2
9532845 Dossett et al. Jan 2017 B1
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
9549781 He et al. Jan 2017 B2
9554692 Levy 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
9561082 Yen et al. Feb 2017 B2
9561982 Enicks et al. Feb 2017 B2
9566708 Kurnianto Feb 2017 B2
9572592 Price et al. Feb 2017 B2
9579503 McKinney 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
9600031 Kaneko et al. Mar 2017 B2
9600138 Thomas et al. Mar 2017 B2
9603024 Wang et al. Mar 2017 B2
9603277 Morgan et al. Mar 2017 B2
D783675 Yagisawa et al. Apr 2017 S
D784270 Bhattacharya Apr 2017 S
9610114 Baxter, III et al. Apr 2017 B2
9622684 Wybo Apr 2017 B2
9622808 Beller et al. Apr 2017 B2
9628501 Datta Ray et al. Apr 2017 B2
9629560 Joseph Apr 2017 B2
9629623 Lytle, IV et al. Apr 2017 B2
9629628 Aranyi 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
9636239 Durand et al. May 2017 B2
9636825 Penn et al. May 2017 B2
9641596 Unagami et al. May 2017 B2
9641815 Richardson et al. May 2017 B2
9642620 Baxter, III et al. May 2017 B2
9643022 Mashiach 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
9671860 Ogawa et al. Jun 2017 B2
9675264 Acquista et al. Jun 2017 B2
9675354 Weir 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
9700320 Dinardo et al. Jul 2017 B2
9706993 Hessler et al. Jul 2017 B2
9710214 Lin et al. Jul 2017 B2
9710644 Reybok et al. Jul 2017 B2
9713424 Spaide Jul 2017 B2
9713503 Goldschmidt Jul 2017 B2
9717141 Tegg Jul 2017 B1
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
9724100 Scheib 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
9750500 Malkowski Sep 2017 B2
9750522 Scheib et al. Sep 2017 B2
9750523 Tsubuku Sep 2017 B2
9750563 Shikhman et al. Sep 2017 B2
9753135 Bosch Sep 2017 B2
9753568 McMillen 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
9763741 Alvarez et al. Sep 2017 B2
9764164 Wiener et al. Sep 2017 B2
9770541 Carr et al. Sep 2017 B2
9775611 Kostrzewski Oct 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
9788835 Morgan 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
9797486 Zergiebel et al. Oct 2017 B2
9801531 Morita 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
9808249 Shelton, IV 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
9830424 Dixon et al. Nov 2017 B2
9833241 Huitema et al. Dec 2017 B2
9833254 Barral et al. Dec 2017 B1
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
9844321 Ekvall et al. Dec 2017 B1
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
9844376 Baxter, III 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
9864839 Baym et al. Jan 2018 B2
9867612 Parihar et al. Jan 2018 B2
9867651 Wham Jan 2018 B2
9867670 Brannan et al. 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
9888864 Rondon et al. Feb 2018 B2
9888914 Martin et al. Feb 2018 B2
9888919 Leimbach et al. Feb 2018 B2
9888921 Williams et al. Feb 2018 B2
9888975 Auld Feb 2018 B2
9895148 Shelton, IV et al. Feb 2018 B2
9900787 Ou Feb 2018 B2
9901342 Shelton, IV et al. Feb 2018 B2
9901406 State et al. Feb 2018 B2
9905000 Chou et al. Feb 2018 B2
9907196 Susini 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
9918326 Gilson et al. Mar 2018 B2
9918730 Trees 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
9924944 Shelton, IV et al. 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
9936863 Tesar 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
9943230 Kaku et al. Apr 2018 B2
9943309 Shelton, IV et al. Apr 2018 B2
9943312 Posada 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
9974595 Anderson et al. May 2018 B2
9980140 Spencer et al. May 2018 B1
9980769 Trees et al. May 2018 B2
9980778 Ohline et al. May 2018 B2
9987000 Shelton, IV et al. Jun 2018 B2
9987068 Anderson et al. Jun 2018 B2
9987072 McPherson Jun 2018 B2
9990856 Kuchenbecker et al. Jun 2018 B2
9993248 Shelton, IV et al. Jun 2018 B2
9993258 Shelton, IV et al. Jun 2018 B2
9993305 Andersson 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
10004557 Gross 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
10022090 Whitman 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
10028402 Walker Jul 2018 B1
10028744 Shelton, IV 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
10037715 Toly et al. Jul 2018 B2
D826405 Shelton, IV et al. Aug 2018 S
10039546 Williams et al. Aug 2018 B2
10039564 Hibner et al. Aug 2018 B2
10039565 Vezzu Aug 2018 B2
10039589 Virshek et al. Aug 2018 B2
10041822 Zemlok Aug 2018 B2
10044791 Kamen et al. Aug 2018 B2
10045704 Fagin 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
10045782 Murthy Aravalli 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
10052104 Shelton, IV et al. Aug 2018 B2
10054441 Schorr et al. Aug 2018 B2
10058393 Bonutti et al. Aug 2018 B2
10069633 Gulati et al. Sep 2018 B2
10076326 Yates et al. Sep 2018 B2
10080618 Marshall et al. Sep 2018 B2
10084833 McDonnell et al. Sep 2018 B2
D831209 Huitema et al. Oct 2018 S
10085748 Morgan et al. Oct 2018 B2
10085749 Cappola et al. Oct 2018 B2
10092355 Hannaford et al. Oct 2018 B1
10095942 Mentese et al. Oct 2018 B2
10097578 Baldonado et al. Oct 2018 B2
10098527 Weisenburgh, II et al. Oct 2018 B2
10098635 Burbank Oct 2018 B2
10098642 Baxter, III et al. Oct 2018 B2
10098705 Brisson et al. Oct 2018 B2
10102926 Leonardi Oct 2018 B1
10105140 Malinouskas et al. Oct 2018 B2
10105142 Baxter, III et al. Oct 2018 B2
10105470 Reasoner 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
10111703 Cosman, Jr. et al. Oct 2018 B2
D834541 You et al. Nov 2018 S
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
10130361 Yates 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
10136891 Shelton, IV et al. Nov 2018 B2
10136949 Felder et al. Nov 2018 B2
10136954 Johnson et al. Nov 2018 B2
10137245 Melker et al. Nov 2018 B2
10143526 Walker et al. Dec 2018 B2
10143948 Bonitas et al. Dec 2018 B2
10147148 Wu et al. Dec 2018 B2
10149680 Parihar et al. Dec 2018 B2
10152789 Carnes et al. Dec 2018 B2
10154841 Weaner 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
10166061 Berry et al. Jan 2019 B2
10169862 Andre et al. Jan 2019 B2
10172618 Shelton, IV 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
10190888 Hryb et al. Jan 2019 B2
10194891 Jeong et al. Feb 2019 B2
10194907 Marczyk et al. Feb 2019 B2
10194913 Nalagatla et al. Feb 2019 B2
10194972 Yates et al. Feb 2019 B2
10197803 Badiali et al. Feb 2019 B2
10198965 Hart Feb 2019 B2
10201311 Chou 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
10213203 Swayze 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
10220522 Rockrohr Mar 2019 B2
10222750 Bang et al. Mar 2019 B2
10226249 Jaworek 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
10231775 Shelton, IV 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
10251725 Valentine et al. Apr 2019 B2
10258331 Shelton, IV et al. Apr 2019 B2
10258359 Kapadia Apr 2019 B2
10258362 Conlon Apr 2019 B2
10258363 Worrell et al. 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
10283220 Azizian et al. May 2019 B2
10285694 Viola et al. May 2019 B2
10285698 Cappola et al. May 2019 B2
10285700 Scheib 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
10293129 Fox et al. May 2019 B2
10299792 Huitema et al. May 2019 B2
10299870 Connolly et al. May 2019 B2
10305926 Mihan 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
10335180 Johnson et al. Jul 2019 B2
10335227 Heard Jul 2019 B2
10339496 Matson et al. 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
10349824 Claude et al. Jul 2019 B2
10349939 Shelton, IV et al. Jul 2019 B2
10349941 Marczyk et al. Jul 2019 B2
10350016 Burbank et al. Jul 2019 B2
10357184 Crawford 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
10363032 Scheib et al. 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
10368903 Morales 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
10384021 Koeth et al. Aug 2019 B2
10386990 Shikhman 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
10398348 Osadchy et al. Sep 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
10426481 Aronhalt 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
10433918 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
10478185 Nicholas Nov 2019 B2
10478189 Bear 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
10492784 Beardsley 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
10499847 Latimer 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
10512413 Schepis et al. Dec 2019 B2
10512461 Gupta et al. Dec 2019 B2
10512499 McHenry et al. Dec 2019 B2
10512514 Nowlin 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
10531579 Hsiao 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
10542978 Chowaniec 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
D876466 Kobayashi et al. Feb 2020 S
10548504 Shelton, IV et al. Feb 2020 B2
10548612 Martinez et al. Feb 2020 B2
10548673 Harris 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
10561753 Thompson et al. 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
10568704 Savaii et al. Feb 2020 B2
10575868 Hall et al. Mar 2020 B2
10582928 Hunter et al. Mar 2020 B2
10582931 Mujawar Mar 2020 B2
10582964 Weinberg et al. 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
10588711 DiCarlo et al. Mar 2020 B2
10592067 Merdan et al. Mar 2020 B2
10595844 Nawana et al. Mar 2020 B2
10595882 Parfett et al. Mar 2020 B2
10595887 Shelton, IV et al. Mar 2020 B2
10595930 Scheib et al. Mar 2020 B2
10595952 Forrest et al. Mar 2020 B2
10602007 Takano 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
10617482 Houser et al. Apr 2020 B2
10617484 Kilroy et al. Apr 2020 B2
10624635 Harris et al. Apr 2020 B2
10624667 Faller et al. Apr 2020 B2
10624691 Wiener et al. Apr 2020 B2
10631423 Collins et al. Apr 2020 B2
10631858 Burbank Apr 2020 B2
10631912 McFarlin 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
10639098 Cosman 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
10656720 Holz May 2020 B1
10660705 Piron et al. 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
10675035 Zingman Jun 2020 B2
10675100 Frushour 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
10716489 Kalvoy 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
10722292 Arya et al. Jul 2020 B2
D893717 Messerly et al. Aug 2020 S
10729458 Stoddard et al. Aug 2020 B2
10729509 Shelton, IV 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
10751052 Stokes 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
D896379 Shelton, IV et al. Sep 2020 S
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
10772630 Wixey Sep 2020 B2
10772651 Shelton, IV et al. Sep 2020 B2
10772673 Allen, IV et al. Sep 2020 B2
10772688 Peine et al. Sep 2020 B2
10779818 Zemlok et al. Sep 2020 B2
10779821 Harris et al. Sep 2020 B2
10779823 Shelton, IV et al. Sep 2020 B2
10779897 Rockrohr Sep 2020 B2
10779900 Pedros et al. Sep 2020 B2
10783634 Nye et al. Sep 2020 B2
10786298 Johnson Sep 2020 B2
10786317 Zhou et al. Sep 2020 B2
10786327 Anderson et al. Sep 2020 B2
10792038 Becerra et al. Oct 2020 B2
10792118 Prpa et al. Oct 2020 B2
10792422 Douglas et al. Oct 2020 B2
10799304 Kapadia et al. Oct 2020 B2
10803977 Sanmugalingham Oct 2020 B2
10806445 Penna et al. Oct 2020 B2
10806453 Chen et al. Oct 2020 B2
10806454 Kopp Oct 2020 B2
10806499 Castaneda et al. Oct 2020 B2
10806506 Gaspredes et al. Oct 2020 B2
10806532 Grubbs et al. Oct 2020 B2
10813638 Shelton, IV et al. Oct 2020 B2
10813703 Swayze et al. Oct 2020 B2
10818383 Sharifi Sedeh et al. Oct 2020 B2
10828028 Harris et al. Nov 2020 B2
10828030 Weir et al. Nov 2020 B2
10835245 Swayze et al. Nov 2020 B2
10835246 Shelton, IV et al. Nov 2020 B2
10835247 Shelton, IV et al. Nov 2020 B2
10842473 Scheib et al. Nov 2020 B2
10842490 DiNardo et al. Nov 2020 B2
10842492 Shelton, IV et al. Nov 2020 B2
10842522 Messerly et al. Nov 2020 B2
10842523 Shelton, IV et al. Nov 2020 B2
10842575 Panescu et al. Nov 2020 B2
10842897 Schwartz et al. Nov 2020 B2
D904612 Wynn et al. Dec 2020 S
10849697 Yates et al. Dec 2020 B2
10849700 Kopp et al. Dec 2020 B2
10856768 Osadchy et al. Dec 2020 B2
10856867 Shelton, IV et al. Dec 2020 B2
10856868 Shelton, IV et al. Dec 2020 B2
10856870 Harris et al. Dec 2020 B2
10863984 Shelton, IV et al. Dec 2020 B2
10864037 Mun et al. Dec 2020 B2
10864050 Tabandeh et al. Dec 2020 B2
10872684 McNutt et al. Dec 2020 B2
10881399 Shelton, IV et al. Jan 2021 B2
10881401 Baber et al. Jan 2021 B2
10881446 Strobl Jan 2021 B2
10881464 Odermatt et al. Jan 2021 B2
10888321 Shelton, IV et al. Jan 2021 B2
10888322 Morgan et al. Jan 2021 B2
10892899 Shelton, IV et al. Jan 2021 B2
10892995 Shelton, IV et al. Jan 2021 B2
10893863 Shelton, IV et al. Jan 2021 B2
10893864 Harris et al. Jan 2021 B2
10893884 Stoddard et al. Jan 2021 B2
10898183 Shelton, IV et al. Jan 2021 B2
10898186 Bakos et al. Jan 2021 B2
10898189 McDonald, II Jan 2021 B2
10898256 Yates Jan 2021 B2
10898280 Kopp Jan 2021 B2
10898622 Shelton, IV et al. Jan 2021 B2
10902944 Casey et al. Jan 2021 B1
10903685 Yates et al. Jan 2021 B2
10905415 DiNardo et al. Feb 2021 B2
10905418 Shelton, IV et al. Feb 2021 B2
10905420 Jasemian et al. Feb 2021 B2
10912559 Harris et al. Feb 2021 B2
10912580 Green et al. Feb 2021 B2
10912619 Jarc et al. Feb 2021 B2
10918385 Overmyer et al. Feb 2021 B2
10930400 Robbins et al. Feb 2021 B2
D914878 Shelton, IV et al. Mar 2021 S
10932784 Mozdzierz et al. Mar 2021 B2
10950982 Regnier et al. Mar 2021 B2
11000276 Shelton, IV et al. May 2021 B2
11051817 Shelton, IV et al. Jul 2021 B2
11058501 Tokarchuk et al. Jul 2021 B2
11179175 Houser Nov 2021 B2
20020049551 Friedman et al. Apr 2002 A1
20020052616 Wiener et al. May 2002 A1
20020072746 Lingenfelder et al. Jun 2002 A1
20020138642 Miyazawa et al. Sep 2002 A1
20030009111 Cory et al. Jan 2003 A1
20030018329 Hooven Jan 2003 A1
20030069573 Kadhiresan et al. Apr 2003 A1
20030093503 Yamaki et al. May 2003 A1
20030114851 Truckai et al. Jun 2003 A1
20030130711 Pearson et al. Jul 2003 A1
20030210812 Khamene et al. Nov 2003 A1
20030223877 Anstine et al. Dec 2003 A1
20040078236 Stoodley et al. Apr 2004 A1
20040199180 Knodel et al. Oct 2004 A1
20040199659 Ishikawa et al. Oct 2004 A1
20040206365 Knowlton Oct 2004 A1
20040243148 Wasielewski Dec 2004 A1
20040243435 Williams Dec 2004 A1
20050020909 Moctezuma de la Barrera et al. Jan 2005 A1
20050023324 Doll et al. Feb 2005 A1
20050063575 Ma et al. Mar 2005 A1
20050065438 Miller Mar 2005 A1
20050100867 Hilscher et al. May 2005 A1
20050131390 Heinrich et al. Jun 2005 A1
20050143759 Kelly Jun 2005 A1
20050149001 Uchikubo et al. Jul 2005 A1
20050149356 Cyr et al. Jul 2005 A1
20050165390 Mauti et al. Jul 2005 A1
20050192633 Montpetit Sep 2005 A1
20050203384 Sati et al. Sep 2005 A1
20050203504 Wham et al. Sep 2005 A1
20050222631 Dalal et al. Oct 2005 A1
20050228425 Boukhny et al. Oct 2005 A1
20050236474 Onuma et al. Oct 2005 A1
20050251233 Kanzius Nov 2005 A1
20050277913 McCary Dec 2005 A1
20060020272 Gildenberg Jan 2006 A1
20060025816 Shelton Feb 2006 A1
20060059018 Shiobara et al. Mar 2006 A1
20060079874 Faller et al. Apr 2006 A1
20060116908 Dew et al. Jun 2006 A1
20060136622 Rouvelin et al. Jun 2006 A1
20060184160 Ozaki et al. Aug 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
20070049947 Menn et al. Mar 2007 A1
20070078678 DiSilvestro et al. Apr 2007 A1
20070084896 Doll et al. Apr 2007 A1
20070167702 Hasser et al. Jul 2007 A1
20070168461 Moore Jul 2007 A1
20070173803 Wham et al. Jul 2007 A1
20070175955 Shelton et al. Aug 2007 A1
20070179482 Anderson Aug 2007 A1
20070179508 Arndt Aug 2007 A1
20070191713 Eichmann et al. Aug 2007 A1
20070203744 Scholl Aug 2007 A1
20070225556 Ortiz et al. Sep 2007 A1
20070225690 Sekiguchi et al. Sep 2007 A1
20070244478 Bahney Oct 2007 A1
20070249990 Cosmescu Oct 2007 A1
20070270660 Caylor et al. Nov 2007 A1
20070282195 Masini et al. Dec 2007 A1
20070282321 Shah et al. Dec 2007 A1
20070282333 Fortson et al. Dec 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
20080077158 Haider et al. Mar 2008 A1
20080083414 Messerges Apr 2008 A1
20080114350 Park et al. May 2008 A1
20080129465 Rao Jun 2008 A1
20080140090 Aranyi et al. Jun 2008 A1
20080177258 Govari et al. Jul 2008 A1
20080177362 Phillips et al. Jul 2008 A1
20080200940 Eichmann et al. Aug 2008 A1
20080234708 Houser et al. Sep 2008 A1
20080255413 Zemlok et al. Oct 2008 A1
20080262654 Omori et al. Oct 2008 A1
20080272172 Zemlok et al. Nov 2008 A1
20080281301 DeBoer et al. Nov 2008 A1
20080281678 Keuls et al. Nov 2008 A1
20080296346 Shelton, IV et al. Dec 2008 A1
20080306759 Ilkin et al. Dec 2008 A1
20080312953 Claus Dec 2008 A1
20090017910 Rofougaran et al. Jan 2009 A1
20090030437 Houser et al. Jan 2009 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
20090048589 Takashino et al. 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
20090217932 Voegele Sep 2009 A1
20090234352 Behnke et al. Sep 2009 A1
20090259149 Tahara et al. Oct 2009 A1
20090259221 Tahara et al. Oct 2009 A1
20090299214 Wu et al. Dec 2009 A1
20090307681 Armado et al. Dec 2009 A1
20090326321 Jacobsen et al. Dec 2009 A1
20090326336 Lemke et al. Dec 2009 A1
20100057106 Sorrentino et al. Mar 2010 A1
20100065604 Weng Mar 2010 A1
20100069939 Konishi Mar 2010 A1
20100069942 Shelton, IV Mar 2010 A1
20100070417 Flynn et al. Mar 2010 A1
20100120266 Rimborg May 2010 A1
20100132334 Duclos et al. Jun 2010 A1
20100137845 Ramstein et al. Jun 2010 A1
20100137886 Zergiebel et al. Jun 2010 A1
20100168561 Anderson Jul 2010 A1
20100179831 Brown et al. Jul 2010 A1
20100191100 Anderson et al. Jul 2010 A1
20100198200 Horvath Aug 2010 A1
20100198248 Vakharia Aug 2010 A1
20100217991 Choi Aug 2010 A1
20100234996 Schreiber et al. Sep 2010 A1
20100235689 Tian et al. Sep 2010 A1
20100250571 Pierce et al. Sep 2010 A1
20100258327 Esenwein et al. Oct 2010 A1
20100292535 Paskar Nov 2010 A1
20100292684 Cybulski et al. Nov 2010 A1
20110015627 DiNardo Jan 2011 A1
20110022032 Zemlok et al. Jan 2011 A1
20110071530 Carson Mar 2011 A1
20110077512 Boswell Mar 2011 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
20110152712 Cao et al. Jun 2011 A1
20110163147 Laurent et al. Jul 2011 A1
20110166883 Palmer et al. Jul 2011 A1
20110196398 Robertson et al. Aug 2011 A1
20110237883 Chun Sep 2011 A1
20110251612 Faller et al. Oct 2011 A1
20110264000 Paul et al. Oct 2011 A1
20110273465 Konishi et al. Nov 2011 A1
20110278343 Knodel et al. Nov 2011 A1
20110290024 Lefler Dec 2011 A1
20110295270 Giordano et al. Dec 2011 A1
20110306840 Allen et al. Dec 2011 A1
20120022519 Huang et al. Jan 2012 A1
20120029354 Mark et al. Feb 2012 A1
20120046662 Gilbert Feb 2012 A1
20120059684 Hampapur et al. Mar 2012 A1
20120078247 Worrell et al. Mar 2012 A1
20120080336 Shelton, IV et al. Apr 2012 A1
20120083786 Artale et al. Apr 2012 A1
20120116265 Houser et al. May 2012 A1
20120116381 Houser et al. May 2012 A1
20120116394 Timm et al. May 2012 A1
20120130217 Kauphusman et al. May 2012 A1
20120145714 Farascioni et al. Jun 2012 A1
20120172696 Kallback et al. Jul 2012 A1
20120190981 Harris et al. Jul 2012 A1
20120191091 Allen Jul 2012 A1
20120191162 Villa Jul 2012 A1
20120197619 Namer Yelin et al. Aug 2012 A1
20120203785 Awada Aug 2012 A1
20120211542 Racenet Aug 2012 A1
20120245958 Lawrence et al. Sep 2012 A1
20120253329 Zemlok et al. Oct 2012 A1
20120265555 Cappuzzo et al. Oct 2012 A1
20120292367 Morgan et al. Nov 2012 A1
20120319859 Taub et al. Dec 2012 A1
20130008677 Huifu Jan 2013 A1
20130024213 Poon Jan 2013 A1
20130046182 Hegg et al. Feb 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
20130096597 Anand et al. Apr 2013 A1
20130116218 Kaplan et al. May 2013 A1
20130144284 Behnke, II et al. Jun 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
20130267874 Marcotte et al. Oct 2013 A1
20130268283 Vann 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
20130331873 Ross et al. Dec 2013 A1
20130331875 Ross et al. Dec 2013 A1
20140001231 Shelton, IV et al. Jan 2014 A1
20140001234 Shelton, IV et al. Jan 2014 A1
20140005640 Shelton, IV et al. Jan 2014 A1
20140006132 Barker Jan 2014 A1
20140009894 Yu Jan 2014 A1
20140013565 MacDonald et al. Jan 2014 A1
20140029411 Nayak et al. Jan 2014 A1
20140033926 Fassel et al. Feb 2014 A1
20140035762 Shelton, IV et al. Feb 2014 A1
20140066700 Wilson et al. Mar 2014 A1
20140073893 Bencini Mar 2014 A1
20140074076 Gertner Mar 2014 A1
20140081255 Johnson et al. Mar 2014 A1
20140081659 Nawana et al. Mar 2014 A1
20140084949 Smith et al. Mar 2014 A1
20140087999 Kaplan et al. Mar 2014 A1
20140092089 Kasuya et al. Apr 2014 A1
20140107697 Patani et al. Apr 2014 A1
20140108035 Akbay et al. Apr 2014 A1
20140108983 William et al. Apr 2014 A1
20140148729 Schmitz et al. May 2014 A1
20140166724 Schellin et al. Jun 2014 A1
20140187856 Holoien et al. Jul 2014 A1
20140188440 Donhowe et al. Jul 2014 A1
20140194864 Martin et al. Jul 2014 A1
20140204190 Rosenblatt, III et al. Jul 2014 A1
20140226572 Thota et al. Aug 2014 A1
20140243799 Parihar Aug 2014 A1
20140243809 Gelfand et al. Aug 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
20140276749 Johnson Sep 2014 A1
20140303660 Boyden et al. Oct 2014 A1
20140303990 Schoenefeld et al. Oct 2014 A1
20140364691 Krivopisk et al. Dec 2014 A1
20150006201 Pait et al. Jan 2015 A1
20150025549 Kilroy et al. Jan 2015 A1
20150032150 Ishida et al. Jan 2015 A1
20150051452 Ciaccio Feb 2015 A1
20150051617 Takemura et al. Feb 2015 A1
20150053737 Leimbach et al. Feb 2015 A1
20150057675 Akeel et al. Feb 2015 A1
20150066000 An et al. Mar 2015 A1
20150070187 Wiesner et al. Mar 2015 A1
20150073400 Sverdlik et al. Mar 2015 A1
20150108198 Estrella Apr 2015 A1
20150133945 Dushyant et al. May 2015 A1
20150140982 Postrel May 2015 A1
20150145682 Harris May 2015 A1
20150148830 Stulen et al. May 2015 A1
20150173673 Toth et al. Jun 2015 A1
20150173756 Baxter, III et al. Jun 2015 A1
20150196295 Shelton, IV et al. Jul 2015 A1
20150199109 Lee Jul 2015 A1
20150208934 Sztrubel et al. Jul 2015 A1
20150237502 Schmidt et al. Aug 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
20150272694 Charles 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
20150324114 Hurley et al. Nov 2015 A1
20150328474 Flyash et al. Nov 2015 A1
20150332003 Stamm et al. Nov 2015 A1
20150332196 Stiller et al. Nov 2015 A1
20150335344 Aljuri et al. Nov 2015 A1
20160000437 Giordano et al. Jan 2016 A1
20160001411 Alberti 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
20160078190 Greene et al. Mar 2016 A1
20160106516 Mesallum Apr 2016 A1
20160106934 Hiraga et al. Apr 2016 A1
20160121143 Mumaw et al. May 2016 A1
20160158468 Tang et al. Jun 2016 A1
20160174998 Lal et al. Jun 2016 A1
20160180045 Syed Jun 2016 A1
20160184054 Lowe Jun 2016 A1
20160192960 Bueno et al. Jul 2016 A1
20160206202 Frangioni Jul 2016 A1
20160224760 Petak et al. Aug 2016 A1
20160225551 Shedletsky Aug 2016 A1
20160228204 Quaid et al. Aug 2016 A1
20160235303 Fleming et al. Aug 2016 A1
20160242836 Eggers et al. Aug 2016 A1
20160249910 Shelton, IV et al. Sep 2016 A1
20160278841 Panescu et al. Sep 2016 A1
20160287312 Tegg et al. Oct 2016 A1
20160287912 Warnking Oct 2016 A1
20160296246 Schaller Oct 2016 A1
20160302210 Thornton et al. Oct 2016 A1
20160310055 Zand et al. Oct 2016 A1
20160314716 Grubbs Oct 2016 A1
20160314717 Grubbs Oct 2016 A1
20160321400 Durrant et al. Nov 2016 A1
20160323283 Kang et al. Nov 2016 A1
20160331460 Cheatham, III et al. Nov 2016 A1
20160342753 Feazell Nov 2016 A1
20160342916 Arceneaux et al. Nov 2016 A1
20160345857 Jensrud et al. Dec 2016 A1
20160345976 Gonzalez et al. Dec 2016 A1
20160350490 Martinez et al. Dec 2016 A1
20160361070 Ardel et al. Dec 2016 A1
20160367305 Hareland Dec 2016 A1
20160374723 Frankhouser et al. Dec 2016 A1
20160374762 Case et al. Dec 2016 A1
20160379504 Bailey et al. Dec 2016 A1
20170000516 Stulen et al. Jan 2017 A1
20170000553 Wiener et al. Jan 2017 A1
20170027603 Pandey Feb 2017 A1
20170042604 McFarland et al. Feb 2017 A1
20170068792 Reiner Mar 2017 A1
20170079730 Azizian et al. Mar 2017 A1
20170086829 Vendely et al. Mar 2017 A1
20170086930 Thompson et al. Mar 2017 A1
20170105754 Boudreaux et al. Apr 2017 A1
20170116873 Lendvay et al. Apr 2017 A1
20170127499 Unoson et al. May 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
20170156076 Eom et al. Jun 2017 A1
20170164997 Johnson et al. Jun 2017 A1
20170165012 Chaplin et al. Jun 2017 A1
20170172550 Mukherjee et al. Jun 2017 A1
20170172565 Heneveld Jun 2017 A1
20170172614 Scheib et al. Jun 2017 A1
20170177807 Fabian Jun 2017 A1
20170196583 Sugiyama Jul 2017 A1
20170196637 Shelton, IV et al. Jul 2017 A1
20170202591 Shelton, IV et al. Jul 2017 A1
20170202595 Shelton, IV Jul 2017 A1
20170202607 Shelton, IV et al. Jul 2017 A1
20170202608 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
20170245809 Ma et al. Aug 2017 A1
20170249432 Grantcharov Aug 2017 A1
20170262604 Francois Sep 2017 A1
20170265864 Hessler et al. Sep 2017 A1
20170265943 Sela et al. Sep 2017 A1
20170273715 Piron et al. 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
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
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
20170312456 Phillips Nov 2017 A1
20170325876 Nakadate et al. Nov 2017 A1
20170325878 Messerly et al. Nov 2017 A1
20170360499 Greep et al. Dec 2017 A1
20170367583 Black et al. Dec 2017 A1
20170367695 Shelton, IV et al. Dec 2017 A1
20170367754 Narisawa Dec 2017 A1
20170367771 Tako et al. Dec 2017 A1
20170367772 Gunn et al. Dec 2017 A1
20170370710 Chen et al. Dec 2017 A1
20180008359 Randle Jan 2018 A1
20180011983 Zuhars et al. Jan 2018 A1
20180042659 Rupp 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
20180078170 Panescu et al. Mar 2018 A1
20180098816 Govari et al. Apr 2018 A1
20180110523 Shelton, IV Apr 2018 A1
20180116662 Shelton, IV et al. May 2018 A1
20180116735 Tierney et al. May 2018 A1
20180122506 Grantcharov et al. May 2018 A1
20180125590 Giordano et al. May 2018 A1
20180132895 Silver May 2018 A1
20180144243 Hsieh et al. May 2018 A1
20180153574 Faller et al. Jun 2018 A1
20180153628 Grover et al. Jun 2018 A1
20180153632 Tokarchuk et al. Jun 2018 A1
20180154297 Maletich 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
20180168590 Overmyer et al. Jun 2018 A1
20180168592 Overmyer et al. Jun 2018 A1
20180168597 Fanelli et al. Jun 2018 A1
20180168598 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
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
20180168633 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
20180177383 Noonan et al. Jun 2018 A1
20180206884 Beaupre Jul 2018 A1
20180206905 Batchelor et al. Jul 2018 A1
20180214025 Homyk et al. Aug 2018 A1
20180221005 Hamel et al. Aug 2018 A1
20180221598 Silver Aug 2018 A1
20180228557 Darisse et al. Aug 2018 A1
20180233222 Daley et al. Aug 2018 A1
20180235719 Jarc Aug 2018 A1
20180235722 Baghdadi et al. Aug 2018 A1
20180242967 Meade Aug 2018 A1
20180263710 Sakaguchi et al. Sep 2018 A1
20180268320 Shekhar Sep 2018 A1
20180271520 Shelton, IV et al. Sep 2018 A1
20180271603 Nir et al. Sep 2018 A1
20180289427 Griffiths et al. Oct 2018 A1
20180296286 Peine et al. Oct 2018 A1
20180303552 Ryan et al. Oct 2018 A1
20180304471 Tokuchi Oct 2018 A1
20180310935 Wixey Nov 2018 A1
20180310986 Batchelor et al. Nov 2018 A1
20180315492 Bishop et al. Nov 2018 A1
20180317826 Muhsin et al. Nov 2018 A1
20180333207 Moctezuma De la Barrera Nov 2018 A1
20180351987 Patel 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
20190000478 Messerly 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
20190025040 Andreason et al. Jan 2019 A1
20190036688 Wasily et al. Jan 2019 A1
20190038335 Mohr et al. Feb 2019 A1
20190038364 Enoki Feb 2019 A1
20190046198 Stokes et al. Feb 2019 A1
20190053801 Wixey et al. Feb 2019 A1
20190053866 Seow et al. Feb 2019 A1
20190069949 Vrba et al. Mar 2019 A1
20190069964 Hagn Mar 2019 A1
20190069966 Petersen et al. Mar 2019 A1
20190070550 Lalomia et al. Mar 2019 A1
20190070731 Bowling et al. Mar 2019 A1
20190083190 Graves et al. Mar 2019 A1
20190087544 Peterson Mar 2019 A1
20190104919 Shelton, IV et al. Apr 2019 A1
20190110828 Despatie Apr 2019 A1
20190110855 Barral et al. Apr 2019 A1
20190115108 Hegedus 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
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
20190125380 Hunter 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
20190159777 Ehrenfels et al. May 2019 A1
20190159778 Shelton, IV et al. May 2019 A1
20190162179 O'Shea et al. May 2019 A1
20190167296 Tsubuku et al. Jun 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 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
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
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
20190201144 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
20190207911 Wiener et al. Jul 2019 A1
20190208641 Yates et al. Jul 2019 A1
20190224434 Silver et al. Jul 2019 A1
20190254759 Azizian Aug 2019 A1
20190261984 Nelson et al. Aug 2019 A1
20190269476 Bowling et al. Sep 2019 A1
20190272917 Couture 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
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 Cut 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
20190278262 Taylor et al. Sep 2019 A1
20190282311 Nowlin 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
20190311802 Kokubo et al. Oct 2019 A1
20190314015 Shelton, IV et al. Oct 2019 A1
20190314016 Huitema et al. Oct 2019 A1
20190314081 Brogna Oct 2019 A1
20190321117 Itkowitz et al. Oct 2019 A1
20190333626 Mansi et al. Oct 2019 A1
20190343594 Garcia Kilroy et al. Nov 2019 A1
20190374140 Tucker et al. Dec 2019 A1
20200000470 Du et al. Jan 2020 A1
20200000509 Hayashida et al. Jan 2020 A1
20200038120 Ziraknejad et al. Feb 2020 A1
20200046353 Deck et al. Feb 2020 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
20200054326 Harris et al. Feb 2020 A1
20200054328 Harris et al. Feb 2020 A1
20200054330 Harris et al. Feb 2020 A1
20200078070 Henderson et al. Mar 2020 A1
20200078071 Asher Mar 2020 A1
20200078076 Henderson et al. Mar 2020 A1
20200078077 Henderson et al. Mar 2020 A1
20200078078 Henderson et al. Mar 2020 A1
20200078079 Morgan et al. Mar 2020 A1
20200078080 Henderson et al. Mar 2020 A1
20200078081 Jayme et al. Mar 2020 A1
20200078082 Henderson et al. Mar 2020 A1
20200078089 Henderson et al. Mar 2020 A1
20200078096 Barbagli et al. Mar 2020 A1
20200078106 Henderson et al. Mar 2020 A1
20200078110 Henderson et al. Mar 2020 A1
20200078111 Oberkircher et al. Mar 2020 A1
20200078112 Henderson et al. Mar 2020 A1
20200078113 Sawhney et al. Mar 2020 A1
20200078114 Asher et al. Mar 2020 A1
20200078115 Asher et al. Mar 2020 A1
20200078116 Oberkircher et al. Mar 2020 A1
20200078117 Henderson et al. Mar 2020 A1
20200078118 Henderson et al. Mar 2020 A1
20200078119 Henderson et al. Mar 2020 A1
20200078120 Aldridge et al. Mar 2020 A1
20200081585 Petre et al. Mar 2020 A1
20200090808 Carroll et al. Mar 2020 A1
20200100825 Henderson et al. Apr 2020 A1
20200100830 Henderson et al. Apr 2020 A1
20200106220 Henderson et al. Apr 2020 A1
20200162896 Su et al. May 2020 A1
20200168323 Bullington et al. May 2020 A1
20200178760 Kashima et al. Jun 2020 A1
20200178971 Harris et al. Jun 2020 A1
20200214699 Shelton, IV et al. Jul 2020 A1
20200237372 Park Jul 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
20200275928 Shelton, IV et al. Sep 2020 A1
20200275930 Harris et al. Sep 2020 A1
20200281665 Kopp Sep 2020 A1
20200305924 Carroll Oct 2020 A1
20200305945 Morgan et al. Oct 2020 A1
20200314569 Morgan et al. Oct 2020 A1
20200405375 Shelton, IV et al. Dec 2020 A1
20210000555 Shelton, IV et al. Jan 2021 A1
20210007760 Reisin Jan 2021 A1
20210015568 Liao et al. Jan 2021 A1
20210022731 Eisinger Jan 2021 A1
20210022738 Weir et al. Jan 2021 A1
20210022809 Crawford et al. Jan 2021 A1
20210059674 Shelton, IV et al. Mar 2021 A1
20210068834 Shelton, IV et al. Mar 2021 A1
20210128149 Whitfield et al. May 2021 A1
20210153889 Nott et al. May 2021 A1
20210169516 Houser et al. Jun 2021 A1
20210176179 Shelton, IV Jun 2021 A1
20210177452 Nott et al. Jun 2021 A1
20210177489 Yates et al. Jun 2021 A1
20210192914 Shelton, IV et al. Jun 2021 A1
20210201646 Shelton, IV et al. Jul 2021 A1
20210205020 Shelton, IV et al. Jul 2021 A1
20210205021 Shelton, IV et al. Jul 2021 A1
20210205028 Shelton, IV et al. Jul 2021 A1
20210205029 Wiener et al. Jul 2021 A1
20210205030 Shelton, IV et al. Jul 2021 A1
20210205031 Shelton, IV et al. Jul 2021 A1
20210212602 Shelton, IV et al. Jul 2021 A1
20210212694 Shelton, IV et al. Jul 2021 A1
20210212717 Yates et al. Jul 2021 A1
20210212719 Houser et al. Jul 2021 A1
20210212770 Messerly et al. Jul 2021 A1
20210212771 Shelton, IV et al. Jul 2021 A1
20210212774 Shelton, IV et al. Jul 2021 A1
20210212775 Shelton, IV et al. Jul 2021 A1
20210212782 Shelton, IV et al. Jul 2021 A1
20210219976 DiNardo et al. Jul 2021 A1
20210220058 Messerly et al. Jul 2021 A1
20210240852 Shelton, IV et al. Aug 2021 A1
20210241898 Shelton, IV et al. Aug 2021 A1
20210249125 Morgan et al. Aug 2021 A1
20210251487 Shelton, IV et al. Aug 2021 A1
20210259697 Shelton, IV et al. Aug 2021 A1
20210259698 Shelton, IV et al. Aug 2021 A1
20210282780 Shelton, IV et al. Sep 2021 A1
20210282781 Shelton, IV et al. Sep 2021 A1
20210315579 Shelton, IV et al. Oct 2021 A1
20210315580 Shelton, IV et al. Oct 2021 A1
20210315581 Shelton, IV et al. Oct 2021 A1
20210315582 Shelton, IV et al. Oct 2021 A1
20210322014 Shelton, IV et al. Oct 2021 A1
20210322015 Shelton, IV et al. Oct 2021 A1
20210322017 Shelton, IV et al. Oct 2021 A1
20210322018 Shelton, IV et al. Oct 2021 A1
20210322019 Shelton, IV et al. Oct 2021 A1
20210322020 Shelton, IV et al. Oct 2021 A1
20210336939 Wiener et al. Oct 2021 A1
Foreign Referenced Citations (65)
Number Date Country
2015201140 Mar 2015 AU
2795323 May 2014 CA
101617950 Jan 2010 CN
104490448 Mar 2017 CN
206097107 Apr 2017 CN
108652695 Oct 2018 CN
2037167 Jul 1980 DE
3016131 Oct 1981 DE
3824913 Feb 1990 DE
4002843 Apr 1991 DE
102005051367 Apr 2007 DE
102016207666 Nov 2017 DE
0000756 Oct 1981 EP
0408160 Jan 1991 EP
0473987 Mar 1992 EP
0929263 Jul 1999 EP
1214913 Jun 2002 EP
2732772 May 2014 EP
2942023 Nov 2015 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
2838234 Oct 2003 FR
2509523 Jul 2014 GB
S5373315 Jun 1978 JP
2001029353 Feb 2001 JP
2007123394 May 2007 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-03079909 Oct 2003 WO
WO-2007137304 Nov 2007 WO
WO-2008053485 May 2008 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-2014031800 Feb 2014 WO
WO-2014071184 May 2014 WO
WO-2014134196 Sep 2014 WO
WO-2015129395 Sep 2015 WO
WO-2016100719 Jun 2016 WO
WO-2016118752 Jul 2016 WO
WO-2016206015 Dec 2016 WO
WO-2017011382 Jan 2017 WO
WO-2017011646 Jan 2017 WO
WO-2017058617 Apr 2017 WO
WO-2017058695 Apr 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-2018116247 Jun 2018 WO
WO-2018152141 Aug 2018 WO
WO-2018176414 Oct 2018 WO
Non-Patent Literature Citations (55)
Entry
US 10,504,709, 08/2018, 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.
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.
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.
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).
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%20Of%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.Mar. 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).
Anonymous, “Internet of Things Powers Connected Surgical Device Infrastructure Case Study”, Dec. 31, 2016 (Dec. 31, 2016), Retrieved from the Internet: URL:https://www.cognizant.com/services-resources/150110_IoT_connected_surgical_devices.pdf.
Draijer, Matthijs et al., “Review of laser pseckle contrast techniques for visualizing tissue perfusion,” Lasers in Medical Science, Springer-Verlag, LO, vol. 24, No. 4, Dec. 3, 2008, pp. 639-651.
Roy D Cullum, “Handbook of Engineering Design”, ISBN: 9780408005586, Jan. 1, 1988 (Jan. 1, 1988), XP055578597, ISBN: 9780408005586, 10-20, Chapter 6, p. 138, right-hand column, paragraph 3.
“Surgical instrumentation: the true cost of instrument trays and a potential strategy for optimization”; Mhlaba et al.; Sep. 23, 2015 (Year: 2015).
Nabil Simaan et al., “Intelligent Surgical Robots with Situational Awareness: From Good to Great Surgeons”, DOI: 10.1115/1.2015-Sep-6 external link, Sep. 2015 (Sep. 2015), p. 3-6, Retrieved from the Internet: URL:http://memagazineselect.asmedigitalcollection.asme.org/data/journals/meena/936888/me-2015-sep6.pdf XP055530863.
Anonymous: “Titanium Key Chain Tool 1.1, Ultralight Multipurpose Key Chain Tool, Forward Cutting Can Opener—Vargo Titanium,” vargooutdoors.com, Jul. 5, 2014 (Jul. 5, 2014), retrieved from the internet: https://vargooutdoors.com/titanium-key-chain-tool-1-1.html.
Anonymous: “Screwdriver—Wikipedia”, en.wikipedia.org, Jun. 23, 2019, XP055725151, Retrieved from the Internet: URL:https://en.wikipedia.org/w/index.php?title=Screwdriver&oldid=903111203 [retrieved on Mar. 20, 2021].
Nordlinger, Christopher, “The Internet of Things and the Operating Room of the Future,” May 4, 2015, https://medium.com/@chrisnordlinger/the-internet-of-things-and-the-operating-room-of-the-future-8999a143d7b1, retrieved from the internet on Apr. 27, 2021, 9 pages.
Screen captures from YouTube video clip entitled “Four ways to use the Lego Brick Separator Tool,” 2 pages, uploaded on May 29, 2014 by user “Sarah Lewis”. Retrieved from internet: https://www.youtube.com/watch?v=ucKiRD6U1LU (Year: 2014).
Sorrells, P., “Application Note AN680. Passive RFID Basics,” retrieved from http://ww1.microchip.com/downloads/en/AppNotes/00680b.pdf on Feb. 26, 2020, Dec. 31, 1998, pp. 1-7.
Related Publications (1)
Number Date Country
20190274708 A1 Sep 2019 US
Provisional Applications (1)
Number Date Country
62640415 Mar 2018 US