Surgical generator for ultrasonic and electrosurgical devices
First Claim
Patent Images
1. A surgical system, comprising:
- a surgical device, wherein the surgical device comprises an end effector to treat tissue;
a surgical generator in communication with the surgical device, the surgical generator comprising at least one programmable logic device, wherein the at least one programmable logic device is programmed to;
provide a drive signal to drive the end effector;
receive a frequency of the drive signal; and
determine a temperature at the end effector considering the frequency of the drive signal and executing a neural network, wherein executing the neural network comprises executing a first hidden layer node and a second hidden layer node;
wherein the first hidden layer node is programmed to;
receive a set of input variables selected from a portion of a plurality of input variables, the selected set of input variables including the frequency of the drive signal;
weight at least a portion of the input variables selected from the set of input variables to generate a weighted set of input variables;
sum the weighted set of input variables to generate a first hidden layer node output; and
wherein the second hidden layer node is programmed to;
receive a set of outputs from hidden layer nodes, wherein the set of outputs from the hidden layer nodes comprises the first hidden layer node output;
weight at least a portion of the set of outputs from the hidden layer nodes to generate a weighted set of outputs from the hidden layer nodes;
sum the weighted set of outputs from the hidden layer nodes; and
apply a transform function to the sum of the weighted set of outputs from the hidden layer nodes to generate a second layer node output.
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Abstract
A method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal.
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Citations
26 Claims
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1. A surgical system, comprising:
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a surgical device, wherein the surgical device comprises an end effector to treat tissue; a surgical generator in communication with the surgical device, the surgical generator comprising at least one programmable logic device, wherein the at least one programmable logic device is programmed to; provide a drive signal to drive the end effector; receive a frequency of the drive signal; and determine a temperature at the end effector considering the frequency of the drive signal and executing a neural network, wherein executing the neural network comprises executing a first hidden layer node and a second hidden layer node; wherein the first hidden layer node is programmed to; receive a set of input variables selected from a portion of a plurality of input variables, the selected set of input variables including the frequency of the drive signal; weight at least a portion of the input variables selected from the set of input variables to generate a weighted set of input variables; sum the weighted set of input variables to generate a first hidden layer node output; and wherein the second hidden layer node is programmed to; receive a set of outputs from hidden layer nodes, wherein the set of outputs from the hidden layer nodes comprises the first hidden layer node output; weight at least a portion of the set of outputs from the hidden layer nodes to generate a weighted set of outputs from the hidden layer nodes; sum the weighted set of outputs from the hidden layer nodes; and apply a transform function to the sum of the weighted set of outputs from the hidden layer nodes to generate a second layer node output. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method of operating a surgical device, the method comprising:
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providing a drive signal to the surgical device to drive an end effector of the surgical device; receiving a frequency of the drive signal; and determining a temperature at the end effector considering the frequency of the drive signal, wherein determining the temperature at the end effector comprises; receiving a set of input variables selected from a portion of a plurality of input variables, the selected set of input variables including the frequency of the drive signal; weighting at least a portion of the input variables selected from the set of input variables to generate a weighted set of input variables; summing the weighted set of input variables to generate a first hidden layer node output; receiving a set of outputs from hidden layer nodes, wherein the set of outputs from the hidden layer nodes comprises the first hidden layer node output; weighting at least a portion of the set of outputs from the hidden layer nodes to generate a weighted set of outputs from the hidden layer nodes; summing the weighted set of outputs from the hidden layer nodes; and applying a transform function to the sum of the weighted set of outputs from the hidden layer nodes to generate a second layer node output. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A surgical system, comprising:
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a surgical device, wherein the surgical device comprises an end effector to treat tissue; and a surgical generator in communication with the surgical device, the surgical generator comprising at least one programmable logic device programmed to; provide a drive signal to drive the end effector; receive a frequency of the drive signal; and determine a temperature at the end effector considering the frequency of the drive signal and training a neural network model of end effector temperature, wherein training the neural network model comprises; providing to the model a set of input values including the frequency of the drive signal and changing or varying weight values w, bias values b, and transform functions f of the set of input values such that an output temperature of the neural network model approximates a measured dependency of the output temperature for known values of the set of input values; comparing the output temperature of the neural network model to a known output temperature associated with the set of input values; and modifying the weight values w, the bias values b, or the transform functions f of the set of input values until an error between the output temperature of the neural network model and a corresponding measured output temperature is below a predetermined error level. - View Dependent Claims (23, 24, 25, 26)
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Specification