DETAILED ACTION
A complete action on the merits of pending claims 21-40 appears below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-39 are rejected under 35 U.S.C. 103 as being unpatentable over Schulte US 20130296908 in view of Harper US 20110118736.
Regarding claims 21 and 31, Schulte teaches a processor configured to operably couple to the electrosurgical generator; at least one memory comprising a non-transitory storage medium that stores a program causing the processor (par. [0178] generator having modules stored on memory) to execute a computer-implemented method for controlling delivery of electrosurgical energy to seal the vessel (par. [0181] the modules are executed by a processor to have the generator output current, voltage, and frequency); and a machine learning algorithm stored in the at least one memory (par. [0487] neural network 3150); wherein, during operation of the electrosurgical generator delivering the electrosurgical energy, the computer-implemented method causes the processor to: collect data comprising at least one parameter associated with the delivery of the electrosurgical energy (par. [0216] impedance measuring, par. [0453] the tracked parameter can be a time period, temperature, or impedance); determine that the vessel is not adequately sealed based on the real-time burst pressure probability; determine an energy-delivery algorithm for sealing the vessel based on the determination that the vessel is not adequately sealed; and controllably operate the electrosurgical generator to deliver additional electrosurgical energy according to the energy-delivery algorithm to seal the vessel (par. [0461] neural network used to determine output of drive signal and if the seal/transection has occurred).
Schulte does not explicitly teach estimate a real-time burst pressure probability of the vessel. However, Schulte teaches using the neural network to determine the probability of an outcome like seal/transection (par. [0493]) and burst pressure is used to determine seal quality (par. [0449]).
Harper, in an analogous device, teaches predicting burst pressure off of pressure needed and size of the vessel (par. [0037]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the device of Schulte to predict burst pressure as in Harper. The higher the burst pressure the greater the chances of a seal, and the probability of a seal can be adjusted accordingly (Schulte par. [0036]).
Regarding claim 22, Schulte teaches wherein the machine learning algorithm comprises a neural network (par. [0461] neural network 3150).
Regarding claim 23, Schulte teaches wherein the neural network comprises at least one of a feed-forward network, a convolutional network, or a recurrent network (par. [0490] forward neural network).
Regarding claim 24, Schulte teaches further comprising a field-programmable gate array (par. [0598] the implementation can be with field programmable gate arrays).
Regarding claim 25, Harper teaches wherein the computer-implemented method further causes the processor to estimate the real-time burst pressure probability by: constructing a representation of the vessel based on the collected data; and performing an action on the representation of the vessel, the action comprising at least one of applying energy, ceasing application of energy, or changing application of energy (par. [0030] taking the inputs of seal pressure, vessel size, etc. to configure an electrical output).
Regarding claims 26 and 32, Schulte teaches wherein the at least one parameter comprises at least one of an impedance, a vessel temperature, a vessel mass, a vessel surface area, or an accumulated energy (par. [0453] the tracked parameter can be a time period, temperature, or impedance).
Regarding claims 27, 33, 34, and 35, Schulte teaches wherein the real-time burst pressure probability is a scaler value and at least partially determined by: when a temperature of the vessel is within a first temperature range for a first predetermined period of time (par. [0453] the tracked parameter can be a time period, temperature, or impedance for the algorithm) for protein denaturing (sealing tissue is protein denaturing), increasing the real-time burst pressure probability by a first amount (par. [0449] burst pressure increases); when the temperature of the vessel is within a second temperature range for a second predetermined period of time for a predetermined percentage of water to be removed (water removal is part of sealing), increasing the real-time burst pressure probability by a second amount; and when the temperature of the vessel is a third temperature range for a third predetermined period of time for allowing a thermoset gelatin to congeal and jaws to cool (gelatin congealing is part of tissue sealing), increasing the real- time burst pressure probability by a third amount (pars. [0454] and [0455] three different drive signals based upon time, par. [0453] the tracked parameter can be temperature instead of time).
Regarding claim 28, Schulte teaches the controller module of claim 21 (rejection of claim 21 above); and a connector port configured to couple to an instrument for delivering electrosurgical energy to the vessel (par. [0164] connects for cable 22).
Regarding claim 29, Schulte teaches the controller module of claim 21 (see rejection of claim 21 above); a first electrosurgical generator communicatively coupled to the controller module and configured to supply electrosurgical energy to a first vessel; and a second electrosurgical generator communicatively coupled to the controller module and configured to supply electrosurgical energy to a second vessel (par. [0161] generator provides RF energy and ultrasonic energy, two different energy modalities from two different generators even if housed together).
Regarding claim 30, the combination of Schulte and Harper teaches wherein the controller module is configured to estimate, via the machine learning algorithm (Schulte par. [0493] the neural network to determine the probability of an outcome like seal/transection), a real-time burst pressure probability for the each of the first vessel and the second vessel during operation of the respective first electrosurgical generator and second electrosurgical generator (Harper par. [0037] burst probability based on pressure and vessel size).
Regarding claims 36 and 37, Schulte does not explicitly teach further comprising comparing the real-time burst pressure probability with a threshold value and wherein the threshold value is a 95% probability of bursting at a pressure of 360 mmHg. However,
Harper further teaches 95% probability of bursting at a pressure of 360 mmHg (par. [0035]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the method of Schulte to determine a 95% probability of bursting at a pressure of 360 mmHg, as in Harper. 360 mmHg is seen as the minimum seal pressure make produce an adequate tissue seal (Harper par. [0035]).
Regarding claim 38, Harper teaches further comprising determining that the vessel is adequately sealed when the real-time burst pressure probability meets or exceeds the threshold value (par. [0035] vessel is sealed with burst pressure greater than 360 mmHg).
Regarding claim 39, Schulte teaches further comprising training the machine learning algorithm via an external neural network (par. [0471] receiving variables from other models).
Claim 40 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Schulte and Harper as applied to claim 39 above, and further in view of Schulman US 20120104311.
Regarding claim 40, Schulte and Harper do not explicitly teach wherein the training comprises reinforcement learning including a reward value and a punishment value, the reward value comprising a threshold burst pressure probability value, and the punishment value comprising a threshold impedance value. However, Schulte teaches impedance thresholds (par. [0196]) and where scaled values of 0 and 1 are used by the neural network (par. [0461]).
Schulman, in an analogous method, teaches reinforcement learning with rewards and punishments based upon different parameters (par. [0046]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the method of Schulte and Harper to use reward and punishment learning methods, as in Schulman. This type of reinforced learning allows for adjusted weighting associated with the neural network (par. [0046]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN T. CLARK whose telephone number is (408)918-7606. The examiner can normally be reached Monday-Friday 7AM-3PM MT.
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/R.T.C./Examiner, Art Unit 3794
/JOSEPH A STOKLOSA/Supervisory Patent Examiner, Art Unit 3794
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