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 .
Response to Amendment
This action is responsive to the amendments filed 12/5/25. Claims 1, 3, 9, 15, 17-20 are amended. Claims 1-20 remain pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: computing device in claim 1, 9, 17, interpreted as a controller as indicated in the specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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.
Claim(s) 1, 3-4, 6-7, 9, 11-12, 14-15, 17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asher et al. [US 2018/0333185 A1, hereinafter “Asher”] in view of Wiener et al. [US 2009/0036913 A1, hereinafter “Wiener”].
Regarding claim 1, Asher teaches a computer-implemented method [Fig. 17] for controlling an ultrasonic surgical system [Fig. 1], the computer-implemented method comprising:
activating [Step 812] an ultrasonic surgical system including an ultrasonic generator [14, Fig. 1], an ultrasonic transducer [24], and an ultrasonic blade [28], wherein, when the ultrasonic surgical system is activated, the ultrasonic generator produces a drive signal for sealing a vessel in contact with the ultrasonic blade, wherein the drive signal drives the ultrasonic transducer which, in turn, produces ultrasonic energy that is transmitted to the ultrasonic blade for sealing the vessel [Steps 838, 842, or 846], the vessel defining a vessel size [small, medium, or large; see Par. 0085];
collecting data from the ultrasonic surgical system [it is following step 812], the data including at least one electrical parameter [RF impedance, Step 820.];
communicating the data to at least one machine learning algorithm [Step 834, Par. 0083, a support vector machine];
determining, using the at least one machine learning algorithm, the vessel size based upon the data [step 834, Par. 0083];
communicating the vessel size to a computing device [controller 46, equivalent to the claimed computing device] associated with the ultrasonic generator; and
determining, based on the vessel size, when to stop generating, by the ultrasonic generator, the drive signal for sealing the vessel [see Fig. 17, the steps following step 834].
Asher fails to disclose the data including at least one electrical parameter of the ultrasonic transducer or the ultrasonic generator producing the drive signal as now claimed.
However, Wiener teaches, in in a method for controlling an ultrasonic surgical system: collecting data from the activated [Par. 0107] ultrasonic surgical system ["surgical device 1300 configured to derive end effector feedback considering a coefficient of collagen denaturation (CCD)," Par. 0100. "the CCD may be calculated as a function of variables, for example, including power provided to the end effector 1308,” Par. 0101], the data including at least one electrical parameter of the ultrasonic transducer or the ultrasonic generator producing the drive signal [“the power provided to the end effector [which is used to determine the CCD, analysis of which provides information on the tissue size, Par. 0100] 1308 may be found by considering the electrical signal between the generator 1304 and the transducer 1306 while the end effector 1308 is under load,” Par. 0101], and determining the vessel size based on this data ["Analysis of a CCD curve taken over the course of a cutting and/or coagulation procedure may provide information about… the tissue portion including...its thickness and outside diameter,” Par. 0100].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method of Asher by configuring the step of collecting data to such that said data includes at least one electrical parameter of the ultrasonic transducer or the ultrasonic generator producing the drive signal, as taught by Weiner, because this allows for a wider variety of feedback including information about the progress of the cutting/coagulation process [Wiener Pars. 0098-0099].
Regarding claim 9, Asher-Wiener discloses the computer-implemented method set forth with respect to claim 1 above. Asher further discloses the system performing the method including the ultrasonic generator and other elements as set forth with respect to claim 1 above. Asher discloses the processor [Par. 0043] and memory [inherently; because the controller performs the process, Par. 0043, it must have memory] having instructions thereon to implement the above method as set forth with respect to claim 1.
Regarding claim 17, Asher-Wiener discloses the computer-implemented method set forth with respect to claim 1 above. Asher further discloses a non-transitory storage medium that stores a program causing a computer to execute the above method [Par. 0043. Note that the processor, in executing the above method, inherently requires a non transitory storage medium that stores the above program].
Regarding claims 3, 11, and 19: Asher discloses the data including the impedance, but fails to disclose, in the above embodiment, the use of the impedance phase. However, in the embodiment of Fig. 8, Asher teaches using impedance phase to determine vessel size (Zph) [Par. 0061]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method of Fig. 17 by using Zph to determine vessel size, as taught by Asher in Fig. 8, because this easily allows small, medium and large vessels to be differentiated [Par. 0061].
Regarding claim 4, 12, and 20: the modified Asher discloses the method/system as set forth above including the machine learning algorithm as set forth above. Asher primarily discusses a support vector machine. However, in another embodiment, Asher teaches that the machine learning algorithm may be a neural network [Par. 0062]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the apparatus or method of Asher, first embodiment, in view of that of the second embodiment to use a machine learning algorithm which includes a neural network, because this amounts to a simple substitution of one type of machine learning algorithm known in the art with predictable results [Asher explicitly discloses the neural network being a suitable machine learning algorithm in the invention].
Regarding claim 6 and 14: Asher further teaches training the neural network using one or more of accessing ultrasonic surgical system data or identifying patterns in data [identifying patterns: Par. 0062 describe how the system is trained on a variety of tissue sizes]. The modification of using the neural network would have been obvious for the reasons set forth above, and once modified, one of ordinary skill would reasonably be apprised of the benefits of training the neural network by accessing ultrasonic surgical system data or identifying patterns as claimed.
Regarding claim 7 and 15: Asher teaches training the neural network using training data including, Zph [see Pars. 0061 and 0062]. Asher teaches training the network, and using Zph to determine the vessel size. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system/method of Asher by training the neural network using Zph as part of the training data because this would allow the system to correlate Zph and vessel size in order to achieve the desired result of determining tissue size based on Zph.
Claim(s) 2, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asher in view of Wiener, as applied to claim 1, 9, or 17, and further in view of Wiener et al. [US 20170000553 A1, hereinafter “Wiener 2017”].
Regarding claim 2, 10, and 18: the modified Asher discloses the method/system as set forth above. Asher further discloses determining when to stop generating, by the ultrasonic generator, the drive signal for sealing the vessel [see claim 1 above]; but fails to teach the second drive signal for cutting. However, Weiner 2017 teaches, in a method/system for controlling an ultrasonic surgical system, after a step of sealing [Steps 2504, Fig. 45; note that the coagulation results in sealing, Par. 0004] and determining when to stop the seal drive signal [Steps 2506-2508],
generating, by the ultrasonic generator, a second drive signal for cutting the vessel, based on the determining [Step 2508]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method/system of Asher by adding to the control method a step of generating, by the ultrasonic generator, a second drive signal for cutting the vessel, based on the determining, as taught by Wiener, in order to allow the system to be used in operations where both sealing and cutting are desired, and to both seal and cut in a controllable and precise manner [Wiener 2017 Par. 0004].
Claim(s) 5, 8, 13, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asher in view of Wiener, as applied to claim 4, 7, 12, or 15, above, and further in view of Georgescu et al. [US 2016/0174902 A1, hereinafter “Georgescu”].
Regarding claim 5 and 13: the modified Asher discloses the method/system as set forth above including the machine learning algorithm and the teaching of the neural network, but fails to disclose the neural network includes at least one of a temporal convolutional network or a feed-forward network. However, Georgescu teaches, in a neural network for a medical system/method, the neural network includes a feed-forward network [Par. 0044]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method/system of Asher by using a neural network including a feed-forward network as taught by Georgescu because this structure has “an efficient training algorithm” and is “powerful enough o approximate complicated target functions” [Georgescu Par. 0044].
Regarding claim 8 and 16: Asher as modified above teaches the method/system as set forth above including the machine learning algorithm and training, but fails to disclose the training includes at least one of supervised training, unsupervised training, or reinforcement learning. However, Georgescu teaches, in a neural network for a medical system/method, the training includes at least one of supervised training, unsupervised training [“ unsupervised pre-training followed by supervised fine-tuning,” Par. 0044]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system/method of Asher such that the training includes supervised and unsupervised training as taught by Georgescu because this “can be used to overcome an over-fitting issue” in a case where a feed-forward neural network is used (which in turn has the advantages set forth with respect to claims 5/13 above) [Georgescu Par. 0044].
Response to Arguments
Applicant's arguments filed 12/5/25 have been fully considered but they are not persuasive. While the examiner agrees that Asher fails to teach the narrower requirement of data including an electrical parameter of the u/s transducer or generator, the examiner finds that Wiener teaches this feature. Applicant’s arguments that Wiener fails to teach the amended portion are not persuasive as these appear to refer to the Wiener 2017 reference, not the Wiener 2009 reference, both of which have been cited in previous office actions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN MCGRATH whose telephone number is (571)270-0674. The examiner can normally be reached M-F 9 am to 5 pm ET.
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/ERIN MCGRATH/Primary Examiner, Art Unit 3771