DETAILED ACTION
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 in response to the preliminary amendment filed on 9/20/2023. In the amendment, claims 1 and 8 have been amended.
Drawings
The drawings are objected to because the Figures throughout contain choppy lines/outlines that make many of the Figures unclear. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4-7, & 9-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wiener et al. (US Pub. No. 2019/0282292 A1).
Regarding claim 1, Wiener et al. disclose a temperature control method (discussed in paragraphs [0257]-[0265]) for a blade shaft of an ultrasonic scalpel (broadly referred to as ‘ultrasonic surgical device 104’ in the method described in paragraphs [0257]-[0265], the ultrasonic surgical device 104 is further described to include an end effector 126 comprising an ultrasonic blade 151 discussed throughout, in particular [0137]) based on a temperature distribution function model (‘artificial neural network’ - paragraph [0257] and on), comprising the following steps: saving the temperature distribution function model (neural network may be implemented in processor 174 and/or programmable logic device 166 of generator 102 - paragraph [0258]) and at least one threshold value (T.sub.th - discussed at least in paragraph [0265]); inputting a corresponding input feature to the temperature distribution function model (inputs in the neural network discussed through at least paragraphs [0257]-[0259]), and outputting corresponding temperature data information (outputs in the neural network discussed through at least paragraphs [0257]-[0260]); comparing at least one value in the temperature data information with the threshold value (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]); and adjusting, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate a temperature of the blade shaft of the ultrasonic scalpel (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’).
Regarding claim 2, Wiener et al. further disclose wherein the temperature distribution function model is a neural network algorithm model (paragraph [0257]), comprising one or a combination of more than one algorithm models of a feedforward neural network (paragraph [0258] discusses layers/hidden layers associated with a feedforward neural network), a memory neural network, and an attention neural network; and a training method for the model is one or a combination of more than one of a supervised learning, a semi-supervised learning, an unsupervised learning, and a reinforcement learning (reinforcement described in paragraph [0263]).
Regarding claim 4, Wiener et al. further disclose wherein the input feature of the temperature distribution function model comprises one or a combination of more than one of a working feedback parameter (feedback discussed in paragraph [0265]; other parameters may also be discussed throughout the control method discussed in paragraphs [0257]-[0265]), a physical structure feature parameter (paragraph [0262] - ‘characteristics of the end effector…’), and an environmental parameter.
Regarding claim 5, Wiener et al. further disclose wherein the working feedback parameter comprises, but is not limited to a real-time voltage U, a real-time current I, a power P, an impedance R, and a real-time response frequency f (frequency - paragraph [0262]); the physical structure feature parameter comprises, but is not limited to a material of the blade shaft of the ultrasonic scalpel (material - paragraph [0262]), and a length of the blade shaft; and the environmental parameter comprises, but is not limited to an environmental temperature, and an environmental humidity (NOTE: the environmental parameter was part of a list of alternate or combined inputs in claim 4, therefore, it is not required by the prior art if the prior art met the other of the inputs listed, which is the case here).
Regarding claim 6, Wiener et al. further disclose wherein the temperature data information comprises a real-time temperature value at any point on the blade shaft of the ultrasonic scalpel, and/or a maximum temperature value, a minimum temperature value, and an average temperature of a certain area on the blade shaft of the ultrasonic scalpel (temperature data discussed throughout paragraphs [0264]-[0265]).
Regarding claim 7, Wiener et al. further disclose wherein the temperature distribution function model consists of layers, corresponding neurons, and weights; wherein weight parameters and application programs are saved in a memory of a generator 102 (paragraphs [0150]-[0154]); the memory comprises a flash, an EEPROM, or another non-volatile storage device (paragraph [0153]); the application program runs in a processor; and the processor comprises an ARM, a DSP, a FPGA, a CPU, a GPU, or an ASIC chip existing in the generator (paragraph [0152]), or is a remote server connected through a network.
Regarding claim 9, Wiener et al. disclose a temperature control system (including generator 102, artificial neural network described throughout paragraphs [0257]-[0264]; and processor described in paragraph [0152]) for a blade shaft of an ultrasonic scalpel (broadly referred to as ‘ultrasonic surgical device 104’ in the method described in paragraphs [0257]-[0265], the ultrasonic surgical device 104 is further described to include an end effector 126 comprising an ultrasonic blade 151 discussed throughout, in particular [0137]) based on a temperature distribution function model (‘artificial neural network’ - paragraph [0257] and on), comprising: a storage unit (memory of generator 102 - paragraphs [0150]-[0154]), configured to save the temperature distribution function model and at least one threshold value (threshold value of user-inputted temperature threshold - discussed throughout [0257]-[0265], but in particular in paragraph [0265]); a processing unit (paragraph [0258]), configured to input a corresponding input feature to the temperature distribution function model (‘the neural network may be implemented in the processor 174’ - paragraph [0258]), and output corresponding temperature data information (neural network comprises output layer 1210 - paragraph [0258]); a comparison unit (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]), configured to compare at least one value in the temperature data information with the threshold value (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]); and an adjusting unit (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’), configured to adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of the blade shaft of the ultrasonic scalpel (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’).
Regarding claim 10, Wiener et al. disclose a generator 102 (paragraphs [0150]-[0153]) for temperature control based on a temperature distribution function model (‘artificial neural network’ - paragraph [0257] and on; neural network may be implemented in processor 174 and/or programmable logic device 166 of generator 102 - paragraph [0258])), comprising: a control circuit (paragraph [0152]) coupled to a memory (paragraphs [0150]-[0154]), wherein the control circuit is configured to be able to: save the temperature distribution function model and at least one threshold value (neural network may be implemented in processor 174 and/or programmable logic device 166 of generator 102 - paragraph [0258]); input a corresponding input feature to the temperature distribution function model (inputs/input layers discussed throughout paragraphs [0258]-[0259]), and output corresponding temperature data information (outputs/output layers discussed in paragraphs [0258]-[0260]); compare at least one value in the temperature data information with the threshold value (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]); and adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of the blade shaft of the ultrasonic scalpel (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’).
Regarding claim 11, Wiener et al. further disclose wherein the control circuit is configured that the input feature input to the temperature distribution function model comprises one or a combination of more than one of a working feedback parameter (feedback discussed in paragraph [0265]; other parameters may also be discussed throughout the control method discussed in paragraphs [0257]-[0265]), a physical structure feature parameter (paragraph [0262] - ‘characteristics of the end effector…’), and an environmental parameter.
Regarding claim 12, Wiener et al. further disclose wherein the working feedback parameter comprises, but is not limited to a real-time voltage U, a real-time current I, a power P, an impedance R, and a real-time response frequency f (frequency - paragraph [0262]); the physical structure feature parameter comprises, but is not limited to a material of the blade shaft of the ultrasonic scalpel (material - paragraph [0262]), and a length of the blade shaft; and the environmental parameter comprises, but is not limited to an environmental temperature, and an environmental humidity (NOTE: the environmental parameter was part of a list of alternate or combined inputs in claim 4, therefore, it is not required by the prior art if the prior art met the other of the inputs listed, which is the case here).
Regarding claim 13, Wiener et al. further disclose wherein the temperature data information comprises a real-time temperature value at any point on the blade shaft of the ultrasonic scalpel, and/or a maximum temperature value, a minimum temperature value, and an average temperature of a certain area on the blade shaft of the ultrasonic scalpel (temperature data discussed throughout paragraphs [0264]-[0265]).
Regarding claim 14, Wiener et al. disclose an ultrasonic scalpel surgical instrument 100 (Figs. 1-3) based on a temperature distribution function model (‘artificial neural network’ - paragraph [0257] and on), comprising: an ultrasonic electromechanical system, comprising an ultrasonic transducer 114 (Figs. 1-3) connected to an ultrasonic scalpel 151 (Figs. 1-3) through an ultrasonic waveguide (not shown but disclosed in paragraph [0136]); and a generator 102 (Fig. 1), configured to supply power to the ultrasonic transducer 114, wherein the generator 102 comprises a control circuit (paragraph [0152]) configured to be able to: save the temperature distribution function model and at least one threshold value (memory of generator 102 - paragraphs [0150]-[0154]; threshold value of user-inputted temperature threshold - discussed throughout [0257]-[0265], but in particular in paragraph [0265])); input a corresponding input feature to the temperature distribution function model (inputs/input layers discussed throughout paragraphs [0258]-[0259]), and output corresponding temperature data information (outputs/output layers discussed in paragraphs [0258]-[0260]); compare at least one value in the temperature data information with the threshold value (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]); and adjust, based on the comparison result, a power level applied to the transducer of the ultrasonic scalpel to modulate the temperature of a blade shaft of the ultrasonic scalpel (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’).
Regarding claim 15, Wiener et al. further disclose wherein the control circuit is configured that the input feature input to the temperature distribution function model comprises one or a combination of more than one of a working feedback (feedback discussed in paragraph [0265]; other parameters may also be discussed throughout the control method discussed in paragraphs [0257]-[0265]), a physical structure feature parameter (paragraph [0262] - ‘characteristics of the end effector…’), and an environmental parameter; wherein the working feedback parameter comprises, but is not limited to a real-time voltage U, a real-time current I, a power P, an impedance R, and a real-time response frequency f (frequency - paragraph [0262]); the physical structure feature parameter comprises, but is not limited to a material of the blade shaft of the ultrasonic scalpel (material - paragraph [0262]), and a length of the blade shaft; and the environmental parameter comprises, but is not limited to an environmental temperature, and an environmental humidity (NOTE: the environmental parameter was part of a list of alternate or combined inputs in claim 4, therefore, it is not required by the prior art if the prior art met the other of the inputs listed, which is the case here).
Regarding claim 16, Wiener et al. further disclose wherein the temperature data information comprises a real-time temperature value at any point on the blade shaft of the ultrasonic scalpel, and/or a maximum temperature value, a minimum temperature value, and an average temperature of a certain area on the blade shaft of the ultrasonic scalpel (temperature data discussed throughout paragraphs [0264]-[0265]).
Regarding claim 17, Wiener et al. disclose an ultrasonic scalpel system 100 (Figs. 1-3) based on a temperature distribution function model (‘artificial neural network’ - paragraph [0257] and on), comprising a processor (‘the neural network may be implemented in the processor 174’ - paragraph [0258]) and a non-volatile storage device (memory of generator 102 - paragraphs [0150]-[0154]) comprising an application program, wherein the application program, when executed by the processor 174 (paragraphs [0150]-[0154]), enables the processor to: save the temperature distribution function model and at least one threshold value (‘the neural network may be implemented in the processor 174’ - paragraph [0258]; threshold in paragraph [0265]); input a corresponding input feature to the temperature distribution function model (inputs/input layers discussed throughout paragraphs [0258]-[0259]), and output corresponding temperature data information (outputs/output layers discussed in paragraphs [0258]-[0260]); compare at least one value in the temperature data information with the threshold value (comparison of values discussed at least in paragraphs [0264] and threshold in paragraph [0265]); and adjust, based on the comparison result, a power level applied to a transducer of the ultrasonic scalpel to modulate the temperature of a blade shaft of the ultrasonic scalpel (paragraph [0265] - ‘the drive current may be treated as a control variable and modulated to minimize or reduce the difference between the estimated temperature and the user-defined temperature threshold’).
Regarding claim 18, Wiener et al. further disclose wherein the temperature distribution function model consists of layers, corresponding neurons, and weights; wherein weight parameters and application programs are saved in a memory of a generator 102 (paragraphs [0150]-[0154]); the memory comprises a flash, an EEPROM, or another non-volatile storage device (paragraph [0153]); the application program runs in a processor; and the processor comprises an ARM, a DSP, a FPGA, a CPU, a GPU, or an ASIC chip existing in the generator (paragraph [0152]), or is a remote server connected through a network.
Allowable Subject Matter
Claims 3 & 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Wiener et al. fails to further disclose, teach, or suggest wherein the training method for the model specifically comprises: extracting an input feature from a training set and inputting the same into the neural network algorithm model to calculate an intermediate value and a gradient value for each neuron, wherein a loss function of the model is a mean square error MSE or an average absolute error MAE; updating a weight by a gradient descent method; repeating the foregoing process until the model meets a predetermined stop condition; and stopping training and saving the model after the stop condition is met [claim 3]; and wherein said outputting corresponding temperature data information forms a one-dimensional spatial temperature distribution T(l) along the blade shaft of the ultrasonic scalpel, which is a solution of an equation
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[claim 8].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY LAUREN FISHBACK whose telephone number is (571)270-7899. The examiner can normally be reached M-F 7:30a-3:30p.
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ASHLEY LAUREN FISHBACK
Primary Examiner
Art Unit 3771
/ASHLEY L FISHBACK/Primary Examiner, Art Unit 3771 December 12, 2025