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 Arguments
101 Rejection
Based on applicant’s arguments, see pages 7-8, the previous 101 Rejection has been withdrawn.
103 Rejection
Applicant argues the rejection does not explain how modifying the artificial neural network would improve operation of securing a securing device or improve operational efficiency. Applicant further states:
“The rationale offered in the rejection for modifying the artificial neural network in Gwon is not supported by Gwon.
There is no suggestion or teaching in Gwon that the artificial neural network is configured to detect a fault. There is also no suggestion in Gwon that acoustic signals generated by applying torque to a bolt are indicative of a fault. Modifying the Gwon artificial neural network to detect a fault would not have been obvious based on the teachings of Gwon and would not have improved the operation of the securing device”.
In response to applicant arguments that the primary reference must originate the motivation to combine under MPEP 2143.04, the rationale for obviousness can come from any prior art reference. Furthermore, MPEP 2144, a person of ordinary skill in the art is deemed to have knowledge of all relevant art.
In this instance, one of ordinary skill in the art would have been motivated to modify Gwon in view of Abbott. As cited below, Abbott at paragraph [0134] teaches that actively monitoring a signal representative of a power tool allows the tool to be operated in a more efficient manner. Therefore, the combined references teach inputting an acoustic signal into Gwon’s modified Artificial Neural Network (ANN). By doing so, when faults like stripping or unseating occur, the power tool can be controlled to aid in preventing further issues, thereby improving overall operating efficiency.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The combination, as a whole, teaches the claimed invention. The examiner disagrees that restructuring the ANN of Gwon would result in the prevention of its original purpose. The modification results in a combination that preforms both operations, as the rejection does not substitute one ANN for another but rather to include fault detection [0187] and its library of conditions of Abbott as described in Table 5 in the ANN of Gwon.
Further Table 5 of Abott discloses machine learning applications, potential inputs into the machine learning controller (for example a detection of screw stripping) and outputs from the machine learning controller, i.e. conditions as warning. Therefore, the combination as a whole, teaches the claimed invention.
With respect to claim 12, Applicant argues the acoustic signal and the application of the signal to determine the fastening force of a bolt described in Gwon should not have been applied as corresponding to both the claimed monitoring the acoustic signal and the monitoring of a property of the securing device, and should not have been applied as corresponding to the step of determining whether an inspection is needed based on both the property of the securing device and an acoustic signal.
However, under the broadest reasonable interpretation of the claim, monitoring the axial force is distinct separate step, yet relies on monitoring the acoustic signal. The claim does not structurally define the property monitored. Thus, the acoustic signal is used to monitor the axial force, aligning with the claim’s scope, as the acoustic signal is monitored and used to monitor the axial force through a calculative process.
With respect to applicant’s arguments of claim 2, the examiner respectfully disagrees. Table 5 of Abbott explicitly describes how a machine learning controller is applied, what inputs are used and what results are outputted. More specifically, Table 5 supports the fact that the machine learning controller was trained to recognized from the inputs a screw stripping and output the respective warning.
With respect to applicant’s arguments of claims 4 and 18, the examiner respectfully disagrees. When in operation, the power tool [0044] of Gwon creates a vibration when fastening two components together. Therefore, under the broadest reasonable interpretation, the acoustic signal is emitted by a securing device, i.e. the power tool.
With respect to applicant’s arguments of claims 13 to 16, the examiner respectfully disagrees. Abbott teaches in Table 7 sensing vibrations while the disclosure as a whole support’s threshold being used to determine faults like unseated or stripped fasteners based on the monitored acoustic signal. Therefore, the disclosure as a whole teaches the claimed invention.
Claim Rejections - 35 USC § 103
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 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gwon et al. (2018/0328797) in view of (Abbott et al. (2022/0299946).
With respect to claim 1, Gwon et al. a method of monitoring a mechanical connection between a first aircraft component and a second aircraft component (Gwon et al. teaches a fastener being part of a mechanical assemble fastening components of a power plant turbine; the examiner considers the teaching of the power plant turbine reading on the claimed first and second aircraft components, as a turbine is capable of being part of an aircraft; insofar as what is structurally recited defining these components), the method comprising: securing, using a securing device (i.e. a power tool; [0044]), the first aircraft component to the second aircraft component (Gwon et al. teaches directly measuring sound [0043] when fastening a bolt using a power tool; [0044]); emitting an acoustic signal from the securing device (as during securing, an acoustic signal is created by the power tool during securing the fastener; [0002]) during the securing of the first aircraft component to the second aircraft component [0002]; and obtaining information indicative of the acoustic signal emitted during the securing the first aircraft component to the second aircraft component based, at least in part, on the acoustic signal (as Gwon et al. teaches using a detector at S20 to measure a vibrational sound; [0045-0046], the obtained information during securing the components together, for example axial force; [0064]).
Gwon et al. remains silent regarding inputting the information indicative of the acoustic signal into a machine learning model, wherein the machine learning model is configured to provide an output indicative of a fault condition of the mechanical connection.
Abbott et al. teaches a similar method including inputting information indicative of an acoustic signal (i.e. sound; as Table 5 discloses inputting sound into a machine learning model) into a machine learning model (i.e. a machine learning controller storing a machine learning model, as seen in Fig. 1 and para. [0062]), wherein the machine learning model [0062] is configured to provide an output indicative of a fault condition of the mechanical connection (as the trained model is disclosed to determined conditions like unseated or stripped conditions; [0078] [0187], Table 5).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Gwon et al. to include the machine learning model of Abbott et al. to receive the acoustic signal from the microphone of Gwon et al. because such a modification allows the output of the machine learning controller to better operate a securing device and achieve a greater operating efficiency; [0134], thereby improving the fastening capabilities of Gwon et al..
With respect to claim 2, Gwon et al. as modified by Abbott et al. teaches the method comprising training the machine learning model using training data (as seen in Fig. 6 of Abbott et al.), wherein the training data comprises information indicative of a plurality of acoustic signals with known fault conditions (as Table 5 of Abbott et al. discloses the various training data used to train the machine learning model, including data indicative of stripping and past screw).
With respect to claim 3, Gwon et al. as modified by Abbott et al. teaches the method wherein the output provided by the machine learning model (of Abbott et al.) is indicative of whether inspection of the mechanical connection is required (as Table 6 of Abbott et al. discloses a possible output of the machine learning model being a recommendation of inspection).
With respect to claim 4, Gwon et al. as modified by Abbott et al. teaches the method wherein the information indicative of the acoustic signal comprises a sound signature (Fig. 2a of Gwon et al.) of an acoustic signal emitted by the securing device (i.e. power tool, as taught in Gwon et al.) during the securing the first aircraft component to the second aircraft component (i.e. during securing of components forming a joint in the aircraft).
With respect to claim 5, Gwon et al. as modified by Abbott et al. teaches the method wherein the sound signature comprises the sound signature (as seen in Fig. 2A of Gwon et al.) comprises a duration of the acoustic signal (as the time of flight is seen in the x-axis).
With respect to claim 6, Gwon et al. as modified by Abbott et al. teaches the method wherein the securing, using the securing device (i.e. the power tool taught in Gwon et al.), the first aircraft component to the second aircraft component (of the power plant turbine) includes applying a fastener (i.e. bolt) between the first aircraft component and the second aircraft component (Abstract of Gwon et al.).
With respect to claims 7 and 19, Gwon et al. as modified by Abbott et al. teaches the method wherein the first aircraft component is secured to the second aircraft component by a single-sided fastener (as Abbott et al. teaches sound signals being generated during the fastening of sheet metal screws, for example).
With respect to claim 8, Gwon et al. as modified by Abbott et al. teaches the method comprising securing, using the securing device (i.e. the power tool taught in Gwon et al.), the first aircraft component to the second aircraft component (of the power plant) during an aircraft assembly process (as Gwon et al. is concerned with detecting bolts during assembly of engine components; [0005]).
With respect to claim 9, Gwon et al. a system (Gwon et al. teaches a computer environment system; Fig. 1) for securing a first aircraft component to a second aircraft component (Gwon et al. teaches a fastener being part of a mechanical assemble fastening components of a power plant turbine; the examiner considers the teaching of the power plant turbine reading on the claimed first and second aircraft components, as a turbine is capable of being part of an aircraft; insofar as what is structurally recited defining these components) comprising: a securing device (a power tool; [0044]) configured to secure the first aircraft component to the second aircraft component (i.e. components of the power plant turbine; insofar as how they are structurally defined) with a mechanical connection (bolt) in use; and a microphone [0046] configured to receive an acoustic signal emitted by the securing device in use (Gwon et al. teaches using a detector at S20 to measure a vibrational sound; [0045-0046].
Gwon et al. remains silent regarding a control module comprising a memory storing a machine learning model configured to receive an input, determine that the mechanical condition is in a fault condition based at least in part of the input and output information indicative of a fault condition of the mechanical connection.
Abbott et al. teaches a similar system, including a control module (540) comprising a memory (580) storing a machine learning model, Fig. 1 and [0062]) configured to receive an input of an acoustic signal (i.e. sound; as Table 5 discloses inputting sound into a machine learning model), determine that the mechanical condition is in a fault condition (i.e. either unseated or stripped) based at least in part of the input (i.e. acoustic signal), and output information indicative of a fault condition of the mechanical connection (as the trained model is disclosed to determined conditions like unseated or stripped; [0078] [0187], Table 5).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Gwon et al. to include the machine learning model of Abbott et al. to receive the acoustic signal from the microphone of Gwon et al. because such a modification allows the output of the machine learning controller to better operate a securing device and achieve a greater operating efficiency; [0134], thereby improving the fastening capabilities of Gwon et al..
With respect to claim 10, Gwon et al. as modified by Abbott et al. teaches the system wherein the securing device (i.e. power tool of Gwon et al.) is configured to secure the first aircraft component to the second aircraft component (i.e. the components of the power plant turbine) by applying a fastener (bolt) between the first aircraft component and the second aircraft component (i.e. during securing of components forming a joint in the aircraft).
With respect to claim 11, Gwon et al. as modified by Abbott et al. teaches the system wherein the securing device (i.e. the power tool taught in Gwon et al.) is configured to secure the first aircraft component to the second aircraft component during an aircraft assembly process (as Gwon et al. is concerned with detecting bolts during assembly of engine components, which is capable of occurring during the assembly of the turbine to be part of an aircraft, insofar as what is structurally recited; [0005]).
With respect to claim 12, Gwon et al. a method of determining a characteristic of a mechanical connection (i.e. joint; abstract) between a first aircraft component and a second aircraft component (Gwon et al. teaches a fastener being part of a mechanical assemble fastening components of a power plant turbine; the examiner considers the teaching of the power plant turbine reading on the claimed first and second aircraft components, as a turbine is capable of being part of an aircraft; insofar as what is structurally recited defining these components), the method comprising: securing, using a securing device (i.e. power tool; [0044]), the first aircraft component to the second aircraft component (of the power plant turbine); emitting an acoustic signal from the securing device (as during securing, an acoustic signal is created by the power tool during securing the fastener; [0002]) during the securing of the first aircraft component to the second aircraft component [0002]; monitoring a property (i.e. force; [0060]; insofar as what is structurally recited for “monitoring a property”) of the securing device (power tool; [0044]) while securing the first aircraft component to the second aircraft component (i.e. the examiner considers the received acoustic sound vibration being a property of the securing device during fastening); monitoring the acoustic signal (S20) emitted while securing the first aircraft component to the second aircraft component (during the fastening of the joint).
Gwon et al. remains silent regarding determining, based on the property of the securing device and the acoustic signal, whether inspection of the mechanical connection is required.
Abbott et al. teaches a similar system including determining, based on a property of the securing device (i.e. motor characteristics; [0136]) and an acoustic signal (i.e. a sound generated during fastening; Table 5), whether inspection of the mechanical connection is required (as Abbott et al. teach based on motor characteristics and sound, as inputted into a model, the system will indicate if inspection needs to occur; Table 6).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Gwon et al. to include model determination using collected securing device data and generated sounds of Abbott et al. because such a modification allows the output of the machine learning controller to better operate a securing device and achieve a greater operating efficiency; [0134], thereby improving the fastening capabilities of Gwon et al..
With respect to claim 13, Gwon et al. as modified by Abbott et al. teaches the method wherein determining whether inspection of the mechanical connection is required (as taught by Abbott et al.) comprises comparing the property of the securing device against a property threshold [0140] and comparing the acoustic signal against an acoustic signal threshold (i.e. vibrational thresholds; Table 7 of Abbott et al.).
With respect to claim 14, Gwon et al. as modified by Abbott et al. teaches the method further comprising providing an indication (via the display of Gwon et al.) if the acoustic signal differs from the acoustic signal threshold by greater than a predetermined amount (as the combination as a whole indicates on the display that a problem was detected based on the comparison being larger than the defined threshold for the acoustic signal; [0072] of Abbott et al., as the threshold are stored and used to compare the detected data against the stored thresholds).
With respect to claim 15, Gwon et al. as modified by Abbott et al. teaches the method wherein comparing the acoustic signal against the acoustic signal threshold (as taught in Abbott et al.) comprises comparing a sound signature of the acoustic signal against the acoustic signal threshold (as Abbott et al. teaches using training data, therefore the combination as a whole would involve training the model found in Abbott et al. such that the detected acoustic signal in Gwon et al. is compared against a sound signature to determined abnormal conditions).
With respect to claim 16, Gwon et al. as modified by Abbott et al. teaches the method wherein the sound signature of the acoustic signal comprises a duration of the acoustic signal (based on time of flight, as taught in Gwon et al. [0006]).
With respect to claim 17, Gwon et al. as modified by Abbott et al. teaches the method wherein the property of the securing device comprises an electrical current provided to the securing device (as Table 7 details sensed properties of the securing device, including current).
With respect to claim 18, Gwon et al. as modified by Abbott et al. teaches the method wherein the acoustic signal comprises a sound emitted by the securing device (i.e. power tool, as taught in Gwon et al.; [0044]) while securing the first aircraft component to the second aircraft component (as Gwon et al. teaches the sound created is during joining the fastener to power plant turbine components).
With respect to claim 19, Gwon et al. as modified by Abbott et al. teaches the method wherein the first aircraft component is secured to the second aircraft component by a fastener (i.e. bolt) and the acoustic signal comprises a sound emitted by the fastener (i.e. bolt) while securing the first aircraft component to the second aircraft component (during assembly of the power plant).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
EL-Bakry et al. (2011/0219878.) which teaches using acoustic emissions to measure the structurally integrity of aircraft components.
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 MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Meier can be reached at 571-272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853
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