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 .
Claim Rejections - 35 USC § 102
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 12 and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20230213401 (herein Sha).
The applied reference has a common inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding claim 12, Sha teaches A method for generating a dataset to train a machine learning model to predict a characteristic of a target bolt in situ (steps 2004-2008, Fig. 20), the method comprising:
setting each of a plurality of test bolts to a plurality of known levels of tension (This model can be created by applying known tension values to the set of bolts, [0019]);
for each known level of tension set in each test bolt: determining a time-of-flight of longitudinal waves in the test bolt; determining a time-of-flight of shear waves in the test bolt (invention is premised on relationships between the times-of-flight of shear waves and longitudinal waves in a bolt and tensile stress in the bolt, [0016], Eq. 1-2);
determining a ratio of the time-of-flight of shear waves and the time-of-flight of longitudinal waves (ratio of a time-of-flight of shear waves (ToF.sub.shear) and a time-of-flight of longitudinal waves, [0017], Eq. 3);
determining a temperature of the test bolt (temperature can be collected, [0103]);
determining a size of the test bolt (machine learning algorithm can receive bolt meta data, such as bolt size, bolt length, [0140]); and
determining a plurality of signal-characterizing features (receiving, from a transducer, raw data relating to reflections of UT longitudinal waves in the test bolt, wherein the raw data comprises at least a first echo and a second echo, [0029]).
Regarding claim 17, Sha teaches wherein determining a plurality of signal- characterizing features comprises:
receiving a first signal from a first test event on the test bolt, wherein the first signal comprises at least a first echo and a second echo of a longitudinal wave in the test bolt (receiving, from a transducer, raw data relating to reflections of UT longitudinal waves in the test bolt, wherein the raw data comprises at least a first echo and a second echo, [0029]);
receiving a second signal from a second test event on the test bolt, wherein the second signal comprises at least a first echo and a second echo of a shear wave in the test bolt (receiving, from a transducer, raw data relating to reflections of UT shear waves in the test bolt, wherein the raw data comprises at least a first echo and a second echo, [0032]);
extracting one or more features from the first signal (echoes, [0029]);
extracting one or more features from a cross correlation performed on the first signal (ToF.sub.longitudinal can be measured and correlated with residual tension and/or tensile stress of the bolt, [0100]; application can analyze the raw data to determine whether it meets certain criteria, [0102]);
extracting one or more features from the second signal (echoes, [0032]); and
extracting one or more features from a cross-correlation performed on the second signal (ToF.sub.shear can be measured and correlated with residual tension and/or tensile stress of the bolt, [0100]; application can analyze the raw data to determine whether it meets certain criteria, [0102]).
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.
The factual inquiries 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.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha as applied to claim 12 above, and further in view of CN 111191742A (herein Wang).
Regarding claim 13, Sha teaches wherein determining a time-of-flight of longitudinal waves in the test bolt comprises: receiving a signal from a test event on the test bolt, wherein the signal comprises at least a first echo and a second echo of a longitudinal wave in the test bolt (receiving, from a transducer, raw data relating to reflections of UT longitudinal waves in the test bolt, wherein the raw data comprises at least a first echo and a second echo, [0029]); determining that the signal passes one or more signal quality checks (analyzing the data to assess certain quality characteristics, Abstract)); and
performing cross correlation on the signal to determine the time-of-flight of the longitudinal wave (time that it takes a longitudinal wave and shear wave to travel from one end of a bolt and reflect back (ToF.sub.longitudinal and ToF.sub.shear, respectively) can be measured and correlated with residual tension and/or tensile stress of the bolt, [0100]).
Further regarding claim 13, Sha does not teach, “wherein performing cross correlation comprises using a flexible window algorithm.” However, Wang teaches a multi-source heterogeneous data stream sliding window length self-adaptive adjusting algorithm is known in the art of data mining (p. 1-2 of translation).
Regarding claim 15, Sha teaches wherein determining a time-of-flight of shear waves in the test bolt comprises: receiving a signal from a test event on the test bolt, wherein the signal comprises at least a first echo and a second echo of a shear wave in the test bolt (receiving, from a transducer, raw data relating to reflections of UT shear waves in the test bolt, wherein the raw data comprises at least a first echo and a second echo, [0032]); determining that the signal passes one or more signal quality checks (analyzing the data to assess certain quality characteristics, Abstract); and performing cross correlation on the signal to determine the time-of-flight of the shear wave time that it takes a longitudinal wave and shear wave to travel from one end of a bolt and reflect back (ToF.sub.longitudinal and ToF.sub.shear, respectively) can be measured and correlated with residual tension and/or tensile stress of the bolt, [0100]).
Further regarding claim 15, Sha does not teach, “wherein performing cross correlation comprises using a flexible window algorithm.” However, Wang teaches a multi-source heterogeneous data stream sliding window length self-adaptive adjusting algorithm is known in the art of data mining (p. 1-2 of translation).
For claims 13 and 15, it would have been obvious to one of ordinary skill in the art before the time of filing to incorporate the sliding windows algorithm of Wang for manipulating the transducer data of Sha. One would have been motivated to do so for at least the purpose of accommodating changing situations by providing boundaries for detecting variation data (p. 2).
Allowable Subject Matter
Claims 14 and 16 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.
Regarding claims 14 and 16, the prior art does not teach, “establishing a plurality of delay times for the second window; for each delay time for the second window, establishing a plurality of window sizes for the first and second windows and determining which of the plurality of window sizes meets one or more criteria; for each window size that meets the one or more criteria, calculating a cross correlation function; determining a maximum amplitude of all cross correlation functions; and identifying the time-of-flight of the longitudinal wave as a time value corresponding to the maximum amplitude.” Wang teaches a corresponding sliding window length self-adaptive adjusting algorithm, using a filtered signal (pre-processing by converting the sound signal into vector data according to Mel-frequency cepstrum coefficient, p. 4,is a form of signal filtering) having established window starting points (see Fig. 6), and setting maximum and minimum window sizes (step 1, p. 7). However, Wang does not teach the above quoted limitation.
Claims 23-24, 27-33 are allowed. Claims 23-24, 27-33 predict tensile stress by training machine learning models on based on bolt coating and by stress, and as well by a particular aspect (such as bolt size in claim 27 and length in claim 28). Using the three models, they determine stress on the bolt. The combination of Sha and Wang train data using longitudinal and shear waves (which are a function of stress) to determine tensile stress on a bolt, but does not teach using a coating to train a second machine learning model. Non-Patent Literature titled, “Effect of Tightening Speed on the Torque-Tension and Wear Pattern in Bolted Connections” (Nassar) teaches it is known in the art to incorporate coating types when determining tension relationships. However, even if it would have been obvious to one of ordinary skill in the art to train a machine learning model on a torque/coating relationship, the combination of prior art would not completely teach the present inventions.
Conclusion
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/WALTER L LINDSAY JR/Supervisory Patent Examiner, Art Unit 2852
/PHILIP T FADUL/Examiner, Art Unit 2852
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