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 § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under step 1, claim 1 , 8 and 15 belongs to a statutory category. Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim s a and similarly 8 and 15 recite(s) “ performing, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal; ” which are directed to mathematical concepts and/or mental processes based on applicant’s specification, for example see Par. 43 . Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “ receiving, as first input into a trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell; ”, “ receiving, as second input into the trained neural network, a second signal representative of acoustic measurements of an under-the-test battery cell; ”, and “ generating, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not ” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The additional elements in claim 8 of “ one or more memories having computer-readable instructions corresponding to a trained neural network stored thereon; and one or more processors configured to execute the computer-readable instructions to ” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer . The additional elements in claim 15 of “ an acoustic array configured to acoustically scan an under-the-test battery cell; ” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use. Under Step 2 B , evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “ receiving, as first input into a trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell; ”, “ receiving, as second input into the trained neural network, a second signal representative of acoustic measurements of an under-the-test battery cell; ”, and “ generating, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not ” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) ( ii ) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)( i ). The additional elements in claim 8 of “ one or more memories having computer-readable instructions corresponding to a trained neural network stored thereon; and one or more processors configured to execute the computer-readable instructions to ” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d) . The additional elements in claim 15 of “ an acoustic array configured to acoustically scan an under-the-test battery cell; ” are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) ( i ). Claims 2-4, 7, 9-11, 14, and 16-18 further describe the abstract ideas cited above. Claims 5-6, 12-13, and 19-20 the judicial exception is not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use and are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) ( i ). Claim Rejections - 35 USC § 102 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. Claim(s) 1- 4, are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Cai ( US 20110108181 A1 ) . In claim 1, Cai discloses a method comprising: receiving, as first input into a trained neural network (Par. 40 “ neural networks ”) , at least one first signal representative of acoustic measurements of at least one reference battery cell (Par. 45 “library” “failing patterns”) ; receiving, as second input into the trained neural network (Par. 10 “ Collectively, these measurements may be considered to be a unique weld signature for each weld joint spot, with the weld signature being correlated with a pre-populated library of validated weld signatures to predict the quality of the resultant weld joint. ”) , a second signal representative of acoustic measurements (Par. 6 “ collecting a set of sensory data from control signals and/or sensors, e.g., temperature, acoustic ”) of an under-the-test battery cell (Par. 4 “ multi-cell vehicle battery, ”) ; performing, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal ( Par. 49 “ threshold limits ” ) ; and generating, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not (Par. 41 “ (1) acceptable, good, or passing, or (2) unacceptable, bad, or failing, according to a predetermined set of quality criteria ”) . In claim 2, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as defect free if the second signal has the threshold similarity to the at least one first signal and the at least one first signal is a signal representing a defect free battery cell (Par. 41 “ (1) acceptable, good, or passing ”) . In claim 3, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as having an unknown defect if the second signal does not have the threshold similarity to the at least one first signal (Par. 41 “ bad, or failing, according to a predetermined set of quality criteria ”) and the at least one first signal includes one signal representing a defect free battery cell and one or more additional signals representing one or more known defects (Par. 41 “n eural network 40 may then determine or recognize whether a particular pattern is represented in the total welding signature collectively defined by the sensory data 11 and 111, or in an extracted feature set of such a weld signature, that is (1) acceptable, good, or passing, ”) . In claim 4, Cai further discloses wherein the trained neural network is not trained to identify a type of the unknown defect (Par. 41, Examiner notes that determining “ unacceptable, bad, or failing ” does not identify the type of defect of the battery rather, it determines the battery does not meet standards) . In claim 8, Cai disclose a device (Fig. 1) comprising: one or more memories having computer-readable instructions (Par. 1260 “memory”) corresponding to a trained neural network stored thereon (Par. 40, 1260 “ neural network ”) ; and one or more processors (Par. 1274 “ microprocessors ”) configured to execute the computer-readable instructions to: receive, as first input into the trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell (Par. 45 “library” “failing patterns”) ; receive, as second input into the trained neural network (Par. 10 “ Collectively, these measurements may be considered to be a unique weld signature for each weld joint spot, with the weld signature being correlated with a pre-populated library of validated weld signatures to predict the quality of the resultant weld joint. ”) , a second signal representative of acoustic measurements of an under-the-test battery cell (Par. 6 “ collecting a set of sensory data from control signals and/or sensors, e.g., temperature, acoustic ”) of an under-the-test battery cell (Par. 4 “ multi-cell vehicle battery, ”) ; perform, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal (Par. 49 “ threshold limits ”) ; and generate, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not (Par. 41 “ (1) acceptable, good, or passing, or (2) unacceptable, bad, or failing, according to a predetermined set of quality criteria ”) . In claim 9, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as defect free if the second signal has the threshold similarity to the at least one first signal and the at least one first signal is a signal representing a defect free battery cell (Par. 41 “ (1) acceptable, good, or passing ”) . In claim 10, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as having an unknown defect if the second signal does not have the threshold similarity to the at least one first signal (Par. 41 “ bad, or failing, according to a predetermined set of quality criteria ”) and the at least one first signal includes one signal representing a defect free battery cell and one or more additional signals representing one or more known defects (Par. 41 “n eural network 40 may then determine or recognize whether a particular pattern is represented in the total welding signature collectively defined by the sensory data 11 and 111, or in an extracted feature set of such a weld signature, that is (1) acceptable, good, or passing, ”) . In claim 11, Cai further discloses wherein the trained neural network is not trained to identify a type of the unknown defect (Par. 41, Examiner notes that determining “ unacceptable, bad, or failing ” does not identify the type of defect of the battery rather, it determines the battery does not meet standards) . In claim 8, Cai disclose a system (Fig. 1) comprising: an acoustic array (Par. 6 “ acoustic, electrical, mechanical, or other suitable sensors ”) configured to acoustically scan an under-the-test battery cell (Par. 37 “ multi-cell battery 34 ”) ; and a trained neural network (Par. 40, 1260 “ neural network ”) configured to: receive, as first input into the trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell (Par. 45 “library” “failing patterns”) ; receive, as second input into the trained neural network (Par. 10 “ Collectively, these measurements may be considered to be a unique weld signature for each weld joint spot, with the weld signature being correlated with a pre-populated library of validated weld signatures to predict the quality of the resultant weld joint. ”) , a second signal representative of acoustic measurements of an under-the-test battery cell (Par. 6 “ collecting a set of sensory data from control signals and/or sensors, e.g., temperature, acoustic ”) of the under-the-test battery cell (Par. 4 “ multi-cell vehicle battery, ”) ; perform, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal (Par. 49 “ threshold limits ”) ; and generate, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not (Par. 41 “ (1) acceptable, good, or passing, or (2) unacceptable, bad, or failing, according to a predetermined set of quality criteria ”) . In claim 16, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as defect free if the second signal has the threshold similarity to the at least one first signal and the at least one first signal is a signal representing a defect free battery cell (Par. 41 “ (1) acceptable, good, or passing ”) . In claim 17, Cai further discloses wherein the defect identification identifies the under-the-test battery cell as having an unknown defect if the second signal does not have the threshold similarity to the at least one first signal (Par. 41 “ bad, or failing, according to a predetermined set of quality criteria ”) and the at least one first signal includes one signal representing a defect free battery cell and one or more additional signals representing one or more known defects (Par. 41 “n eural network 40 may then determine or recognize whether a particular pattern is represented in the total welding signature collectively defined by the sensory data 11 and 111, or in an extracted feature set of such a weld signature, that is (1) acceptable, good, or passing, ”) . In claim 18, Cai further discloses wherein the trained neural network is not trained to identify a type of the unknown defect (Par. 41, Examiner notes that determining “ unacceptable, bad, or failing ” does not identify the type of defect of the battery rather, it determines the battery does not meet standards) . 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) 5 , 12 , and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai in view of Cella ( US 20220197306 A1 ) . In claim 5, Cai does not explicitly disclose wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture . (Emphasis added) Cella teaches wherein the trained neural network has a twin network architecture (Par. 1176 “ twin-based product architectures ”) and the at least one first signal is received as input into a first one of two neural networks of the twin architecture (Par. 1176 “ CTO ”) and the second signal is received as input into a second one of the two neural networks of the twin network architecture ((Par. 1176 “ CTO digital twin”). Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture as taught by Cella in Cai in order to simulate product usage under a plurality of constraints that might impact product performance ( Cella Par. 1176), thus leading to an improved system. In claim 12, Cai does not explicitly disclose wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture . (Emphasis added) Cella teaches wherein the trained neural network has a twin network architecture (Par. 1176 “ twin-based product architectures ”) and the at least one first signal is received as input into a first one of two neural networks of the twin architecture (Par. 1176 “ CTO ”) and the second signal is received as input into a second one of the two neural networks of the twin network architecture ((Par. 1176 “ CTO digital twin”). Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture as taught by Cella in Cai in order to simulate product usage under a plurality of constraints that might impact product performance ( Cella Par. 1176), thus leading to an improved system. In claim 19, Cai does not explicitly disclose wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture . (Emphasis added) Cella teaches wherein the trained neural network has a twin network architecture (Par. 1176 “ twin-based product architectures ”) and the at least one first signal is received as input into a first one of two neural networks of the twin architecture (Par. 1176 “ CTO ”) and the second signal is received as input into a second one of the two neural networks of the twin network architecture ((Par. 1176 “ CTO digital twin”). Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network has a twin network architecture and the at least one first signal is received as input into a first one of two neural networks of the twin architecture and the second signal is received as input into a second one of the two neural networks of the twin network architecture as taught by Cella in Cai in order to simulate product usage under a plurality of constraints that might impact product performance ( Cella Par. 1176), thus leading to an improved system. Claim (s) 6 , 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai in view of Liebman ( US 20220147897 A1 ) . In claim 6, Cai does not explicitly disclose wherein the trained neural network is an autoencoder. Liebman teaches wherein the trained neural network is an autoencoder (Par. 28 “ autoencoders ”) . Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network is an autoencoder as taught by Liebman in order to facilitate identifying which data sources are more relevant in different contexts for the purpose of modeling/decision making (Liebman Par. 28) thus increasing accuracy. In claim 13, Cai does not explicitly disclose wherein the trained neural network is an autoencoder. Liebman teaches wherein the trained neural network is an autoencoder (Par. 28 “ autoencoders ”) . Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network is an autoencoder as taught by Liebman in order to facilitate identifying which data sources are more relevant in different contexts for the purpose of modeling/decision making (Liebman Par. 28) thus increasing accuracy. In claim 20, Cai does not explicitly disclose wherein the trained neural network is an autoencoder. Liebman teaches wherein the trained neural network is an autoencoder (Par. 28 “ autoencoders ”) . Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the trained neural network is an autoencoder as taught by Liebman in order to facilitate identifying which data sources are more relevant in different contexts for the purpose of modeling/decision making (Liebman Par. 28) thus increasing accuracy. Claim (s) 7 , 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai in view of Wang ( US 10759298 B2 ) . In claim 7, Cai does not explicitly disclose reducing a dimensionality of the at least one first signal and the second signal using a recurring neural network. Wang teaches reducing a dimensionality of the at least one first signal and the second signal using a recurring neural network (Par. 53 “ recurrent neural network ”) . Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to reducing a dimensionality of the at least one first signal and the second signal using a recurring neural network as taught by Wang in order to provide a predictive result (Wang Par. 53-55 ) thus increasing accuracy. In claim 14, Cai does not explicitly disclose wherein the computer-readable instructions further include instructions corresponding to a recurring neural network, which when executed by the one or more processors, cause the one or more processors to reduce a dimensionality of the at least one first signal and the second signal using the recurring neural network . Wang teaches wherein the computer-readable instructions further include instructions corresponding to a recurring neural network, which when executed by the one or more processors, cause the one or more processors to reduce a dimensionality of the at least one first signal and the second signal using the recurring neural network (Par. 53 “ recurrent neural network ”) . Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to have wherein the computer-readable instructions further include instructions corresponding to a recurring neural network, which when executed by the one or more processors, cause the one or more processors to reduce a dimensionality of the at least one first signal and the second signal using the recurring neural network as taught by Wang in order to provide a predictive result (Wang Par. 53-55) thus increasing accuracy. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BRANDON J BECKER whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)431-0689 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9:30-5:30 . 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, FILLIN "SPE Name?" \* MERGEFORMAT Shelby Turner can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-6334 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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