CTNF 18/601,444 CTNF 91535 DETAILED ACTION 07-03-aia AIA 15-10-aia 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 § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim(s) 1-20 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. With respect to Claim(s) 1 , the limitation states “… reworking the assembled product using the rework prediction”. With respect to Claim(s) 8 , the limitation states “… rework , using the hardware processor, the assembled product using the rework prediction”. With respect to Claim(s) 15 , the limitation states “… rework the assembled product using the rework prediction”. It is unclear what the defined scope of ‘rework(ing)’ an already assemble product. Is this a physical action like taking apart and reassembling the product? Is this a mental or mathematical step of reworking a mental or mathematical problem? Amending the language to define the scope of the term ‘rework(ing)’ would help cure the standing rejection(s). For examination purposes, Examiner will assume the step merely reworking a mental or mathematical problem. Claim(s) 2-7, 9-14, 16-20 is/are rejected as for being dependent on the above rejected parent claim(s). Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim(s) 15-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim(s) are directed towards “A computer program product”. The claim(s) should be amended to read ‘ A non-transitory computer readable media having instructions stored thereon that, when executed by a processor, cause the processor to: … ’. Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more ( See 2019 Update: Eligibility Guidance ). Independent Claim(s) 1 recites receiving live data about components for a production process to result in an assembled product; generating a tolerance analysis model to simulate tolerances at each operation of the production process; feeding the live data into the tolerance analysis model, wherein the tolerance analysis model provides an assembly prediction of failure; building the assembled product according to the assembly prediction of failure; comparing the assembled product to the assembly prediction; retraining the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction; and reworking the assembled product using the rework prediction [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . Independent Claim(s) 8 recites receive, using the hardware processor, live data about components for a production process resulting in an assembled product; generate, using the hardware processor, a tolerance analysis model to simulate tolerances at each operation of the production process; feed, using the hardware processor, the live data into the tolerance analysis model, wherein the tolerance analysis model provides an assembly prediction of failure; build, using the hardware processor, the assembled product according to the assembly prediction of failure; compare, using the hardware processor, the assembled product to the assembly prediction; retrain, using the hardware processor, the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction; and rework, using the hardware processor, the assembled product using the rework prediction [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . Independent Claim(s) 15 recites layer normalization in machine learning applications, receive live data about components for a production process resulting in an assembled product; generate a tolerance analysis model to simulate tolerances at each operation of the production process; feed the live data into the tolerance analysis model, wherein the tolerance analysis model provides an assembly prediction of failure; build the assembled product according to the assembly prediction of failure; compare the assembled product to the assembly prediction; retrain the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction; and rework the assembled product using the rework prediction [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . In combination with Independent Claim(s) 1, 8, 15, Claim(s) 2-6, 9-13, 16-20 recite(s) the tolerance analysis model includes a Monte Carlo simulation for stacking defects. the live data is real time data from an assembly process, the live data including sub assembly component dimensions, assembly data and component inventory. the tolerance analysis model is a provided by a neural network performing a Monte Carlo simulation using historical data on the production process. the assembly prediction includes a list of components to be assembled into the assembled product, process steps for assembling the components, and testing characteristics indicative of correct assembly. the rework prediction includes instructions to rework the assembled product without assembly tolerance faults [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. A computer implemented method; A system including a hardware processor; and a memory that stores a computer program product, the computer program product of the system includes instructions comprising:; A computer program product; the computer program product including a computer readable storage medium having computer readable program code embodied therewith, program instructions executable by a processor to cause the processor to: ); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. for assembly; for assembling products; the assembled products is a tape drive ). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well- understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)). 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-3, 5, 6, 8-10, 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DER VELDEN (US 20130304439 A1) in view of TORNQUIST ET AL. (US 20060129259 A1) (hereinafter “TORNQUIST”) . With respect to Claim(s) 1 , VAN DER VELDEN teaches ‘simulating behavior of a modeled object includes storing a tolerance attribute value in a memory area for a specified parameter of the modeled object, defining a set of rules representative of a plurality of assumptions of a model simulation, executing the model simulation based on the tolerance attribute, verifying an output of the model simulation with respect to a set of rules that are dependent on input and output values for which the tolerance attribute as verified, and validating the output behavior against requirements for every stage of the product lifecycle, from preliminary design to end of life’ and the BRI of: A computer implemented method ( See, e.g., Fig(s). 6, 7 ) comprising: receiving live data about components during operation ( See, e.g., ¶ 0046 ; See also, e.g., Fig(s). 2- 4B ); generating a tolerance analysis model to simulate tolerances at each operation of the production process ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); feeding the live data into the tolerance analysis model, wherein the tolerance analysis model provides an assembly prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); building the assembled product according to the assembly prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); comparing the assembled product to the assembly prediction ( See, e.g., ¶ 0003, 0004, 0016, 0019, 0020, 0027, 0031-0033, 0039 ; See also, e.g., Fig(s). Fig(s). 2- 4B ); retraining the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction ( See, e.g., Fig(s). 2 ); and reworking the assembled product using the rework prediction ( See, e.g., Fig(s). 2 ). However, VAN DER VELDEN is lacking the explicit language of: for assembly; data about components for a production process to result in an assembled product; assembly prediction. TORNQUIST teaches ‘Determining a minimum condition and a maximum condition of an assembly of parts includes determining a subset of the assembly of parts, constructing a tolerance chain comprised of tolerance features associated with the parts and that have tolerances that can assume maximum and minimum values, setting at least one tolerance to a minimum value or a maximum value, and calculating the minimum condition and the maximum condition of the assembly based on the setting of the tolerance’ and the BRI of: for assembly; data about components for a production process to result in an assembled product; assembly prediction ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include for assembly; data about components for a production process to result in an assembled product; assembly prediction. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 8 , VAN DER VELDEN teaches ‘simulating behavior of a modeled object includes storing a tolerance attribute value in a memory area for a specified parameter of the modeled object, defining a set of rules representative of a plurality of assumptions of a model simulation, executing the model simulation based on the tolerance attribute, verifying an output of the model simulation with respect to a set of rules that are dependent on input and output values for which the tolerance attribute as verified, and validating the output behavior against requirements for every stage of the product lifecycle, from preliminary design to end of life’ and the BRI of: A system ( See, e.g., Fig(s). 6, 7 ) including a hardware processor; and a memory that stores a computer program product, the computer program product of the system includes instructions ( See, e.g., Fig(s). 6, 7 ) comprising: receive, using the hardware processor, live data about components ( See, e.g., ¶ 0046 ; See also, e.g., Fig(s). 2- 4B ); generate, using the hardware processor, a tolerance analysis model to simulate tolerances at each operation of the production process ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); feed, using the hardware processor, the live data into the tolerance analysis model, wherein the tolerance analysis model provides a prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); build, using the hardware processor, the assembled product according to the assembly prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); compare, using the hardware processor, the assembled product to the assembly prediction ( See, e.g., ¶ 0003, 0004, 0016, 0019, 0020, 0027, 0031-0033, 0039 ; See also, e.g., Fig(s). Fig(s). 2- 4B ); retrain, using the hardware processor, the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction ( See, e.g., Fig(s). 2 ); and rework, using the hardware processor, the assembled product using the rework prediction ( See, e.g., Fig(s). 2 ). However, VAN DER VELDEN is lacking the explicit language of: for assembling products, for a production process resulting in an assembled product. TORNQUIST teaches ‘Determining a minimum condition and a maximum condition of an assembly of parts includes determining a subset of the assembly of parts, constructing a tolerance chain comprised of tolerance features associated with the parts and that have tolerances that can assume maximum and minimum values, setting at least one tolerance to a minimum value or a maximum value, and calculating the minimum condition and the maximum condition of the assembly based on the setting of the tolerance’ and the BRI of: for assembling products, for a production process resulting in an assembled product ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include for assembling products, for a production process resulting in an assembled product. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 2, 9 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the tolerance analysis model includes a Monte Carlo simulation for stacking defects ( See, e.g., ¶ 0042, 0053 ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the tolerance analysis model includes a Monte Carlo simulation for stacking defects. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 3, 10 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the live data is real time data from an assembly process, the live data including sub assembly component dimensions, assembly data and component inventory ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the live data is real time data from an assembly process, the live data including sub assembly component dimensions, assembly data and component inventory. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 5, 12 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the assembly prediction includes a list of components to be assembled into the assembled product, process steps for assembling the components, and testing characteristics indicative of correct assembly ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the assembly prediction includes a list of components to be assembled into the assembled product, process steps for assembling the components, and testing characteristics indicative of correct assembly. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 6, 13, 20 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). VAN DER VELDEN further teaches the BRI of: the rework prediction includes instructions to rework the assembled product without assembly tolerance faults ( See, e.g., Fig(s). 2 ) . 07-21-aia AIA Claim (s) 4, 11, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of CHYOU ET AL. (US 20220138560 A1) (hereinafter “CHYOU”) . With respect to Claim(s) 15 , VAN DER VELDEN teaches ‘simulating behavior of a modeled object includes storing a tolerance attribute value in a memory area for a specified parameter of the modeled object, defining a set of rules representative of a plurality of assumptions of a model simulation, executing the model simulation based on the tolerance attribute, verifying an output of the model simulation with respect to a set of rules that are dependent on input and output values for which the tolerance attribute as verified, and validating the output behavior against requirements for every stage of the product lifecycle, from preliminary design to end of life’ and the BRI of: A computer program product ( See, e.g., Fig(s). 6, 7 ), the computer program product including a computer readable storage medium having computer readable program code embodied therewith, program instructions executable by a processor ( See, e.g., Fig(s). 6, 7 ) to cause the processor to: receive live data about components ( See, e.g., ¶ 0046 ; See also, e.g., Fig(s). 2- 4B ); generate a tolerance analysis model to simulate tolerances at each operation of the production process ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); feed the live data into the tolerance analysis model, wherein the tolerance analysis model provides an assembly prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); build the assembled product according to the assembly prediction of failure ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039, 0042 ; See also, e.g., Fig(s). 2- 4B ); compare the assembled product to the assembly prediction ( See, e.g., ¶ 0003, 0004, 0016, 0019, 0020, 0027, 0031-0033, 0039 ; See also, e.g., Fig(s). Fig(s). 2- 4B ); retrain the tolerance analysis model with deviations determined from the comparing of the assembly prediction with the assembled product to provide a rework prediction ( See, e.g., Fig(s). 2 ); and rework the assembled product using the rework prediction ( See, e.g., Fig(s). 2 ). However, VAN DER VELDEN is lacking the explicit language of: for layer normalization in machine learning applications; for a production process resulting in an assembled product. TORNQUIST teaches ‘Determining a minimum condition and a maximum condition of an assembly of parts includes determining a subset of the assembly of parts, constructing a tolerance chain comprised of tolerance features associated with the parts and that have tolerances that can assume maximum and minimum values, setting at least one tolerance to a minimum value or a maximum value, and calculating the minimum condition and the maximum condition of the assembly based on the setting of the tolerance’ and the BRI of: for a production process resulting in an assembled product ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include for a production process resulting in an assembled product. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. CHYOU teaches ‘A behavior recommendation apparatus, behavior recommendation method, and non-transitory computer readable storage medium thereof are provided. The behavior recommendation apparatus stores a digital twin model, wherein the digital twin model outputs a predicted parameter set after being inputted a behavior sequence and a monitored parameter set. The behavior sequence includes a plurality of behaviors in a first sequence and quantized data. The behavior recommendation apparatus receives the monitored parameter set and an objective, wherein the objective corresponds to a particular parameter in the monitored parameter set. The behavior recommendation apparatus generates a recommended behavior sequence according to the particular parameter, the monitored parameter set, the digital twin model, and a plurality of simulated behavior sequences and displays the recommended behavior sequence on an operation interface’ and the BRI of: for layer normalization in machine learning applications ( See, e.g., ¶ 0038, 0047, 0053 ; See also, e.g., Fig(s). 1F ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include for layer normalization in machine learning applications. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing behavior condition(s). Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 16 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the tolerance analysis model includes a Monte Carlo simulation for stacking defects ( See, e.g., ¶ 0042, 0053 ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the tolerance analysis model includes a Monte Carlo simulation for stacking defects. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 17 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the live data is real time data from an assembly process, the live data including sub assembly component dimensions, assembly data and component inventory ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the live data is real time data from an assembly process, the live data including sub assembly component dimensions, assembly data and component inventory. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 4, 11, 18 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). VAN DER VELDEN further teaches the BRI of: the tolerance analysis model is performing a Monte Carlo simulation using historical data on the production process ( See, e.g., ¶ 0003, 0018, 0033, 0034, 0039 ). CHYOU further teaches the BRI of: a neural network performing a Monte Carlo simulation using historical data ( See, e.g., ¶ 0038, 0047, 0053 ; See also, e.g., Fig(s). 1F ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include a neural network performing a Monte Carlo simulation using historical data. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing behavior condition(s). Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 19 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). TORNQUIST further teaches the BRI of: the assembly prediction includes a list of components to be assembled into the assembled product, process steps for assembling the components, and testing characteristics indicative of correct assembly ( See, e.g., ¶ ABSTRACT ; See also, e.g., Fig(s). 1-9 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include the assembly prediction includes a list of components to be assembled into the assembled product, process steps for assembling the components, and testing characteristics indicative of correct assembly. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to analyzing condition(s) of assembly of parts. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 20 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). VAN DER VELDEN further teaches the BRI of: the rework prediction includes instructions to rework the assembled product without assembly tolerance faults ( See, e.g., Fig(s). 2 ) . 07-21-aia AIA Claim (s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of MINDLIN (US 12625837 B1) . With respect to Claim(s) 7, 14 , the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s). However, VAN DER VELDEN is lacking the explicit language of: the assembled products is a tape drive. MINDLIN teaches ‘A magnetic tape drive and an assembly for a tape drive’ and the BRI of: assembling a tape drive ( See, e.g., ¶ ABSTRACT ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify VAN DER VELDEN to include assembling a tape drive. One of ordinary skill in the art would have been motivated to modify VAN DER VELDEN because it would be beneficial to improve assembling a tape drive. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT. 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RAYMOND NIMOX Primary Examiner Art Unit 2857 /RAYMOND L NIMOX/Primary Examiner, Art Unit Application/Control Number: 18/601,444 Page 2 Art Unit: 2857 Application/Control Number: 18/601,444 Page 3 Art Unit: 2857 Application/Control Number: 18/601,444 Page 4 Art Unit: 2857 Application/Control Number: 18/601,444 Page 5 Art Unit: 2857 Application/Control Number: 18/601,444 Page 6 Art Unit: 2857 Application/Control Number: 18/601,444 Page 7 Art Unit: 2857 Application/Control Number: 18/601,444 Page 8 Art Unit: 2857 Application/Control Number: 18/601,444 Page 9 Art Unit: 2857 Application/Control Number: 18/601,444 Page 10 Art Unit: 2857 Application/Control Number: 18/601,444 Page 11 Art Unit: 2857 Application/Control Number: 18/601,444 Page 12 Art Unit: 2857 Application/Control Number: 18/601,444 Page 13 Art Unit: 2857 Application/Control Number: 18/601,444 Page 14 Art Unit: 2857 Application/Control Number: 18/601,444 Page 15 Art Unit: 2857 Application/Control Number: 18/601,444 Page 16 Art Unit: 2857 Application/Control Number: 18/601,444 Page 17 Art Unit: 2857 Application/Control Number: 18/601,444 Page 18 Art Unit: 2857 Application/Control Number: 18/601,444 Page 19 Art Unit: 2857 Application/Control Number: 18/601,444 Page 20 Art Unit: 2857 Application/Control Number: 18/601,444 Page 21 Art Unit: 2857 Application/Control Number: 18/601,444 Page 22 Art Unit: 2857 Application/Control Number: 18/601,444 Page 23 Art Unit: 2857
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