DETAILED ACTION
The instant application having application number 18/024,418 filled on 3/2/2023 has a total of 10 claims pending for examination. There is 1 independent claim and 9 dependent claims, all of which are examined below.
The M903 document indicates that the domestic benefit and foreign priority claim has been recognized. Therefore, the application is entitled to the September 11th, 2020 filing date through the International Application No. PCT/JP2021/018338.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted from April 18th, 2023 through June 18th, 2026 have been considered by the examiner.
Drawings
The drawings were received on 3/2/23. These drawings are accepted.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5, 7, and 8 of U.S. Patent No. 12,465,967 B2 in view of Offshore Standard DNV-OS-F101, SUBMARINE PIPELINE SYSTEMS, DET NORSKE VERITAS, (2010), pp. 41-56. (hereinafter "DNV").
Instant Application 18/024,418
US Patent 12,465,967 B2
Mapping presenting the obviousness of claims
1. A steel pipe collapse strength prediction model generation method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe- making strain during steel pipe forming, a coating condition, and a bending strain during construction and, as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
7. A steel pipe manufacturing method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, and a coating condition and, as an output datum for the input datum, a previous collapse strength of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength of a coated steel pipe coated after steel pipe forming;
…
The instant claim is directed to “a steel pipe collapse strength prediction model generation method”, while the patented claim is directed to “a steel pipe manufacturing method”. However, the claims are not patentably distinct based on this difference in preamble because the patented manufacturing method expressly includes the same prediction model generation step. Specifically, patent claim 7 requires performing machine learning of prior coated steel pipe manufacturing characteristics and prior coated steel pipe collapse strength to generate a steel pipe collapse strength prediction model. Therefore, the instant claim merely claims the model generation portion that is already required within the patented manufacturing method. [Mapping A]
The instant claims differ from the patented claims by specifying that the predicted collapse strength is for a coated steel pipe “under external pressure bending” and by adding “bending strain during construction” as an input or changeable characteristic. These differences do not render the claims patentably distinct because DNV shows that those added features were known factors in submarine pipeline collapse analysis. In particular, DNV teaches that submarine pipeline design considers local buckling/system collapse under external pressure, using a characteristic resistance calculation for external pressure collapse (equation 5.10 and 5.11-5.13). DNV further teaches combined loading criteria involving interaction between external or internal pressure, axial force, and bending moment, and specifically requires design checks for pipe members subjected to bending moment, effective axial force and external overpressure (D600 [601-610]). DNV also teaches design checks for longitudinal compressive strain caused by bending moment and axial force under external overpressure (equation 5.31), and teaches that construction phase ovalisation is included in total ovality used in design (D400 [401]).
These disclosures show that external pressure, bending, strain, and construction phase deformation were already known factors that affect local buckling/system collapse of submarine pipelines. Therefore, when applying the patented coated steel pipe ML collapse strength framework to the known external pressure bending condition, a person of ordinary skill in the art would have had reason to include bending strain during construction as an additional input. This is because the bending strain reflects the pipe’s construction bending history, which affects the collapse response under external pressure bending. Including that input would allow the model to account for that known variation and provide a more accurate prediction. Therefore, the instant claims merely recite an obvious variant of the patented claims and are not patentably distinct. Paragraph 3 of the applicants disclosure also supports this mapping [Mapping B]
2. The steel pipe collapse strength prediction model generation method according to claim 1,
wherein a method of the machine learning is a neural network, and
the steel pipe collapse strength prediction model is a prediction model constructed by the neural network.
8. The steel pipe manufacturing method according to claim 7, wherein
the steel pipe collapse strength prediction model is constructed by a neural network.
The differences in the preambles do not render claim 2 patentably distinct because claim 7 already includes the model generation step within the patented manufacturing method. [see Mapping A for more details]
Patent claim 8 already recites that the prediction model is constructed by a neural network, and because patented claim 8 depends from claim 7’s model generation step, the neural network is the machine learning technique used to construct the model. Therefore, the added wording in claim 2 does not create a patentable distinction over patented claim 8.
3. A steel pipe collapse strength prediction method comprising:
inputting, into a steel pipe collapse strength prediction model generated by the steel pipe collapse strength prediction model generation method according to claim 1, a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
7. A steel pipe manufacturing method comprising: …
inputting, into the steel pipe collapse strength prediction model, a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, and a coating condition, to predict a collapse strength of a coated steel pipe coated after steel pipe forming;
…
The differences in the preambles do not render claim 3 patentably distinct because claim 7 already includes the model generation step within the patented manufacturing method. [see Mapping A for more details]
The instant claim recites inputting characteristic into a model “generated by” the method of claim 1, while patented claim 7 recites inputting characteristic into “the steel pipe collapse strength prediction model.” This is not a substantive difference because patent claim 7 previously recited generating that same model using machine learning. Therefore, both claims use the generated steel pipe collapse strength prediction model for predictions.
The remaining differences are that claim 3 adds “bending strain during construction” as an input and limits the predicted value to collapse strength “under external pressure bending.” These differences do not render claim 3 patentably distinct for the same reasons discussed in Mapping B above.
4. A steel pipe manufacturing characteristics determination method comprising:
sequentially changing at least one of a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction included in a steel pipe manufacturing characteristic such that a predicted coated steel pipe collapse strength under external pressure bending by the steel pipe collapse strength prediction method according to claim 3
asymptotically approaches a requested collapse strength under external pressure bending of an intended coated steel pipe, to determine a steel pipe manufacturing characteristic.
7. A steel pipe manufacturing method comprising: …
sequentially changing at least one of the steel pipe shape after steel pipe forming, the steel pipe strength characteristic after steel pipe forming, the pipe-making strain during steel pipe forming, and the coating condition included in the steel pipe manufacturing characteristic such that the predicted collapse strength of the coated steel pipe
asymptotically approaches a requested collapse strength of an intended coated steel pipe, to determine an optimum steel pipe manufacturing characteristic; …
The differences in the preambles do not render claim 4 patentably distinct because patented claim 7 already includes the same manufacturing characteristics determination steps within its manufacturing method.
Claim 4 is not patentably distinct from patented claim 7 because both claims use the same basic determination process of sequentially changing at least one steel pipe manufacturing characteristic so that the predicted collapse strength approaches the requested collapse strength.
The additional mention of determining an optimum manufacturing characteristic does not create a real distinction because claim 4 uses the same sequential changing process to determine the characteristic that meets the requested collapse strength.
The only remaining differences are that claim 4 adds bending strain during construction and limits the predicted/requested collapse strength to external pressure bending, which are not patentably distinct for the same reasons discussed in Mapping B above.
5. A steel pipe manufacturing method comprising:
a coated steel pipe forming step of forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; [i]
a collapse strength prediction step of predicting a collapse strength under external pressure bending of the coated steel pipe formed in the coated steel pipe forming step, by the steel pipe collapse strength prediction method according to claim 3; [ii]
and a performance predictive value assignment step of assigning the coated steel pipe collapse strength under external pressure bending predicted in the collapse strength prediction step to the coated steel pipe formed in the coated steel pipe forming step. [iii]
5. A steel pipe manufacturing method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, and a coating condition and, as an output datum for the input datum, a previous collapse strength of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength of a coated steel pipe coated after steel pipe forming; [same steps as patented claim 7]
forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; [i]
predicting a collapse strength of the coated steel pipe by inputting, into the steel pipe collapse strength prediction model, a steel pipe manufacturing characteristic including a steel pipe shape of the coated steel pipe, a steel pipe strength characteristic of the coated steel pipe, a pipe-making strain during steel pipe forming, and a coating condition, to predict a collapse strength of the coated steel pipe; [ii]
and assigning the predicted coated steel pipe collapse strength to the coated steel pipe. [iii]
Markers [i], [ii], and [iii] have been added to help visualize the claim mapping.
Claim 5 of the instant application is not patentably distinct from patent claim 5 because both claims recite the same steel pipe manufacturing workflow involving forming and coating the steel pipe, predicting the coated steel pipe collapse strength, and assigning the predicted collapse strength to the coated steel pipe.
The phrase “performance predictive value assignment step” does not create a real distinction because patent claim 5 already performs the same assignment by assigning the predicted coated steel pipe collapse strength to the coated steel pipe.
Likewise, the “according to claim 3” wording does not add a separate distinction because claim 3’s prediction method has already been shown to map to the patented prediction step.
The only remaining differences are that instant claim 5 limits the predicted and assigned collapse strength to collapse strength under external pressure bending, which is not patentably distinct for the same reasons discussed in Mapping B above.
6. A steel pipe manufacturing method comprising:
determining a coated steel pipe manufacturing condition in accordance with
a steel pipe manufacturing characteristic determined by the steel pipe manufacturing characteristics determination method according to claim 4;
and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition
7. A steel pipe manufacturing method comprising: …
determining a coated steel pipe manufacturing condition in accordance with
the determined optimum steel pipe manufacturing characteristic;
and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition.
Claim 6 is not patentably distinct from patent claim 7 because both claims recite determining a coated steel pipe manufacturing condition based on the determined steel pipe manufacturing characteristic, and then manufacturing the coated steel pipe under that determined condition.
The “according to claim 4” wording merely identifies the source of the determined steel pipe manufacturing characteristic. It does not add a separate distinction because the same determination process involving sequentially changing manufacturing characteristics to approach a requested collapse strength is already recited in patent claim 7.
Patent claim 7 refers to the determined characteristic as an “optimum” steel pipe manufacturing characteristic, but that does not create a real difference because both claims use the determined characteristic to set the manufacturing condition.
7. A steel pipe collapse strength prediction method comprising:
inputting, into a steel pipe collapse strength prediction model generated by the steel pipe collapse strength prediction model generation method according to claim 2,
a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
7. A steel pipe manufacturing method comprising: …
inputting, into the steel pipe collapse strength prediction model,
a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, and a coating condition, to predict a collapse strength of a coated steel pipe coated after steel pipe forming;
…
Claim 7 is not patentably distinct for substantially the same reasons discussed for claim 3.
Although instant claim 7 is drafted as a steel pipe collapse strength prediction method, while patent claim 7 is drafted as a steel pipe manufacturing method, that preamble difference does not make the claim patentably distinct because patented claim 7 already includes the same prediction step within the manufacturing method. Both claims input steel pipe manufacturing characteristics into a steel pipe collapse strength prediction model to predict coated steel pipe collapse strength.
The “according to claim 2” wording does not create a separate distinction because claim 2’s neural network model has already been shown to map to patent claim 8.
The additional bending strain during construction and collapse strength under external pressure bending limitations are not patentably distinct for the same reasons discussed in Mapping B above.
8. A steel pipe manufacturing characteristics determination method comprising:
sequentially changing at least one of a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction included in a steel pipe manufacturing characteristic such that a predicted coated steel pipe collapse strength under external pressure bending by the steel pipe collapse strength prediction method according to claim 7
asymptotically approaches a requested collapse strength under external pressure bending of an intended coated steel pipe, to determine a steel pipe manufacturing characteristic.
7. A steel pipe manufacturing method comprising: …
sequentially changing at least one of the steel pipe shape after steel pipe forming, the steel pipe strength characteristic after steel pipe forming, the pipe-making strain during steel pipe forming, and the coating condition included in the steel pipe manufacturing characteristic such that the predicted collapse strength of the coated steel pipe
asymptotically approaches a requested collapse strength of an intended coated steel pipe, to determine an optimum steel pipe manufacturing characteristic; …
Claim 8 is not patentably distinct for substantially the same reasons discussed for claim 4. This is because the claim recites the same sequential characteristic
changing process to determine an ideal steel pipe manufacturing characteristic.
The “according to claim 7” wording merely identifies the prediction method used to produce the predicted collapse strength. It does not add a separate distinction because claim 7 has already been shown to map to the patented prediction step, with the neural network aspect addressed by patent claim 8.
The additional bending strain during construction and collapse strength under external pressure bending limitations are not patentably distinct for the same reasons discussed in Mapping B above.
9. A steel pipe manufacturing method comprising:
a coated steel pipe forming step of forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; [i]
a collapse strength prediction step of predicting a collapse strength under external pressure bending of the coated steel pipe formed in the coated steel pipe forming step, by the steel pipe collapse strength prediction method according to claim 7; [ii]
and a performance predictive value assignment step of assigning the coated steel pipe collapse strength under external pressure bending predicted in the collapse strength prediction step to the coated steel pipe formed in the coated steel pipe forming step. [iii]
5. A steel pipe manufacturing method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, and a coating condition and, as an output datum for the input datum, a previous collapse strength of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength of a coated steel pipe coated after steel pipe forming; [same steps as patented claim 7]
forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; [i]
predicting a collapse strength of the coated steel pipe by inputting, into the steel pipe collapse strength prediction model, a steel pipe manufacturing characteristic including a steel pipe shape of the coated steel pipe, a steel pipe strength characteristic of the coated steel pipe, a pipe-making strain during steel pipe forming, and a coating condition, to predict a collapse strength of the coated steel pipe; [ii]
and assigning the predicted coated steel pipe collapse strength to the coated steel pipe. [iii]
Markers [i], [ii], and [iii] have been added to help visualize the claim mapping.
Claim 9 is not patentably distinct for substantially the same reasons discussed for claim 5. This is because both claims recite the same steel pipe manufacturing framework.
The phrase “performance predictive value assignment step” does not create a real distinction because patent claim 5 already recites assigning the predicted coated steel pipe collapse strength to the coated steel pipe.
The “according to claim 7” wording merely identifies the prediction method used to produce the predicted collapse strength. It does not add a separate distinction because claim 7 has already been shown to map to the patented prediction step, with the neural network aspect addressed by patent claim 8.
The additional collapse strength under external pressure bending limitations is not patentably distinct for the same reasons discussed in Mapping B above.
10. A steel pipe manufacturing method comprising:
determining a coated steel pipe manufacturing condition in accordance with
a steel pipe manufacturing characteristic determined by the steel pipe manufacturing characteristics determination method according to claim 8;
and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition.
7. A steel pipe manufacturing method comprising: …
determining a coated steel pipe manufacturing condition in accordance with
the determined optimum steel pipe manufacturing characteristic;
and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition.
Claim 10 is not patentably distinct for substantially the same reasons discussed for claim 6. This is because both claims recite determining a coated steel pipe manufacturing condition based on the determined steel pipe manufacturing characteristic, and then manufacturing the coated steel pipe under that determined condition.
The “according to claim 8” wording merely identifies the source of the determined steel pipe manufacturing characteristic. It does not add a separate distinction because claim 8 has already been shown to map to the patented manufacturing characteristic determination step and neural network usage.
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 thereof, subject to the conditions and requirements of this title.
Claim 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1, Statutory Category:
Yes: Claims 1-10 are directed to a method.
Step 2A Prong I, judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mental processes and mathematical concepts when given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 1 recites the limitations:
A steel pipe collapse strength prediction model generation method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction and, as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
The limitation performing machine learning of a plurality of learning data recites an abstract idea because it is directed to mathematical concepts under MPEP § 2106.04(a)(2)(I), which includes mathematical relationships and mathematical calculations. Machine learning uses mathematical analysis of training data to identify relationships between inputs/outputs and generates a model based on those relationships. Here the claim does not recite a specific improvement to machine learning, a particular training architecture, or a specific technical way the model is generated. Instead, the claim broadly uses machine learning as a mathematical tool to create a prediction model from prior data.
The limitation to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming recites an abstract idea because it is directed to mathematical concepts under MPEP § 2106.04(a)(2)(I), which includes mathematical relationships and mathematical calculations. The claim does not recite any particular improvement to the model structure, training process, or validation process. Instead, the prediction model is recited at a high level of generality as a model generated by analyzing relationships between prior steel pipe input data and prior collapse strength output data. Once that relationships is learned, the model is configured to use the relationship to calculate or estimate a collapse strength value. Therefore, the claim merely uses the prediction model as a computer implemented tool to automate the mathematical analysis of data and produce a predicted value.
Additionally, the prediction aspect of the limitation predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming also recites a mental process under MPEP § 2106.04(a)(2)(III) because it can be practically performed in the human mind or with pen and paper through observation, evaluation, and judgment. The claim is recited at a high level of generality and only requires predicting the value at which a coated steel pipe is expected to collapsed under external pressure bending. Under the broadest reasonable interpretation, a person could review known coated steel pipe characteristics, such as pipe shape, pipe making strain, coating condition, and prior collapse strength results, and then estimate the expected collapse strength for another coated pipe based on the observed relationship between those values.
Step 2A Prong II, Integration into a Practical Application:
Claim 1 recites the following additional claim limitations outside the abstract idea which only present general field of use:
A steel pipe collapse strength prediction model generation method comprising (general field of use, see MPEP § 2106.05(h))
that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction and, as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming (general field of use, see MPEP § 2106.05(h))
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of claim 1 do not amount to significantly more than the judicial exceptions. The claim only limits the abstract machine learning model generation to a specific data set involving coated steel pipe characteristics and collapse strength under external pressure bending. Those limitations describe what data is used, not technical improvements to how the model is generated or how the prediction is applied. Therefore, the additional limitations merely apply the abstract mathematical modeling process to a particular field of use and do not provide an inventive concept.
Regarding claim 2, the claim recites the steel pipe collapse strength prediction model generation method according to claim 1, wherein a method of the machine learning is a neural network, and the steel pipe collapse strength prediction model is a prediction model constructed by the neural network. Claim 2 does not remove or change the abstract limitations identified in claim 1. Instead, claim 2 merely specifies the mathematical technique used to perform the machine learning model generation. A neural network is still used to analyze relationships between input/output data and generate a model that predicts a collapse strength value. The claim does not recite any specific neural network architecture, training improvement, or technical improvement to how the model operates. Therefore, claim 2 only provides a more specific implementation of the abstract machine leaning model generation identified in claim 1 and does not integrate the judicial exception into a practical application or add significantly more than the judicial exception. Accordingly, claim 2 is not patent eligible.
Regarding claim 3, the claim recites a steel pipe collapse strength prediction method comprising: inputting, into a steel pipe collapse strength prediction model generated by the steel pipe collapse strength prediction model generation method according to claim 1, a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming. Claim 3 does not remove or change the abstract limitations identified in claim 1. Instead, it merely uses the generated model as a mathematical tool to analyze input variables and produce a predicted collapse strength value. The claim does not recite any further practical application of the predicted value or any technical improvements to the model. Therefore, claim 3 merely applies the abstract model to coated steel pipe data and does not integrate the judicial exception into a practical application or add significantly more than the judicial exception. Accordingly, claim 3 is not patent eligible.
Regarding claim 4, the claim recites a steel pipe manufacturing characteristics determination method comprising: sequentially changing at least one of a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction included in a steel pipe manufacturing characteristic such that a predicted coated steel pipe collapse strength under external pressure bending by the steel pipe collapse strength prediction method according to claim 3 asymptotically approaches a requested collapse strength under external pressure bending of an intended coated steel pipe, to determine a steel pipe manufacturing characteristic. Claim 4 does not remove or change the abstract limitations identified in claim 1 and 3. Instead, it adds an optimization step that changes input variables and compares the predicted value to a target value until the predicted value approaches the requested value. This is directed to a mathematical concept because it uses calculation, comparison, and adjustment of variables to reach a desired value. The claim does not recite how the steel pipe is actually manufactured or how the determined characteristic is used in a practical way during manufacturing. Therefore, claim 4 merely adds mathematical optimization to the abstract prediction process and does not integrate the judicial exception into a practical application or add significantly more than the judicial exception. Accordingly, claim 4 is not patent eligible.
Regarding claim 5, the claim recites a steel pipe manufacturing method comprising: a coated steel pipe forming step of forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; a collapse strength prediction step of predicting a collapse strength under external pressure bending of the coated steel pipe formed in the coated steel pipe forming step, by the steel pipe collapse strength prediction method according to claim 3; and a performance predictive value assignment step of assigning the coated steel pipe collapse strength under external pressure bending predicted in the collapse strength prediction step to the coated steel pipe formed in the coated steel pipe forming step. Claim 5 does not remove or change the abstract limitations identified in claim 1 and 3. The prediction step remains directed to the abstract idea of determining a collapse strength value from steel pipe characteristics. The assignment step also does not add significantly more because, under broadest reasonable interpretation, a person could mentally associate the predicted value with the coated steel pipe or write the value down as a tag for that pipe. Finally, the recitation of forming or coating a steel pipe does not resolve the rejection because the predicted value is not used in any meaningful way to improve or control those manufacturing steps. Instead, the claim only assigns the predicted value to the already formed and coated steel pipe. Therefore, claim 5 merely applies the abstract prediction process to a physical pipe, without integrating the judicial exception into a practical application or adding significantly more than the judicial exception. Accordingly, claim 5 is not patent eligible.
Regarding claim 6, the claim recites a steel pipe manufacturing method comprising: determining a coated steel pipe manufacturing condition in accordance with a steel pipe manufacturing characteristic determined by the steel pipe manufacturing characteristics determination method according to claim 4; and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition. Claim 6 does not remove or change the abstract limitations identified in claims 1, 3, and 4. The determination step does not add significantly more because, under broadest reasonable interpretation, a person could review the resulting values from claim 4 and use judgement and evaluation to select manufacturing conditions. Finally, the recitation of manufacturing a pipe under the determined manufacturing condition does not resolve the rejection because it merely applies the abstract determination process to a pipe, without integrating the judicial exception into a practical application or adding significantly more than the judicial exception (Apply It; MPEP 2106.05(f)). Accordingly, claim 6 is not patent eligible.
Regarding claim 7, the claim recites a steel pipe collapse strength prediction method comprising: inputting, into a steel pipe collapse strength prediction model generated by the steel pipe collapse strength prediction model generation method according to claim 2, a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming. Claim 7 does not remove or change the abstract limitations identified in claim 1 and 2. This is because claim 7 recites substantially the same prediction method as claim 3. The only difference is that claim 7 depends from claim 2 rather than claim 1. As discussed above, claim 2’s addition of a generic neural network does not resolve the rejection because it does not recite a specific improvement to machine learning or computer functionality. Therefore, claim 7 merely applies the same abstract prediction process using a generically recited neural network, without integrating the judicial exception into a practical application or adding significantly more than the judicial exception. Accordingly, claim 7 is not patent eligible.
Regarding claim 8, the claim recites a steel pipe manufacturing characteristics determination method comprising: sequentially changing at least one of a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction included in a steel pipe manufacturing characteristic such that a predicted coated steel pipe collapse strength under external pressure bending by the steel pipe collapse strength prediction method according to claim 7 asymptotically approaches a requested collapse strength under external pressure bending of an intended coated steel pipe, to determine a steel pipe manufacturing characteristic. Claim 8 does not remove or change the abstract limitations identified above. This is because claim 8 is substantially the same as claim 4, but relies on the prediction method of claim 7 instead of claim 3. That difference does not change the rejection because, as discussed above, claim 7 only applies the same abstract prediction process using a generically recited neural network. Therefore, for the same reasons discussed for claim 4, claim 8 merely adds mathematical optimization to the abstract prediction process without integrating the judicial exception into a practical application or adding significantly more than the judicial exception. Accordingly, claim 8 is not patent eligible.
Regarding claim 9, the claim recites a steel pipe manufacturing method comprising: a coated steel pipe forming step of forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; a collapse strength prediction step of predicting a collapse strength under external pressure bending of the coated steel pipe formed in the coated steel pipe forming step, by the steel pipe collapse strength prediction method according to claim 7; and a performance predictive value assignment step of assigning the coated steel pipe collapse strength under external pressure bending predicted in the collapse strength prediction step to the coated steel pipe formed in the coated steel pipe forming step. Claim 9 does not remove or change the abstract limitations identified above. This is because claim 9 is substantially the same as claim 5, but relies on the prediction method of claim 7 instead of claim 3. That difference does not change the rejection because, as discussed above, claim 7 only applies the same abstract prediction process using a generically recited neural network. Therefore, for the same reasons discussed for claim 5, claim 9 merely applies the abstract prediction process to a physical pipe and assigns the predicted value to that pipe without integrating the judicial exception into a practical application or adding significantly more than the judicial exception. Accordingly, claim 9 is not patent eligible.
Regarding claim 10, the claim recites a steel pipe manufacturing method comprising: determining a coated steel pipe manufacturing condition in accordance with a steel pipe manufacturing characteristic determined by the steel pipe manufacturing characteristics determination method according to claim 8; and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition. Claim 10 does not remove or change the abstract limitations identified in claims 1, 2, 7, and 8. The determination step does not add significantly more because, under broadest reasonable interpretation, a person could review the resulting values from claim 8 and use judgement and evaluation to select manufacturing conditions. Finally, the recitation of manufacturing a pipe under the selected manufacturing conditions does not resolve the rejection because it merely applies the abstract determination process to a physical pipe, without integrating the judicial exception into a practical application or adding significantly more than the judicial exception (Apply It; MPEP 2106.05(f)). Accordingly, claim 10 is not patent eligible.
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.
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.
Claims 1-10 are rejected under 35 U.S.C. § 103 as being unpatentable over Nagata et al., U.S. Patent Application Publication No. 2020/0034403 A1 (hereinafter "Nagata") in view of DJERRAD et al., "Artificial Neural Networks (ANN) Based Compressive Strength Prediction of AFRP Strengthened Steel Tube", International Journal of Steel Structures, 2020, Vol. 20, No. 1, pp. 156-174. (hereinafter "Djerrad "), and further in view of Offshore Standard DNV-OS-F101, SUBMARINE PIPELINE SYSTEMS, DET NORSKE VERITAS, (2010), pp. 41-56. (hereinafter "DNV")
Regarding Claim 1, Nagata teaches a steel pipe collapse strength prediction model generation method (“A collapse strength prediction method according to the present embodiment is a method for predicting the collapse strength of a steel pipe and includes a step of deriving a prediction equation indicating a relationship among D/t obtained by dividing an outer diameter D (mm) by a thickness t (mm), material characteristics, a collapse strength dominant factor, a collapse dominant proof stress, and a predicted collaspe strength of the steel pipe using a plurality of reference steel pipes the collapse whose strengths have been obtained in advance.”) (e.g., Nagata, paragraph [0048]).
that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming (“First, a prediction equation for predicting the collapse strength of the steel pipe is derived using a plurality of reference steel pipes the collapse whose strengths have been obtained in advance. As the prediction equation, an equation including parameters that indicate the relationship among the ratio D/t between the outer diameter D (mm) and the thickness t (mm), the material characteristics, the collapse strength dominant factor, the collapse dominant proof stress, and the predicted collapse strength of the steel pipe is preferably used … Next, for a steel pipe that is the evaluation subject, the ratio D/t between the outer diameter D (mm) and the thickness t (mm), the material characteristics, the collapse strength dominant factor, and the like are obtained.”)(e.g., Nagata, paragraph [0053]-[0054]). Nagata D/t corresponds to a steel pipe shape after steel pipe forming because D/t is based on the outer diameter and wall thickness of the steel pipe itself, which are dimensions of the formed pipe.
a steel pipe strength characteristic after steel pipe forming (“In the collapse strength prediction method according to the present embodiment, the material characteristics may include the Young's modulus and the Poisson's ratio of the steel pipe that is the evaluation subject. In addition, the collapse strength dominant factor may include one or more selected from the ovality, the eccentricity, and the residual stress in the circumferential direction of the steel pipe” and “Next, the compressive stress-strain curve (SS curve) in the circumferential direction (C direction) of the steel pipe is obtained. The compressive stress-strain curve is obtained by sampling a cylindrical test piece in the circumferential direction and carrying out a compression test.”)(e.g., Nagata, paragraph [0061] and [0063]). Nagata’s Young's modulus, Poisson's ratio, and compressive stress-strain curve correspond to steel pipe strength characteristic after steel pipe forming because they are obtained from the steel pipe itself, including a test piece sampled in the pipe’s circumferential direction, rather than from the raw steel material before pipe forming.
Nagata alone does not specifically teach performing machine learning of a plurality of learning data … a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction and, as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
However, Djerrad teaches performing machine learning of a plurality of learning data … a coating condition (“The use of FRP composites as external confinement has recently become a very important system to consider when reinforcing concrete and steel structures … In this research, an artificial neural network (ANN) based model is used to estimate the compressive strength, maximum stresses and strains of AFRP strengthened circular hollow section steel tubes under axial compression. A database of 129 cases of finite element model (FEM) was analyzed … Using this FEM database, ANNs have been trained using two approaches.”)(e.g., Djerrad, Abstract and Table 4 input column “AFRP Thickness”). Djerrad’s FEM databased corresponds to the plurality of learning data, the trained ANN corresponds to performing machine learning, and Djerrad’s AFRP strengthened steel tube teaches the coating condition because the AFRP is externally applied over the steel tube as a strengthening material.
Neither Nagata nor Djerrad specifically teach a pipe-making strain during steel pipe forming, and a bending strain during construction and, as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
However, DNV teaches a pipe-making strain during steel pipe forming and a bending strain during construction (“For manufacturing processes which introduce cold deformations giving different strength in tension and compres-sion, a fabrication factor, αfab, shall be determined. If no other information exists, maximum fabrication factors for pipes manufactured by the UOE or UO processes are given in Table 5-7” and “In this case the residual strain from bending at the over-bend shall satisfy the following during installation”)(DNV, Section 5 C307 and H203). DNV’s cold deformations introduced by UOE or UO pipe manufacturing correspond to pipe-making strain during steel pipe forming because they are deformations to the pipe during the pipe forming process. DNV’s residual strain from bending at the overbend during installation corresponds to bending strain during construction because installation bending is part of pipeline construction.
DNV alone does not specifically teach as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
However, Nagata in view of Djerrad, further in view of DNV teaches as an output datum for the input datum, a previous collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming. (“Meanwhile, the coefficients α, β, ζ, and η in the equations are coefficients that are obtained in advance. A method for computing these coefficients is not particularly limited, and the coefficients can be determined using, for example, the least-square method from errors between the actual measurement values and the predicted values of a plurality of reference steel pipes the collapse whose strengths have been obtained in advance.” and “computing the predicted collapse strength of the steel pipe that is the evaluation subject from the D/t, the material characteristics, the collapse strength dominant factor, and the collapse dominant proof stress, which have been obtained, on the basis of the prediction equation.”)( e.g., Nagata, paragraph [0087] and [0051]). Nagata’s actual measurement values and known collapse strengths correspond to previous collapse strength output data because they are the known collapse strength results used to determine the prediction equation. Nagata’s prediction equation corresponds to the claimed steel pipe collapse strength prediction model because the equation is generated by fitting coefficients from the prior actual/predicted collapse strength data and is then used to compute the predicted collapse strength of the evaluation pipe. (“Pipe members subjected to bending moment, effective axial force and external overpressure shall be designed to satisfy” and “Pipe members subjected to longitudinal compressive strain (bending moment and axial force) and external over pressure shall be designed to satisfy the following condition at all cross sections”) (DNV, Section 5 D607 and D609). (“The use of FRP composites as external confinement has recently become a very important system to consider when reinforcing concrete and steel structures … In this research, an artificial neural network (ANN) based model is used to estimate the compressive strength, maximum stresses and strains of AFRP strengthened circular hollow section steel tubes under axial compression.”)(e.g., Djerrad, Abstract)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Nagata, Djerrad, and DNV before him or her, to improve Nagata’s steel pipe collapse strength prediction method by implementing Nagata’s prediction model using Djerrad’s neural network training technique. The improvement would have included training the neural network using Nagata’s previously obtained collapse strengths as output data and the corresponding pipe characteristics as input data, while further including the coating condition taught by Djerrad and the pipe deformation and installation bending strain taught by DNV. The resulting neural network would predict the collapse strength of a formed and coated steel pipe under the combined external pressure bending condition addressed by DNV.
A person of ordinary skill in the art would have looked to Djerrad and DNV when improving Nagata because the references address closely related problems involving the structural strength of steel pipes and circular tubes. Nagata is directed to accurately predicting the collapse strength of steel pipes, including seabed pipelines subjected to external pressure. DNV is similarly directed to the structural design and failure resistance of submarine pipelines and specifically addresses collapse and buckling under external pressure and bending, as well as the effects of pipe fabrication, coating, and installation strain. Djerrad is also reasonably pertinent because it teaches predicting the strength, stress, and strain of externally coated circular steel tubes from geometric and coating parameters using an ANN trained with finite element data. Therefore, the references are analogous because they are all directed to predicting or evaluating the structural strength of steel pipes or tubes based on characteristics that affect their response to compressive loads.
A person of ordinary skill in the art would have been motivated to implement Nagata’s prediction model using Djerrad’s neural network technique because Djerrad provides a solution to the computation burden identified by Nagata. Nagata recognizes that FEA can accurately estimate collapse strength but requires a great amount of effort (paragraph 5). Djerrad teaches replacing repeated finite element analyses with a trained ANN and reports that the ANN produced predictions that correlated well with finite element and experimental results while reducing computational time and labor (Abstract). Therefore, using Djerrad’s ANN to model the relationship between Nagata’s steel pipe characteristics and known collapse strengths would have yielded the predictable result of maintaining accurate collapse strength predictions while reducing the time and effort required for repeated finite element analyses.
A person of ordinary skill in the art would have been further motivated to include DNV’s pipe forming deformation and installation bending strain in the Nagata-Djerrad framework because Nagata emphasizes the need to accurately predict the collapse strength of seabed pipelines subjected to external pressure, where a collapse may have catastrophic consequences (paragraph 3). DNV addresses the same operating environment and teaches that cold deformations introduced during pipe forming and residual bending strain during installation affect pipeline collapse and buckling resistance under external pressure/bending. Because these manufacturing and installation effects remain in the completed pipeline and alter its resistance to collapse, one of ordinary skill in the art would have included them as additional inputs to the Nagata-Djerrad neural network, in order to yield the predictable result of a more accurate collapse strength prediction.
Regarding Claim 2, Djerrad teaches the steel pipe collapse strength prediction model generation method according to claim 1, wherein a method of the machine learning is a neural network, and the steel pipe collapse strength prediction model is a prediction model constructed by the neural network. (“In the present study, the Artificial Neural Network approach was employed to predict the ultimate compressive strength, maximum equivalent Von Mises stress and strain of AFRP strengthening steel tubes using different geometrical parameters … The generated data from FEA analysis is used for training and testing the Artificial Neural Network models. The ANN models are developed using MATLAB neural network tools and ANSYS built-in neural network.”) (e.g., Djerrad, Introduction)
Regarding Claim 3, Nagata teaches a steel pipe collapse strength prediction method comprising: inputting, into a steel pipe collapse strength prediction model generated by the steel pipe collapse strength prediction model generation method according to claim 1, a steel pipe manufacturing characteristic … to predict a collapse strength (“Next, for a steel pipe that is the evaluation subject, the ratio D/t between the outer diameter D (mm) and the thickness t (mm), the material characteristics, the collapse strength dominant factor, and the like are obtained.” and “A predicted collapse strength of the steel pipe is computed from the D/t, the material characteristics, the collapse strength dominant factor, and the collapse dominant proof stress, which have been obtained in advance, using a prediction equation represented by Equation 3.”)(e.g., Nagata, paragraphs [0054] and [0078])
a steel pipe manufacturing characteristic including a steel pipe shape of a coated steel pipe to be predicted after steel pipe forming (“A predicted collapse strength of the steel pipe is computed from the D/t, the material characteristics, the collapse strength dominant factor, and the collapse dominant proof stress, which have been obtained in advance, using a prediction equation represented by Equation 3.”)(e.g., Nagata, paragraph [0078])
a steel pipe manufacturing characteristic including … a steel pipe strength characteristic after steel pipe forming (“In addition, a stress corresponding to the permanent strain set according to the value of the D/t of the steel pipe that is the evaluation subject is obtained on the basis of the compressive stress-strain curve, and this proof stress is considered as the collapse dominant proof stress.” and “A predicted collapse strength of the steel pipe is computed from the D/t, the material characteristics, the collapse strength dominant factor, and the collapse dominant proof stress, which have been obtained in advance, using a prediction equation represented by Equation 3.”)(e.g., Nagata, paragraphs [0067] and [0078])
Nagata does not explicitly teach a steel pipe manufacturing characteristic including … a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
However, Djerrad teaches a steel pipe manufacturing characteristic including … a coating condition (“The use of FRP composites as external confinement has recently become a very important system to consider when reinforcing concrete and steel structures … In this research, an artificial neural network (ANN) based model is used to estimate the compressive strength, maximum stresses and strains of AFRP strengthened circular hollow section steel tubes under axial compression.”) (e.g., Djerrad, Abstract and Table 4 input column “AFRP Thickness”). Under the broadest reasonable interpretation, the AFRP thickness is a characteristic of the coating condition because the AFRP is the externally applied strengthening layer on the steel tube, and its thickness defines a condition of that applied coating that affects the predicted strength, stress, and strain of the tube.
Djerrad does not explicitly teach a steel pipe manufacturing characteristic including … a pipe-making strain during steel pipe forming, and a bending strain during construction, to predict a collapse strength under external pressure bending of a coated steel pipe coated after steel pipe forming.
However, DNV teaches a steel pipe manufacturing characteristic including … a pipe-making strain during steel pipe forming, and a bending strain during construction, to predict a collapse strength under external pressure bending (“For manufacturing processes which introduce cold deformations giving different strength in tension and compres-sion, a fabrication factor, αfab, shall be determined. If no other information exists, maximum fabrication factors for pipes manufactured by the UOE or UO processes are given in Table 5-7”, “the residual strain from bending at the over-bend shall satisfy the following during installation”, and “Pipe members subjected to bending moment, effective axial force and external overpressure shall be designed to satisfy…”)(DNV, Section 5 C307, H203, and D607). DNV is not merely mentioning manufacturing and construction conditions in isolation. DNV uses those conditions in the context of external pressure collapse design. The fabrication factor accounts for cold deformation from pipe manufacturing, construction phase ovalisation is included in the collapse design ovality, and the combined loading check applies where the pipe is subjected to bending moment and external overpressure. Therefore, DNV supports using pipe making strain and construction bending strain as relevant input characteristics for predicting collapse strength under external pressure bending. This mapping is further supported by paragraph 3 of the applicant’s disclosure, which states that DNV proposed an “estimation equation for predicting the collapse strength under external pressure bending” from data including ovality, yield stress, Young's modulus, Poisson's ratio, and “the bending strain during construction”.
Accordingly, in the combined Nagata-Djerrad-DNV framework, Nagata’s steel pipe shape and strength characteristics, Djerrad’s coating condition, and DNV’s pipe forming deformations and installation bending strain are supplied as input to the previously generated neural network model. The model then evaluates those characteristics together to predict the collapse strength of the formed and coated steel pipe under the combined effects of external pressure and bending. As discussed above with respect to claim 1, one of ordinary skill in the art would have been motivated to include those additional manufacturing and installation characteristics because they remain in the completed pipeline and affect its resistance to collapse, therefore including them would yield the predictable result of a more accurate collapse strength prediction.
Regarding Claim 4, Nagata in view of Djerrad, further in view of DNV (as state above) teaches a steel pipe manufacturing characteristics determination method comprising: sequentially changing at least one of a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, a pipe-making strain during steel pipe forming, a coating condition, and a bending strain during construction included in a steel pipe manufacturing characteristic such that a predicted coated steel pipe collapse strength under external pressure bending by the steel pipe collapse strength prediction method according to claim 3 asymptotically approaches a requested collapse strength under external pressure bending of an intended coated steel pipe, to determine a steel pipe manufacturing characteristic. The examiner is selecting steel pipe shape after steel pipe forming from the provided options. (“The adopted neural network can be used as a conventional function to predict further analysis within the range of parameters. Therefore, a limited parametric study was then performed to extend the existing database on the compressive strength of AFRP strengthening steel tubes by varying the geometrical parameters. The ultimate load capacity (P) was the only output parameter considered in this parametric study. The ANSYS-ANN model (4-6-3) is used to find out the influence of each input parameters on the behavior of the structure. Design parameters and their ranges considered for the parametric study are as follows: tube height (630 mm < L < 1700 mm), steel thickness (1 mm < ts < 2 mm), AFRP thickness (0 mm < tf < 3 mm), tube diameter (52 mm < D < 100 mm) and equivalent slenderness ratio (20 < λ < 86). Material properties and fiber orientation are kept the same for the whole analysis. For any investigated parameter, the others are kept constant.” and “A nonlinear relation between parameters is evaluated using Spearman’s correlation coefficient and a global sensitivity chart is generated to establish the overall correlation between input and output parameters.”) (e.g., Djerrad, Section 8 and 9). Djerrad teaches varying geometrical parameters including tube height, steel thickness, tube diameter, and slenderness ratio, while keeping the remaining parameters constant, and evaluating the relationship between the changed parameters and predicted strength using correlation and sensitivity analyses. Under the broadest reasonable interpretation, this teaches changing the steel pipe shape such that predicted strength approaches the requested strength because the claim does not require a particular convergence algorithm.
Regarding Claim 5, Nagata in view of Djerrad, further in view of DNV (as state above) teaches a coated steel pipe forming step of forming a steel pipe and coating the formed steel pipe to form a coated steel pipe; a collapse strength prediction step of predicting a collapse strength under external pressure bending of the coated steel pipe formed in the coated steel pipe forming step, by the steel pipe collapse strength prediction method according to claim 3; and a performance predictive value assignment step of assigning the coated steel pipe collapse strength under external pressure bending predicted in the collapse strength prediction step to the coated steel pipe formed in the coated steel pipe forming step (“For steel pipes having a shape shown in Tables 1 to 4, collapse strengths obtained by the finite element analyses (FEA) and predicted collapse strengths estimated using a method of the related art and the prediction method according to the present invention were compared with each other. The values of D/t of the steel pipes were any of 10, 19, 28, 32, or 48.”)(e.g., Nagata, paragraph [0111] and Tables 1-4). Under the broadest reasonable interpretation, Nagata meets these limitations by evaluating existing steel pipes that have already been shaped, treated, and characterized. Nagata then inputs the characteristics of each pipe into the collapse strength prediction model, which calculates a predicted collapse strength specifically for that pipe. Because the resulting value is associated with the same pipe whose characteristics were used to calculate it, Nagata links the predictive performance value to the corresponding formed steel pipe. This association satisfies the claimed assignment step because the claim does not require any particular manner of assigning the value.
Regarding Claim 6, Nagata in view of Djerrad, further in view of DNV (as state above) teaches a steel pipe manufacturing method comprising: determining a coated steel pipe manufacturing condition in accordance with a steel pipe manufacturing characteristic determined by the steel pipe manufacturing characteristics determination method according to claim 4; and manufacturing a coated steel pipe under the determined coated steel pipe manufacturing condition (“The sensitivity analysis shows that all of the design parameters cited above contribute more or less on the load-carrying capacity. This could help researchers and designers make decisions to meet requirements when designing a particular system.”)(e.g., Djerrad, Section 9). Djerrad’s sensitivity analysis identifies how design parameters, including tube diameter, steel thickness, and AFRP thickness, affect the load-carrying capacity of the coated steel tube. The analysis therefore helps a designer select the parameter values needed to meet the required specifications and load capacity. Because those selected values define the physical characteristics of the desired product, the designer would use them as manufacturing conditions when producing the pipe.
Regarding Claims 7-10, claims 7-10 recite substantially the same limitations as claims 3-6, respectively, but further require that the prediction model used in the corresponding method is the neural network model of claim 2. As discussed above with respect to claim 2, Djerrad teaches a prediction model constructed by a neural network (“In the present study, the Artificial Neural Network approach was employed to predict the ultimate compressive strength, maximum equivalent Von Mises stress and strain of AFRP strengthening steel tubes using different geometrical parameters … The generated data from FEA analysis is used for training and testing the Artificial Neural Network models. The ANN models are developed using MATLAB neural network tools and ANSYS built-in neural network.”)(e.g., Djerrad, Introduction)
Therefore, claims 7-10 are rejected under 35 U.S.C. § 103 for the same reasons set forth above with respect to claims 3-6.
Conclusion
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
MOHAMADIAN et al., "A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning", Journal of Petroleum Science and Engineering, Vol. 196, 2021. Mohamadian teaches using machine learning to predict casing collapse in oil and gas wells. Mohamadian uses well log data with hybrid neural network models to predict geomechanical parameters related to casing collapse, including Poisson’s ratio and maximum horizontal stress. Mohamadian is pertinent to applicant's disclosure because both references address collapse prediction for steel pipe or casing structures used in high pressure environments. Mohamadian is especially relevant because it applies machine learning to engineering data for evaluating collapse behavior, while applicant similarly uses steel pipe characteristics and prior collapse data to generate a model for predicting collapse strength.
LI et al., "Prediction of Casing Collapse Strength Based on Bayesian Neural Network", Processes, Vol. 10, No. 7, Article 1327, July 2022. Li teaches predicting casing collapse strength using a Bayesian regularized artificial neural network. Li explains that casing collapse is affected by several factors, including geometric size, mechanical parameters, ovality, uneven wall thickness, and residual stress, which can make conventional formula-based prediction less accurate. To address this, Li uses characteristics and collapse strength data to train a neural network model for predicting external collapse strength. Li is pertinent to applicant's disclosure because both relate to using machine learning models to predict collapse strength based on pipe or casing characteristics. It is worth mentioning that Li was published after the application’s earliest priority date, but before the March 2nd, 2023 application filling date.
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/AREEBAH FATIMA/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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