DETAILED ACTION
In the response filed November 13, 2025, the Applicant amended claims 1, 2, 11, and 12; canceled claims 4 and 14; and added claims 21 and 22. Claims 1-3, 5-13, and 15-22 are pending in the current application.
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The drawings were objected to for informalities. Examiner thanks the Applicant for revising and amending the disclosure and hereby withdraws the objection from the previous Office action.
Applicant’s arguments for claims 1-3, 5-13, and 15-22 with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims integrate the judicial exception into a practical application. Examiner respectfully disagrees. Here, under broadest reasonable interpretation, the amended steps describe or set-forth tagging required parts/elements in texts of design requirements of a product and determine whether the product contains the required parts/elements, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
The claims recite the additional elements/limitations of: “a computing system comprising: a processor coupled to a memory that stores instructions,” (claim 1); and “a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and “perform natural language processing on the input text, using a natural language processing model” (claims 1 and 11).
Here, the requirement to execute the claimed steps/functions identified by the Applicant (e.g., “a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and “perform natural language processing on the input text, using a natural language processing model” (claims 1 and 11)), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s arguments remain unpersuasive.
Applicant argues that the claims integrate the judicial exception into a practical application as the trained models and predefined rubrics improve the accuracy and efficiency of compliance verification in the field of aerospace engineering. Examiner respectfully disagrees. Here, the claims as defined in the identified abstract idea describe concepts performed in the human mind. The execution of the defined abstract idea “using a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and performing “natural language processing on the input text, using a natural language processing model” amount to performing these functions with a programmed general-purpose computer and/or limits the claim to a particular technological environment or field of use. The underlying process is one that amounts to a “mental processes” subject matter grouping of abstract ideas. A generically-recited general-purpose computer programmed to perform certain tasks/functions does not constitute a practical application in the field of aerospace engineering.
Applicant reminds examiner to avoid improperly categorizing complex machine learning operations as abstract algorithms and analyzing elements in isolation rather than considering their integrated technological contribution. Here, the claims simply append “a trained machine learning model” and “a natural language processing model” to the abstract idea without significantly more. Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-3, 5-10, and 21, are drawn to a device and claims 11-13, 15-20, and 22, are drawn to a process, each of which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 (representative of claim 11) recites/describes the following steps:
“store input text of a product requirement for a target aerospace component;”
“identify a requirement type for the input text from among a plurality of candidate requirement types,”
“determine a plurality of required elements for the identified requirement type from among a plurality of candidate elements, using a predefined requirements rubric that determines, for each requirement type, a respective set of required elements applicable to the product requirement for the target aerospace component;”
“…apply one or more tags from a predefined tagset to respective portions of the input text to generate tagged input text;”
“apply one or more predefined element identification rules to the tagged input text, to thereby identify a presence or absence of each of the plurality of required elements in the input text for the product requirement;” and
“output an indication of the identified presence or absence of each of the plurality of the required elements in the product requirement, wherein the indication indicates non-compliance of the product requirement for the target aerospace component with the predefined requirements rubric for the requirement type, thereby identifying missing information or errors in the product requirement.”
These steps, under broadest reasonable interpretation, describe or set-forth tagging required parts/elements in texts of design requirements of a product and determine whether the product contains the required parts/elements, which amounts to concepts performed in the human mind (including an observation, evaluation, judgment, opinion). These limitations therefore fall within the “mental processes” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Each of the depending claims 2, 3, 5-10, 12, 13, and 15-22, likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any elements recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A – Prong Two:
The claims recite the additional elements/limitations of: “a computing system comprising: a processor coupled to a memory that stores instructions,” (claim 1); and “a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and “perform natural language processing on the input text, using a natural language processing model” (claims 1 and 11).
The requirement to execute the claimed steps/functions using “a computing system comprising: a processor coupled to a memory that stores instructions,” (claim 1); and “a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and “perform natural language processing on the input text, using a natural language processing model” (claims 1 and 11), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 2, 3, 5-10, 12, 13, and 15-22, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a computing system comprising: a processor coupled to a memory that stores instructions,” (claim 1); and “a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text;” and “perform natural language processing on the input text, using a natural language processing model” (claims 1 and 11), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 2, 3, 5-10, 12, 13, and 15-22, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
Allowable Subject Matter
Claims 1-3, 5-13, and 15-22 would be allowable subject matter if revised and amended to overcome the rejection under 35 U.S.C. 101 as set forth in this Office action.
As per claim 1 (representative of claim 11), the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “identify a requirement type for the input text from among a plurality of candidate requirement types, using a trained machine learning model that has been trained using a training data set that includes input texts from historical product requirements and ground truth data indicating the requirement type for each input text; determine a plurality of required elements for the identified requirement type from among a plurality of candidate elements, using a predefined requirements rubric that determines, for each requirement type, a respective set of required elements applicable to the product requirement for the target aerospace component; perform natural language processing on the input text, using a natural language processing model, to thereby apply one or more tags from a predefined tagset to respective portions of the input text to generate tagged input text; apply one or more predefined element identification rules to the tagged input text, to thereby identify a presence or absence of each of the plurality of required elements in the input text for the product requirement; and output an indication of the identified presence or absence of each of the plurality of the required elements in the product requirement, wherein the indication indicates non-compliance of the product requirement for the target aerospace component with the predefined requirements rubric for the requirement type, thereby identifying missing information or errors in the product requirement.” This combination of functions/features would not have been obvious to a PHOSITA in view of the prior art.
Claims 2, 3, 5-10, 12, 13, and 15-22, depend upon independent claims 1 and 11, have all the limitations of claims 1 and 11, and are allowable for the same reason.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Steingrimsson et al. (US 10,853,536 B1) discloses a system where design oversights are identified through proper structuring of the engineering design requirements, extraction of relevant design parameters through application program interfaces provided by the pertinent design tools, and mapping against the requirements. Big data analytics are applied to repositories of past designs, for the purpose of improving new designs. The Engine can be used stand-alone, as a part of a design ecosystem, or integrated into existing systems for product lifecycle or data management.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Patrick Kim/Examiner, Art Unit 3628
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626