DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are currently pending in application18/968,460.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/4/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,190,331. Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions disclose equivalent elements for logistic-based carbon emission optimization using machine-learning.
18/968,460
US 12,190,331
Independent Claims 1 and 11
An apparatus/ method for carbon emission optimization using machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an integrated logistics data collection;
determine at least one projected carbon emission as a function of the integrated logistics data collection, wherein determining the at least one projected carbon emission comprises:
identifying one or more emission factors associated with the integrated logistics data collection;
generate at least one transportation plan as a function of the integrated logistics data collection and the at least one projected carbon emission;
continuously receive a current logistics datum from an external source;
update the at least one transportation plan as a function of at least one carbon emission offset, wherein updating the at least one transportation plan as a function of at least one carbon emission offset comprises:
identifying a carbon emission outlier as a function of the current logistics datum and the one or more emission factors associated with the integrated logistics data collection, wherein the one or more emission factors associated with the integrated logistics data collection comprises an emission threshold;
determining at least one carbon emission offset as a function of the carbon emission outlier and a machine-learning model;
updating the transportation plan to incorporate the at least one carbon emission offset; and
retraining the machine-learning model using outputs of each iteration of the machine-learning model comprising at least one carbon emission offset; and
output the updated transportation plan to a requesting party.
Independent Claims 1 and 11
An apparatus/ method for carbon emission optimization using machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an integrated logistics data collection;
determine at least one projected carbon emission as a function of the integrated logistics data collection, wherein determining the at least one projected carbon emission comprises:
training a carbon emission projection model using carbon emission training data, wherein the carbon emission training data comprises a plurality of logistics datasets as input correlated to a plurality of historical carbon emissions as output, wherein training the carbon emission projection model comprises:
updating the carbon emission training data as a function of the inputs and outputs of a previous iteration of the carbon emission projection model; and
retraining the carbon emission projection model using the updated carbon emission training data; and
determining the at least one projected carbon emission as a function of the integrated logistics data collection using the trained carbon emission projection model;
generate at least one transportation plan as a function of the integrated logistics data collection and the at least one projected carbon emission;
continuously receive a current logistics datum from an external source; and
iteratively modify the at least one transportation plan based on the current logistics datum, wherein iteratively modifying the at least one transportation plan comprises:
identifying a carbon emission outlier as a function of the current logistics datum and the trained carbon emission projection model;
determining at least one carbon emission offset as a function of the carbon emission outlier; and
updating the transportation plan to incorporate the at least one carbon emission offset.
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, 9-13, 15, and 19-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea.
Claims 1-3, 5, 9-13, 15, and 19-20 are directed to a judicial exception (i.e., abstract idea), without providing a practical application, and without providing significantly more.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1,
Claims 1-3, 5, 9-10 are directed toward an apparatus (system). Claims 11-13, 15, and 19-20 are directed toward a process (method). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1],
Claims 1-3, 5, 9-13, 15, and 19-20 are directed toward the judicial exception of an abstract idea. Independent claims 1 and 11 are directed specifically to the abstract idea of logistics planning.
Regarding independent claims 1 and 11, the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention:
An apparatus/ method for carbon emission optimization using machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an integrated logistics data collection;
determine at least one projected carbon emission as a function of the integrated logistics data collection, wherein determining the at least one projected carbon emission comprises:
identifying one or more emission factors associated with the integrated logistics data collection;
generate at least one transportation plan as a function of the integrated logistics data collection and the at least one projected carbon emission;
continuously receive a current logistics datum from an external source;
update the at least one transportation plan as a function of at least one carbon emission offset, wherein updating the at least one transportation plan as a function of at least one carbon emission offset comprises:
identifying a carbon emission outlier as a function of the current logistics datum and the one or more emission factors associated with the integrated logistics data collection, wherein the one or more emission factors associated with the integrated logistics data collection comprises an emission threshold;
determining at least one carbon emission offset as a function of the carbon emission outlier and a machine-learning model;
updating the transportation plan to incorporate the at least one carbon emission offset; and
retraining the machine-learning model using outputs of each iteration of the machine-learning model comprising at least one carbon emission offset; and
output the updated transportation plan to a requesting party.
As the underlined claim limitations above demonstrate, independent claims 1 and 11 are directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)); and Certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)).
Dependent claims 2-3, 5, 9-10, 12-13, 15, and 19-20 provide further details to the abstract idea of claims 1 and 11 regarding the received data, therefore, these claims include mathematical concepts, mental processes, and certain methods of organizing human activities for similar reasons provided above for claims 1 and 11.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Regarding Step 2A [prong 2],
Claims 1-3, 5, 9-13, 15, and 19-20 fail to integrate the recited judicial exception into any practical application. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as an “apparatus”, “machine-learning”, a “processor”, and “memory”. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of an abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The presence of a machine learning model or computer implementations do not necessarily restrict the claim from reciting an abstract idea. The machine learning model and computer limitations claimed herein are simply used as a tool to apply the abstract idea without transforming the underlying abstract idea into patent eligible subject matter. As claimed, the machine learning model fails to claim specific model training methodologies and/or model architectures. Machine learning features must be anchored to specific, tangible technical implementations rather than general "apply it" automation. Examiner further notes that the additional limitations of machine learning and computer processing do not result in computer functionality or technical/technology improvement and hence do not result in a practical application. The machine learning algorithm and the computer limitation simply process the data through inputting and outputting data. Processing data is mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 Fed.Cir. 2017) or speeding up a loan application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, Lending Tree, LLLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2019)(non-precedential). Thus, the additional limitations of machine learning algorithm and computer limitations do not transform the abstract idea into a practical application.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant’s claimed invention. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Examples where the Courts have found selecting a particular data source or type of data to be manipulated to be insignificant extra-solution activity include selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Applicant’s limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits. Dependent claims 2-3, 5, 9-10, 12-13, 15, and 19-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims respectively, but these features only serve to further limit the abstract idea of independent claims. Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B,
Claims 1-3, 5, 9-13, 15, and 19-20 fail to amount to “significantly more” than an abstract idea. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as an “apparatus”, “machine-learning”, a “processor”, and “memory”. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Dependent claims 2-3, 5, 9-10, 12-13, 15, and 19-20 merely recite further additional embellishments of the abstract idea of independent claims 1 and 11 respectively, but these features only serve to further limit the abstract idea of independent claims 1 and 11; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits. The addition of another abstract concept to the limitations of the claims does not render the claim other than abstract. Under the Interim Guidance on Patent Subject Matter Eligibility (PEG 2019), it specifically states that narrowing an abstract idea of claims do not resolve the claims of being "significantly more" than the abstract idea. Thus, the additional elements in the dependent claims only serve to further limit the abstract idea utilizing the computer components as a tool and/or generally link the use of the abstract idea to a particular technological environment.
Therefore, since there are no limitations in the claims 1-3, 5, 9-13, 15, and 19-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims 1-3, 5, 9-13, 15, and 19-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kulkarni et al. (US 2023/0186217 A1).
As per independent Claim 1 and 11, Kulkarni discloses an apparatus/ method for carbon emission optimization using machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor (See at least Figs.8-9 and Claims 1, 12, and 20) to:
receive an integrated logistics data collection (See at least Fig.2, Para 0040, Data ingestion);
determine at least one projected carbon emission as a function of the integrated logistics data collection (See at least Para 0040), wherein determining the at least one projected carbon emission comprises:
identifying one or more emission factors associated with the integrated logistics data collection (See at least Para 0040);
generate at least one transportation plan as a function of the integrated logistics data collection and the at least one projected carbon emission (See at least Para 0041);
continuously receive a current logistics datum from an external source (See at least Para 0042);
update the at least one transportation plan as a function of at least one carbon emission offset, wherein updating the at least one transportation plan as a function of at least one carbon emission offset comprises: identifying a carbon emission outlier as a function of the current logistics datum and the one or more emission factors associated with the integrated logistics data collection, wherein the one or more emission factors associated with the integrated logistics data collection comprises an emission threshold; determining at least one carbon emission offset as a function of the carbon emission outlier and a machine-learning model; updating the transportation plan to incorporate the at least one carbon emission offset; and retraining the machine-learning model using outputs of each iteration of the machine-learning model comprising at least one carbon emission offset (See at least Para 0015-0017, Para 0042-0046); and
output the updated transportation plan to a requesting party (See at least Para 0035-0038 and Para 0046-0048).
As per Claims 2 and 12, Kulkarni discloses wherein the current logistics datum is received from one or more sensors (Para 0020, Spatio-temporal features).
As per Claims 3 and 13, Kulkarni discloses wherein the at least one carbon emission offset comprises one or more implementations regarding route optimization (See at least Para 0035-0038).
As per Claims 4 (3) and 14 (13), Kulkarni discloses wherein the machine-learning model comprises a neural network, wherein the neural network comprises: an input layer, wherein a user inputs the integrated logistics data collection at the input layer; one or more hidden layers, wherein the one or more hidden layers are configured to learn patterns and interactions between features of the integrated logistics data collection and the at least one carbon emission offset; and an output layer, wherein the neural network outputs an optimized emission offset (See at least Para 0040-0041).
As per Claims 5 and 15, Kulkarni discloses wherein identifying the carbon emission outlier further comprises: detecting a carbon emission deviation as a function of the one or more emission factors and a plurality of historical carbon emissions using a statistical model; comparing the detected carbon emission deviation against the emission threshold; and identifying the carbon emission outlier based on the comparison (See at least Para 0035-0038, Para 0042-0046).
As per Claims 6 and 16, Kulkarni discloses wherein the machine-learning model further comprises a carbon emission category classifier trained on carbon emission category training data, wherein carbon emission category training data comprises a plurality of exemplary carbon emission outliers correlated to a plurality of exemplary carbon emission categories (See at least Para 0035-0038, Para 0042-0046).
As per Claims 7 (6) and 17 (16), Kulkarni discloses wherein determining the at least one carbon emission offset further comprises: classifying the carbon emission outlier into a plurality of carbon emission categories using the carbon emission classifier; and selecting at least one carbon emission offset from a set of pre-defined emission offsets based on the plurality of carbon emission categories (See at least Para 0035-0038, Para 0042-0046).
As per Claims 8 (6) and 18 (16), Kulkarni discloses wherein the plurality of carbon emission categories comprise a category selected from a list consisting of direct emissions from fuel combustion, emissions from electricity consumption, emissions from transportation mode, emission from vehicle type, and emissions related to cargo type or weight (See at least Para 0035-0038, Para 0042-0046).
As per Claims 9 and 19, Kulkarni discloses wherein the at least a processor is further configured to input the updated transportation plan into one or more generative machine-learning models, wherein the one or more generative machine-learning models are configured to optimize at least one transportation plan for an objective (See at least Para 0035-0038, Para 0041-0046, Equivalent Automated Models disclosed for optimization of strategic, tactical and operational decision variables).
As per Claims 10 (9) and 20 (19), Kulkarni discloses wherein the one or more generative machine-learning models comprises a generative adversarial network (GAN) trained on exemplary historical transportation plans and their outcomes (See at least Para 0016, Para 0038-0040).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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June 22, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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