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
This action is in reply to the application 18/839,093 filed on 8/16/2024. Claims 1, 3, 12, 18, and 20 were amended and claims 4, 9, 11, and 19 were cancelled in the reply filed 7/22/2025. Claims 1, 18, and 20 were amended in the reply filed 2/23/2026. Claims 1-3, 5-8, 10, 12-18, and 20 are pending. This action is final.
Response to Arguments
Regarding Applicant’s argument starting on page 8 regarding claims 1-3, 5-8, 10, 12-18, and 20: Applicant’s arguments filed with respect to the rejections made under 35 USC § 101 have been fully considered, but are not persuasive.
Applicant first argues that claims vehicle assignment for a scheduled delivery in a particular time slot is a practical application. Examiner respectfully disagrees. Vehicle assignment for a scheduled delivery in a particular time slot is an abstract idea merely involving collecting, analyzing, and outputting data.
Applicant further argues that the claims are directed to a technological solution to a technological problem in the field of scheduled delivery logistics, integrating any alleged judicial exception into a practical application. Examiner respectfully disagrees. The alleged improvements that Applicant’s invention provides are business improvements to a business related process, and not improvements to a computer system technology itself (See MPEP § 2106.04(d)(1) and 2106.05(a) for examples and description of what is considered an improvement to a computer-functionality or an improvement to a technology). "Identifying, analyzing, and presenting certain data to a user is not an improvement specific to computing." International Business Machines Corp. v. Zillow Group, Inc., (Fed. Cir. No. 2021-2350, Oct. 17, 2022, pg. 8). The claimed computer components are generic and broadly recited, and the alleged improvements are not to the generic computer components themselves, but to the abstract process being performed by the computer components. Examiner respectfully argues that the claimed limitations not analogous to the MPEP descriptions and examples of improvements to computer-functionality or improvements to a technology, and that the claims are directed to an abstract idea.
Applicant further argues that the claims represent an ordered combination which meaningfully limits any alleged exception and reflects a particular way to achieve the desired outcome. Examiner respectfully disagrees. The claims merely implement a trained neural network and similar computer algorithms in order to process data using a generic computer. Merely using a trained neural network or similar computer algorithm provides nothing more than mere instructions to implement the abstract idea on a generic computer. See detailed explanation in the rejection below.
Applicant further argues that the claims fall squarely within the guidance of the August 4 2025 USPTO Memorandum regarding “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101.” However, Applicant does not explain how the claims fall within that guidance, nor does Applicant explain how the claims should be viewed in light of that guidance.
Applicant further argues that Examiner is required to show that the additional elements, in the ordered combination, are well-understood, routine, and conventional. Examiner respectfully disagrees. Examiner has considered all of the additional elements in their ordered combination and has concluded that they, in the aggregate, amount to a generic computer environment upon which the abstract idea is merely being “applied.” Examiner has reached this conclusion based on the broadest reasonable interpretation of each of the additional elements in light of the specification. Since Examiner has determined that all of the recited additional elements in their ordered combination amount to a generic computer environment upon which the abstract idea is merely being “applied,” Examiner need not also separately determine that the additional elements are well-understood, routine, and conventional. The evidence provided in the rejection below which shows that the claims are directed to an abstract idea merely “applied” to a generic computer environment is sufficient to determine ineligibility at both Step 2A Prong Two and Step 2B. See MPEP § 2106.05(f).
Applicant further argues that since the claims have been found novel, that this express finding strongly undercuts the Office Action’s Step 2B characterization that the same ordered combination is merely routine and conventional. Examiner respectfully disagrees. "The 'novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Diamond v. Diehr, 450 U.S. 175, 188-89 (1981). "[U]nder the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd. v. Merial L.L.C., 818 F.3d 1369, 1376 (Fed. Cir. 2016).
Applicant further argues that the amendment to claim 1 clarifies that the predicted batching rate and selected time slot are not merely calculated for display, they are used to drive a concrete operational action in the delivery network. Applicant continues, “This further demonstrates that the ordered combination is a specific technological implementation in a delivery logistics system, not an abstract idea performed on a generic computer.” Examiner respectfully disagrees. Making determinations (such as vehicle assignments) based on data analysis is part of the abstract idea. The determination of vehicle assignments is merely a data output and not a technological implementation as Applicant describes. See MPEP § 2106.05(a) which describes improvements to a computer, technology, or technical field and the associated examples of what does/doesn’t constitute such an improvement.
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, 5-8, 10, 12-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 18, and 20 recite a method, a server, and a non-transitory computer-readable medium for performing: A method for forecasting a delivery fare and determining a batching possibility prediction-based dynamic discount for a scheduled order of a goods delivery service, the method comprising: predicting, by a computer, a delivery fare for a scheduled delivery in an available time slot using a trained artificial neural network (ANN), wherein the trained ANN is a trained quantile regression neural network that comprises a multiple hidden layer feedforward neural network that is trained with historical data and a quantile loss; predicting, by a computer, a batching rate for the scheduled delivery to be batched with at least one other order in the available time slot using a neural network with long short-term memory (LSTM) layers that is trained on a set of historical delivery data; determining, by a computer, a discount for the scheduled delivery in the available time slot, based on the predicted batching rate using a S-shaped sigmoid function having the predicted batching rate as an input and the discount as an output; determining a final delivery fare for the scheduled delivery in the available time slot, based on the predicted delivery fare and the determined discount; causing the determined final delivery fare to be displayed for user selection of the scheduled delivery on a client device; receiving a user selection of the scheduled delivery fare; and identifying a delivery vehicle and assigning the scheduled delivery to the delivery vehicle based on the predicted batching rate and the available time slot selected by the user. Therefore, claims 1, 18, and 20 are each directed to one of the four statutory categories of invention: a method, a system, and an article of manufacture, respectively.
Step 2A – Prong One: The claim limitations [a] method for forecasting a delivery fare and determining a batching possibility prediction-based dynamic discount for a scheduled order of a goods delivery service, the method comprising: predicting ... a delivery fare for a scheduled delivery in an available time slot ... predicting ... a batching rate for the scheduled delivery to be batched with at least one other order in the available time slot ... determining ... a discount for the scheduled delivery in the available time slot, based on the predicted batching rate using a S-shaped sigmoid function having the predicted batching rate as an input and the discount as an output; determining a final delivery fare for the scheduled delivery in the available time slot, based on the predicted delivery fare and the determined discount; causing the determined final delivery fare to be displayed for user selection of the scheduled delivery ... receiving a user selection of the scheduled delivery fare; and identifying a delivery vehicle and assigning the scheduled delivery to the delivery vehicle based on the predicted batching rate and the available time slot selected by the user, as drafted, is a method that, under its broadest reasonable interpretation, only covers concepts which may be categorized as “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations; fundamental economic practices (e.g., shipping/logistics)) and “Mathematical Concepts” (e.g., mathematical relationships). That is, nothing in the claim discloses anything outside the grouping of “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations; fundamental economic practices (e.g., shipping/logistics)) and “Mathematical Concepts” (e.g., mathematical relationships). Accordingly, the claim recites an abstract idea.-
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. The claims as a whole merely describes how to generally “apply” the concept of the aforementioned abstract idea using a computer (claim 1), a server (claim 18), one or more processors (claim 20), a non-transitory computer-readable medium (claim 20), and a client device (claims 1, 18, and 20). The claimed components are recited at a high level of generality and are merely invoked as generic computer components to perform the aforementioned abstract idea. Simply implementing the abstract idea on a generic computerized system is not a practical application of the abstract idea. Furthermore, claims 1, 18, and 20 recite the limitations a trained artificial neural network (ANN) (claims 1, 18, and 20), a trained quantile regression neural network (claims 1, 18, and 20), a multiple hidden layer feedforward neural network that is trained with historical data and a quantile loss (claims 1, 18, and 20), and a neural network with long short-term memory (LSTM) layers that is trained on a set of historical delivery data (claims 1, 18, and 20). These limitations do not integrate the abstract idea into a practical application. In support of this position, Examiner cites the USPTO’s July 2024 Subject Matter Eligibility Examples. Specifically, Examiner argues that the aforementioned limitations of claims 1, 18, and 20 are analogous to the artificial neural network (ANN) described in Example 47 Claim 2. The USPTO’s analysis examines the mere use of the ANN in the following way: “The limitations in (d) and (e) reciting ‘using the trained ANN’ provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).” Similarly, the instant claims recite a computer, server, or one or more processors used to perform the functions of a trained artificial neural network (ANN) (claims 1, 18, and 20), a trained quantile regression neural network (claims 1, 18, and 20), a multiple hidden layer feedforward neural network that is trained with historical data and a quantile loss (claims 1, 18, and 20), and a neural network with long short-term memory (LSTM) layers that is trained on a set of historical delivery data (claims 1, 18, and 20). These limitations describe no more than the mere use of algorithms, and they are therefore analogous to Example 47 Claim 2 limitations (d) and (e). In other words, merely using a trained neural network or similar computer algorithm provides nothing more than mere instructions to implement the abstract idea on a generic computer (i.e., a computer, server, or one or more processors). See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The trained neural network or similar computer algorithm is used to generally apply the abstract idea without placing any limits on how the trained neural network or similar computer algorithm functions. Rather, these limitations only recite the outcome of “predicting ... a delivery fare for a scheduled delivery in an available time slot,” or “predicting ... a batching rate for the scheduled delivery to be batched with at least one other order in the available time slot,” for example, and do not include any details about how the “predicting” is accomplished. See MPEP 2106.05(f). Accordingly, the aforementioned additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims as a whole merely describe how to generally “apply” the aforementioned abstract idea using generic computer components. The additional elements a server (described in spec. para. [0043]), one or more processors (described in spec. para. [0093]), and a computer-readable medium (described in spec. para. [0025]) are recited at a high level of generality and are merely invoked as a tool to perform the aforementioned abstract idea. As explained with respect to Step 2A, Prong Two, the additional elements of “using” a trained artificial neural network (ANN) (described in spec. para. [0009]), a trained quantile regression neural network (described in spec. para. [0009]), a multiple hidden layer feedforward neural network that is trained with historical data and a quantile loss (described in spec. para. [0009]), and a neural network with long short-term memory (LSTM) layers that is trained on a set of historical delivery data (described in spec. para. [0059]) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP § 2106.05(f). The additional elements are described at a high-level indicating the known nature of each of these additional elements in the art. Simply implementing the abstract idea on a generic computerized system does not amount to significantly more than the judicial exception. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore the claims are not patent eligible.
Claims 2-3, 5-8, 10, and 12-17 have been given the full two part analysis including analyzing the limitations both individually and in combination. Claims 2-3, 5-8, 10, and 12-17 when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the recited limitations of the dependent claims merely further narrow the abstract idea.
Step 2A – Prong Two: The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the claims as a whole merely describe how to generally “apply” the aforementioned abstract idea. Specifically, the claims as a whole merely describes how to generally “apply” the concept of the aforementioned abstract idea using a display (claim 17). The claimed components are recited at a high level of generality and are merely invoked as generic computer components to perform the aforementioned abstract idea. Simply implementing the abstract idea on a generic computerized system is not a practical application of the abstract idea. Furthermore, claim 3 recites the limitations quantile regression network, linear regression network, lasso regression network, support vector regression network, a multilayer perceptron neural network, a long short-term memory neural network, and a decision tree-based algorithm. These limitations do not integrate the abstract idea into a practical application. In support of this position, Examiner cites the USPTO’s July 2024 Subject Matter Eligibility Examples. Specifically, Examiner argues that the aforementioned additional elements of claims 3 are analogous to the artificial neural network (ANN) described in Example 47 Claim 2. The USPTO’s analysis examines the mere use of the ANN in the following way: “The limitations in (d) and (e) reciting ‘using the trained ANN’ provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).” Similarly, the instant claims recite a computer used to perform the functions of quantile regression network, linear regression network, lasso regression network, support vector regression network, a multilayer perceptron neural network, a long short-term memory neural network, and a decision tree-based algorithm. These limitations describe no more than the mere use of algorithms, and they are therefore analogous to Example 47 Claim 2 limitations (d) and (e). In other words, merely using a trained neural network or similar computer algorithm provides nothing more than mere instructions to implement the abstract idea on a generic computer (i.e., a computer). See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The trained neural network or similar computer algorithm is used to generally apply the abstract idea without placing any limits on how the trained neural network or similar computer algorithm functions. Rather, these limitations only recite the outcome of “predicting ... a delivery fare for a scheduled delivery in an available time slot,” and do not include any details about how the “predicting” is accomplished. See MPEP 2106.05(f). Accordingly, the aforementioned additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of a display (described in spec. para. [0081]) are described in the specification at a high-level indicating the known nature of each of these additional elements in the art. As explained with respect to Step 2A, Prong Two, the additional elements of “using” quantile regression network (described in spec. para. [0056]), linear regression network (described in spec. para. [0056]), lasso regression network (described in spec. para. [0056]), support vector regression network (described in spec. para. [0056]), a multilayer perceptron neural network (described in spec. para. [0056]), a long short-term memory neural network (described in spec. para. [0059]), and a decision tree-based algorithm (described in spec. para. [0056]) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP § 2106.05(f). The additional elements are described at a high-level indicating the known nature of each of these additional elements in the art. Simply implementing the abstract idea on a generic computerized system does not amount to significantly more than the judicial exception. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore the claims are not patent eligible.
Reasons for Novelty
Claims 1-3, 5-8, 10, 12-18, and 20 are novel over the prior art. Specifically, claims 1-3, 5-8, 10, 12-18, and 20 are not anticipated by the prior art and they are also non-obvious over the prior art. Although independent claims 1 and 18-20 were previously rejected as being obvious over Kumar in view of Sahay, the amendments to these independent claims render them non-obvious. Other relevant art of note includes: Mishra (U.S. Pub. No. 2022/0253801) – Piece versus multi-piece carrier optimization; Lye (U.S. Pub. No. 2021/0082074) – Allocation of dynamically batched service providers and service requesters; and Loppatto (U.S. Pub. No. 2015/0363843) – Dynamic provisioning of pick-up, delivery, transportation and/or sortation options. In particular, the inventive combination of, “a trained artificial neural network (ANN), wherein the trained ANN is a trained quantile regression neural network that comprises a multiple hidden layer feedforward neural network that is trained with historical data and a quantile loss,” “a neural network with long short-term memory (LSTM) layers that is trained on a set of historical delivery data,” and “using a S-shaped sigmoid function having the predicted batching rate as an input and the discount as an output,” in the field of batching and scheduling deliveries would not have been obvious to one of ordinary skill in the art before the time of filing. Although particular amended claim elements are being highlighted by Examiner, it is the claim as a whole which Examiner has determined is non-obvious over the prior art. Therefore, claims 1-3, 5-8, 10, 12-18, and 20 are novel over the prior art and are not subject to rejection under 35 USC § 102 or 35 USC § 103.
Conclusion
All claims are either identical to or patentably indistinct from claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 extension fee 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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/CHRISTOPHER GOMEZ/ Examiner, Art Unit 3628