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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/08/2026 has been entered.
Status of Claims
Claims 1-3, 6-12, 14-18, and 21-23 were rejected in the Final Office action mailed on 05/06/2026. Applicant’s amended claimset, entered on 07/08/2026, amended Claims 1, 10, and 21. Herein this Non-Final Office Action, Claims 1-3, 6-12, 14-18, and 21-23 are rejected.
Response to Arguments
Applicant’s arguments filed 07/08/2026, with respect to Rejections under 35 U.S.C. 101 for Claims 1-3, 6-12, 14-18, and 21, have been fully considered and are not persuasive.
On Pages 1-2, Applicant outlines the patent subject matter eligibility analysis and argues eligibility. Specifically, Applicant states “As outlined by the Office's Manual of Patent Examining Procedure ("MPEP"), the patent eligibility framework includes Step 1, Step 2A with Prongs 1 & 2, and Step 2B. Step 1 evaluates whether the claims are directed to one of the four enumerated statutory categories (process, machine, article of manufacture, or composition of matter). See MPEP § 2106.03. Step 2A, Prong 1 evaluates whether the claims are directed to a judicial exception to the statutory categories. See MPEP § 2106.04(a) & (b). Step 2A, Prong Two evaluates, if the claims are directed to a judicial exception, whether additional elements beyond the judicial exception(s) integrate the judicial exception(s) into a practical application. See MPEP § 2106.04(d). Finally, Step 2B evaluates whether the additional elements amount to an inventive step beyond mere recitation of the judicial exception(s). See MPEP § 2106.05. Without concession of any other eligibility pathway, Applicant argues that (1) under Step 2A, Prong Two, the additional elements integrate any alleged judicial exception into a practical application, and (2) under Step 2B, the additional elements are non-routine and unconventional activity that amount to an inventive concept.” (Emphasis added).
Examiner disagrees with Applicant’s characterization of the analysis and conclusion of eligibility.
Examiner responds that the specific verbs used in the language describing each of the steps and prongs of the patent subject matter eligibility analysis, although colloquially may be used interchangeably, carry significant weight. Applicant uses the phrase “directed to” in discussing Step 1 and Step 2A Prongs 1&2.
The subject matter eligibility analysis comprises: Step 1 (i.e. Does the claim fall within one of the four stator categories, e.g. process, machine, manufacture, or composition of matter?), Step 2A (Is the claim “directed to” a judicial exception, e.g. abstract idea, natural phenomena, or law of nature?), and Step 2B (i.e. Does the claim recite “additional elements” that amount to “significantly more” than the judicial exception?). MPEP 2106.III.
Step 2A is a two-prong analysis. MPEP 2106.04. Step 2A Prong-One first determines whether the claim merely “recites” (i.e. “sets forth” or “describes”) a judicial exception. MPEP 2106.04.II.A.1. Then, Step 2A Prong-Two determines if the claim “recites” “additional elements” that integrate the recited judicial exception into a practical application (e.g. if the recited additional elements do not “integrate the recited judicial exception into a practical application,” then, Step 2A would conclude that the claim is “directed to” the recited judicial exception.). MPEP 2106.04.II.A.2.
On Pages 2-4, regarding Step 2A Prong One, Applicant argues that the new limitations of amended Claim 1 “cannot be characterized as directed to any abstract idea.” Applicant argues that these limitations cannot be categorized into each of the enumerated groupings.
First, Applicant argues “The recited limitations are directed to concrete data operations rather than to any mathematical relationship, formula, equation, or calculation. Specifically, the limitations gather logged order data of prior orders, derive from that data a set of features characterizing each prior order, associate each training example with a label specifying an actual delivery cost, and adjust the weights of a plurality of layers of a multilayer perceptron based on a loss between an output of the model and the label. These are operations performed by a computer system on stored order data to configure the parameters of a machine-learned model; they do not recite a mathematical concept as such. As such, these steps cannot be characterized as reciting any mathematical concepts.”
Second Applicant argues “The recited limitations train and configure machine-learned models within a computer system and do not recite any fundamental economic principle or practice, any commercial or legal interaction, or the management of personal behavior or interactions between people. Deriving features from logged order data and adjusting the weights of a multilayer perceptron based on a loss between the model output and a delivery-cost label are operations of a computer system on stored data, not a scheme for organizing human activity. No human interaction, economic practice, or commercial or legal transaction is recited or organized by these limitations. As such, these limitations cannot be characterized as reciting any excluded method of organizing human activity.”
Third, Applicant argues “Training a multilayer perceptron comprising a plurality of layers-iteratively adjusting the weights of those layers based on a loss computed between the model's output and a label over a set of training examples derived from logged order data of prior orders-requires the computational structure of the model itself and cannot be carried out mentally. The limitations are therefore necessarily rooted in computer technology, rather than merely reciting a mental process implemented on a generic computer. As such, these limitations cannot be characterized as a mental processes abstract idea.”
Examiner disagrees.
Examiner responds that Applicant’s arguments demonstrate a fundamental misapplication of the patent subject matter eligibility analysis. The foundation of Applicant’s argument is to cite to the recited additional elements, and conclude that the claim does not “recite” an abstract idea in Step 2A Prong One. Examiner points to MPEP 2106.05(f), which provides several examples of claims limited to computer implementations that (1) recite an abstract idea, and (2) are held ineligible.
Examiner responds that the claims do recite additional elements such as storing data on a computer, training a machine learning model, and performing operations on a computer. However, these additional elements do not prevent the other claim limitations from reciting the abstract idea, as identified in the rejection section below.
In responding to Applicant’s arguments as if they were arguing patent subject matter eligibility under Step 2A Prong Two or Step 2B, Examiner maintains that the use of the additional elements do not integrate the recited abstract idea into a practical application or provide significantly more. As demonstrated in PEG Example 47 Claim 2, merely training a machine learning model on certain types of data does not necessarily provide subject matter eligibility.
On Pages 4-6, regarding Step 2A Prong Two, Applicant argues “The additional elements embody an improvement to computer-implemented delivery-logistics technology. The additional elements specify a particular architecture that trains two distinct machine-learned models-a present cost model and a counterfactual cost model, each a multilayer perceptron-on respective sets of training examples derived from logged order data of prior orders and on distinct labels. The present cost model is trained against a label specifying an unhatched delivery cost so as to output a first estimate of the delivery cost of releasing an order without attempting batching, while the counterfactual cost model is trained against labels specifying a batch size and a total batch delivery cost so as to output a plurality of tuples for candidate batching options. Training each model on its own set of features and labels, and then comparing their outputs, provides a technological mechanism for dynamically timing when to release an order for selection by pickers. By comparing the first estimate from the present cost model against the tuples from the counterfactual cost model, the system determines whether to release the order immediately or to delay its release for a period of time so that the order may be batched with later-arriving orders. Unlike a conventional system that releases every order for fulfillment as soon as it is received, the recited architecture uses the compared model outputs to decide, on a per-order basis, whether the estimated savings from waiting justify a delay. Where the counterfactual cost model indicates that releasing the order within a future time window would reduce the delivery cost by at least a given degree relative to the first estimate output by the present cost model, the system delays release of the order for a period of time; otherwise, the order is released without delay. This dynamic, per-order timing of release is a specific improvement in the operation of the delivery system, and not a generic application of a computer, because it is the two trained models and the comparison of their respective outputs that determine when each order is released. Moreover, leveraging two models in parallel bifurcates the inference task into constituent parts. Rather than relying on a single model to predict both the cost of immediate release and the range of possible batching outcomes, the recited architecture assigns each prediction to a dedicated model: the present cost model is trained, on features and a label specifying unhatched delivery cost, to predict the cost of releasing an order without batching, while the counterfactual cost model is trained, on features and labels specifying a batch size and a total batch delivery cost, to predict a plurality of tuples for candidate batching options. Because each model is trained on its own set of features and labels and maintains its own distinct set of parameters, each model is better trained for the particular inference it performs, and the outputs of the two models can be compared to time the release of each order. This division of the prediction task between two specialized, separately trained models is a specific improvement to machine-learning technology, and not a result obtainable by generic computer processing.” (Emphasis added).
Examiner does not agree.
Examiner responds that the claims do not recite a patent eligible improvement to technology or the functioning of a computer. MPEP 2106.05(a) distinguishes between a patent eligible improvement to the functioning of a computer (or technology) itself and a patent ineligible improvement to an abstract idea itself.
Examiner responds that successfully timing a market is a part of the abstract idea (i.e. commercial or legal interaction). Applicant has not provided a technological reason as to why conventional systems cannot delay the release of the orders. Therefore, Applicant’s assertion of better timing the delivery market based on better mathematical models does not provide a patent eligible improvement, but instead is an improvement in the abstract idea itself.
Examiner responds that the use of two models based on certain information to determine the optimal timing of a market is a part of the abstract idea (i.e. commercial or legal interaction). Examiner concedes that the training of a machine learning model is an additional element, i.e. a computer function. However, the limitation on the type of data used to train the model does not demonstrate an improvement to the functioning of a computer. Instead, the claims merely provide generic machine learning training to develop the models per MPEP 2106.05(f). Therefore, the “better” prediction that results from two models trained on specific types of data remains an improvement in the abstract idea itself, not an improvement in the functioning of the computer or an improvement in machine learning.
Examiner responds that the claims do not address a feature of parallel processing, and the specification fails to provide a technical explanation that demonstrates an improvement to the generic parallel processing that occurs when using any multi-core CPU.
On Pages 6-8, regarding Step 2B, Applicant argues “The MPEP provides that ‘an examiner should determine that an element ( or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry’ (italics for emphasis). MPEP § 2106.05(d). The additional elements are not so widely prevalent. Wide prevalence requires a high degree of understanding among artisans in the field, such that it need not be described in detail. See id, further referencing 35 U.S.C. §112(a). The additional elements are not so widely prevalent as to render them conventional, routine, or well understood. Critically, the additional elements, specifically the architecture of two separately trained multilayer-perceptron cost models-a present cost model trained against an unbatched-delivery cost label to output a first estimate, and a counterfactual cost model trained against batch-size and total-batch-delivery-cost labels to output a plurality of tuples for candidate batching options-used together to determine whether to delay release of an order to attempt batching, stand unrejected over any prior art. The Office has not rejected these claims under 35 U.S.C. §§ 102 or 103, and the absence of any prior-art rejection implies that the additional elements are neither disclosed nor suggested by the art of record and, accordingly, are not well-understood, routine, or conventional activity.” Examiner does not agree.
Examiner responds that “well-understood, routine, conventional activity” is not the stand alone test for Step 2B. MPEP 2106.05.I.A provides four types of limitations that have been held to not provide “significantly more,” including “i. Adding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, . . . (see MPEP 2106.05(f)).”
Examiner responds that because a “multi-layer perceptron” machine learning model is widely known in the art, a special definition is not necessary to satisfy the disclosure requirements of 35 U.S.C.112(a). However, because the specific architecture that defines “multi-layer perceptron” was not needed in the specification to show ownership of the claims, Applicant’s specification supports the determination that the machine learning aspects of the claim would be well-understood, routine, and conventional.
Further, Specification Paragraphs 48 provide a variety of types of machine learning models which could be used, demonstrating that the type of model is not essential to achieve the asserted advantage. Additionally, Specification Paragraph 50-52 discussing training the models, by iteratively adjusting parameters, which is essential to several of the suggested model examples discussed in Paragraph 48.
Although the recited abstract idea may be novel and non-obvious, the additional computer elements merely apply the abstract idea using generic computer components as a tool in their ordinary capacity under MPEP 2106.05(f).
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, 6-12, 14-18, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-3, 6-9, and 22-23 recite a method (i.e. a process)., Claims 10-12 and 14-18 recite a non-transitory computer-readable medium (i.e. a machine or manufacture), and Claim 21 recites a non-transitory computer-readable medium (i.e. a machine or manufacture). Therefore, Claims 1-3, 6-12, 14-18, and 21-23 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of:
“maintaining an unclaimed order pool of orders received from users and available to be claimed by pickers for fulfillment on behalf of the users, and a batching candidate pool of orders not yet available to be claimed by pickers;
receiving, from a user, an order for delivery of a set of items to an address of the user;
adding the order to the batching candidate pool;
deriving, from the order, a set of features characterizing the order;
providing the set of features as input to a . . . present cost model to obtain a first estimate representing an estimated delivery cost if the order were released from the batching candidate pool to the unclaimed order pool without attempting batching, the . . . present cost model being . . . for operating on the set of features as input to the [model] and for outputting the first estimate representing the estimated delivery cost without attempting batching;
the . . . present cost model being [based] on a first set of training examples derived from logged order data of prior orders, each training example in the first set including features characterizing one prior order and a label specifying unbatched delivery cost, [by] adjusting weights of the [model] based on a loss between an output of the present cost model and the label;
providing the set of features as input to a . . . counterfactual cost model to obtain a plurality of tuples, for candidate batching options, the . . . counterfactual cost model being . . . for operating on the set of features as input to the [model] and for outputting the plurality of tuples,
the . . . counterfactual cost model being [based] on a second set of training examples derived from logged order data of prior orders, each training example of the second set including features characterizing one prior order and one or more labels specifying a batch size and a total batch delivery cost, . . . adjusting weights of the . . . the counterfactual cost model based on a loss between an output of the counterfactual cost model and the label,
each tuple comprising:
a possible batch size,
an estimated delivery cost if the order were released to the unclaimed order pool during a given future time window allowing for a possibility of batching the order with orders of other users, and
a probability associated with the estimated delivery cost;
determining, based at least in part on the first estimate and on the tuples, whether to delay release of the order to the unclaimed order pool in order to attempt batching of the order;
responsive to determining that the release of the order to the unclaimed order pool should be delayed, delaying release of the order to the unclaimed order pool for a period of time;
during the future time window following the period of time, receiving another order for delivery of another set of items to another address of another user;
batching the other order for delivery along with the order and releasing the batch comprising the order and the other order to the unclaimed order pool;
receiving a request to claim the batch comprising the order and the other order;
transmitting information on the batch comprising the order and the other order to a . . . picker for fulfillment;
following fulfillment of the batch by the picker, computing a batched delivery cost based on a cost associated with fulfillment of the batch and a time to claim the batch from the unclaimed order pool; and
[updating] the. . . counterfactual cost model based on the batched delivery cost.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) maintaining a pool of orders available to pickers for fulfillment and a batching candidate pool of orders that are temporarily unavailable to pickers, (2) receiving an order that is added to the batching candidate pool, (3) deriving features of that order, (4) inputting the features into a model to obtain estimated delivery cost if the order were released without attempting batching, (4a) the model created based on certain information by adjusting weights of certain information, (5) inputting the features into a model to obtain tuples comprising certain data including batch size, estimated cost if the order were released, and probability associated with the estimated cost, (5a) the model created based on certain information by adjusting weights of certain information, (6) determining whether to delay the release of the order based on the estimate and the tuple, (7) delaying the release responsive to determining that the release should be delayed, (8) receiving a second order during the future time window and batching the orders, (9) releasing the batch, (10) receiving a request to claim the batch, (11) transmitting information on the batch to a picker, (12) after fulfilment, computing a cost based on certain information, and (13) updated the model based on the computed cost, all of which are:
mathematical calculations (i.e. using a model to calculate outputs including cost and probability, computing actual cost, updating the parameters of the model, and adjusting weights of the model based on certain information), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I,
managing personal behavior by following rules and interacting between people (i.e. determining when to release orders to pickers are at least “following rules or instructions” and communication of information is at least a “social activity”) and commercial or legal interactions (i.e. receiving orders, maintaining pools of orders, batching certain orders based on order features, predicting business metrics based on batching, releasing a batch, receiving a request, communicating necessary information for fulfilment, and providing cost feedback after fulfilment are at least “marketing or sales activities or behaviors” or “business relations”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II, and
observations and evaluations (i.e. developing a model based on logged order data and a labeled cost and adjusting weights of the model), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., the “computer system,” “processor,” “computer-readable medium,” and “machine learned [models],” “a multilayer perceptron comprising a plurality of layers,” “(re)training the machine-learned [models],” and “client device”) implementing the identified abstract idea does not take the claim out of the mathematical concepts, certain methods of organizing human activity, and mental processes groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “mathematical calculations,” “managing personal behavior or relationships or interactions between people,” “commercial or legal interactions,” “observations,” and “evaluations” but for the recitation of generic computer components, then it falls in the mathematical concepts, certain methods of organizing human activity, or mental processes groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 1 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “computer system”
(ii) “processor,”
(iii) “computer-readable medium,”
(iv) “machine learned [models]” including
(v) “a multilayer perceptron comprising a plurality of layers” and
(vi) “(re)training the machine-learned [models],” and
(vii) “client device.”
The additional elements of (i) “computer system” (Fig. 2 and ¶29 shows “online concierge system 140.”), (ii) “processor” (¶73 shows “a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium”), (iii) “computer-readable medium” (¶73 shows “a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media.”), and (iv) “machine learned [models]” being (v) “a multilayer perceptron comprising a plurality of layers” (Fig. 2 and ¶48 shows “The machine-learning training module 230 trains machine-learning models used by the online concierge system 140. . . Example machine-learning models include regression models, support vector machines, naïve bayes, decision trees, k nearest neighbors, random forest, boosting algorithms, k-means, and hierarchical clustering. The machine-learning models may also include neural networks, such as perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, or transformers. A machine-learning model may include components relating to these different general categories of model, which may be sequenced, layered, or otherwise combined in various configurations.” See also ¶75 further discussing machine learning model), (vi) “(re)training the machine-learned [models]” (¶51 shows “The machine-learning training module 230 may apply an iterative process to train a machine-learning model whereby the machine-learning training module 230 updates parameter values of the machine-learning model based on each of the set of training examples. . . . The machine-learning training module 230 updates the set of parameters for the machine-learning model based on the score generated by the loss function. For example, the machine-learning training module 230 may apply gradient descent to update the set of parameters.” See also ¶¶48-51 further showing that the claimed training is does not require a specific method of training, but could be performed by any known method for training a machine learning model.), and (vii) “client device” (Fig. 1 and ¶18 shows “The picker client device 110 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The (i) “computer system,” (ii) “processor,” (iii) “computer-readable medium,” (iv) “machine learned [models],” (v) “a multilayer perceptron comprising a plurality of layers,” (vi) “(re)training the machine-learned [models],” and (vii) “client device,” when viewed as whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. online computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) “computer system,” (ii) “processor,” (iii) “computer-readable medium,” (iv) “machine learned [models],” (v) “a multilayer perceptron comprising a plurality of layers,” (vi) “(re)training the machine-learned [models],” and (vii) “client device,” do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 2-3, 6-9, and 22-23 recite the abstract idea of:
“. . . wherein delaying release of the order comprises: generating a second estimated delivery cost based on the plurality of tuples; and determining that the second estimated delivery cost is at least a given threshold degree lower than the first estimate” (Claim 2);
“. . . during the future time window, receiving a second order for delivery of another set of items to another address of a second user; determining that the set of items and the other set of items are to be obtained at a same retailer location; determining that the address and the other address are within a threshold distance of each other; and responsive at least in part to determining that the set of items and the other set of items are to be obtained at the same retailer and determining that the address and the other address are within a threshold distance of each other, evaluating batching the other order for delivery along with the order” (Claim 3);
“. . . apportioning delivery costs between the order and the other order, the apportioning comprising computing a batching cost savings value as a sum of a cost of delivery of the order and a separate cost of delivery of the other order, less a cost of delivering the order and the other order together” (Claim 6);
“. . . wherein the apportioning further comprises computing a ratio of a cost savings for the order with an amount of additional delivery time for the order” (Claim 7);
“. . . determining whether to batch the other order for delivery along with the order, wherein the determining comprises: generating, using the . . . counterfactual cost model, an estimated cost of delivering the order and the other order as a batch; generating incremental delivery cost savings of the estimated cost of delivering the order and the other order as a batch relative to a cost total cost of delivering the order and the other order separately; generating incremental delivery time increases of delivering the order and the other order as a batch rather than separately; and identifying whether each of the following is at least a threshold value: a ratio of an incremental delivery cost savings for the order relative to an incremental delivery time increase for the order, and a ratio of an incremental delivery cost savings for the other order relative to an incremental delivery time increase for the other order” (Claim 8);
“. . . wherein the features comprise at least one of: a location of a retailer from which to obtain items of the order, a location corresponding to the address of the user, number of items in the order, number of types of items in the order, a distance travelled for the order, a time of the order, or an estimated time of arrival of the order” (Claim 9);
“. . . wherein the . . . present cost model and the . . . counterfactual cost model are [updated] independently on respective sets of features and labels” (Claim 22); and
“. . . wherein the . . . present cost model and the . . . counterfactual model comprise distinct sets of parameters” (Claim 23).
Dependent Claims 2-3, 6-9, and 22-23, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2-3, 6-9, and 22-23 fail to establish claims that are not directed to an abstract idea because the further limitations of (1) delaying release of an order by estimating a second cost and applying a threshold (Claim 2), (2) receiving a second order during the future time window, determining locations are within a threshold distance and items are from the same retailer, and evaluating batching of the orders (Claim 3), (3) apportioning delivery cost between orders based on certain computations (Claims 6-7), (4) determining whether to batch the orders by estimating costs and savings of batching orders and generating delivery time increases, and identifying satisfaction of a threshold for certain calculated ratios (Claim 8), (5) limiting the extracted features to certain information (Claim 9), and (6) updating the models independently (Claim 22), and (7) the models having distinct parameters (Claim 23), are a part of the abstract idea. The elements of Claims 2-3, 6-9, and 22-23 (i.e. (i) “computer system,” (ii) “processor,” (iii) “computer-readable medium,” (iv) “machine learning,” (v) “a multilayer perceptron comprising a plurality of layers,” (vi) “(re)training the machine-learned [models],” and (vii) “client device.”) fails to establish claims that are not directed to an abstract idea because the elements merely recite generic computer components similar to the generic computer components of Claim 1 and generally link the abstract idea to a particular technology or field of use (i.e. online computer environment) just as in Claim 1. The organization of the further limitations of Claims 2-3, 6-9, and 22-23 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2-3, 6-9, and 22-23, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2-3, 6-9, and 22-23 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2-3, 6-9, and 22-23 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-3, 6-9, and 22-23 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claims 10-12 and 15-18 recite elements and limitations that are substantially similar to Claims 1-3 and 6-9. Claims 1-3 and 6-9 recite a method embodied by the elements and limitations of Claims 10-12 and 15-18. Therefore, Claims 10-12 and 15-18 are rejected under 35 U.S.C. 101 just as Claims 1-3 and 6-9 are rejected under 35 U.S.C. 101 as discussed above.
Dependent Claim 14 recites the abstract idea of “. . . [updating] the counterfactual cost model based on features obtained from data log about subsequent delivery of the batch.”
Dependent Claim 14, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claim 14 fail to establish claims that are not directed to an abstract idea because the further limitations of updating the model based on certain information is a part of the abstract idea. The elements of Claim 14 (i.e. (ii) “processor,” (iii) “computer-readable medium,” (iv) “machine learning,” (v) “a multilayer perceptron comprising a plurality of layers,” (vi) “retraining the machine-learned [models],” and (vii) “client device.”) fails to establish claims that are not directed to an abstract idea because the elements merely recite generic computer components similar to the generic computer components of Claim 10 and generally link the abstract idea to a particular technology or field of use (i.e. online computer environment) just as in Claim 10. The organization of the further limitations of Claim 14 fails to integrate an abstract idea into a practical application just as discussed above for Claim 10. Additionally, performing the abstract idea of Claim 10 as recited in each of the further limitations of Claim 14, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 10. Therefore, Claim 14 amounts to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claim 14 fails to establish that the claims provide an inventive concept, just as in Claim 10. Therefore, Claim 14 fails the Subject Matter Eligibility Test and is consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 21 recites the abstract idea of:
“. . .
a first set of parameters for a first [model],
wherein the first set of parameters includes weights and biases in . . . the first . . . model to transform an input set of features characterizing a first set of items to output an unbatched estimate on obtainment of the set of items without batching;
wherein the first. . . model is [based] on a first set of training examples derived from logged order data of prior orders, each training example in the first set including features characterizing one prior order and a label specifying unbatched delivery cost, [by] adjusting weights of the first [model] based on a loss between an output of the first . . . model and the label;
a second set of parameters for a second [model],
wherein the second set of parameters weights and biases in . . . the second . . . model to transform the input set of features characterizing the first set of items to output a plurality of tuples for candidate batching options,
wherein the second. . . model is [based] on a second set of training examples derived from logged order data of prior orders, each training example of the second set including features characterizing one prior order and one or more labels specifying a batch size and a total batch delivery cost, [by] adjusting weights of the second [model] based on a loss between an output of the second . . . model and the label,
wherein the second [model] is configured to output each tuple comprising: a candidate batch size selected from a plurality of candidate batch sizes, a batching estimate for batching the set of items with other sets at the candidate batch size in a future time window, and a likelihood associated with the candidate batch size; and
. . .
input the input set of features into the first . . . model to compute the unbatched estimate, input the input set of features into the second . . . model to compute the plurality of tuples,
determine, based at least in part on the unbatched estimate and the plurality of tuples, whether to delay release of the set of items to an unclaimed pool to attempt batching with other sets, responsive to determining to delay release, delay release of the set to the unclaimed pool for a period of time, and
responsive to receiving a second set of items during the period of time, batch the second set with the first set and
release the batch comprising the first set and the second set to the unclaimed pool,
receive a request to claim the batch comprising the first set and the second set,
transmit information on the batch comprising the first set and the second set to . . . a picker for fulfillment,
following fulfillment of the batch by the picker, compute a batched delivery cost based on a cost associated with fulfillment of the batch and a time to claim the batch from the unclaimed pool, and
[update] the second . . . model based on the batched delivery cost.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) a first set of parameters for a first model including weights and biases that are used to output an unbatched estimate based on an input, (1a) the model created based on certain information by adjusting weights of certain information, (2) a second set of parameters for a second model including weights and biases that are used to output batching tuples comprising certain data including batch size, estimated cost if the order were released, and probability associated with the estimated cost, (2a) the model created based on certain information by adjusting weights of certain information, (3) determining whether to delay the release of the order based on the estimate and the tuple, (4) delaying the release responsive to determining that the release should be delayed, (5) receiving a second order during the future time window and batching the orders, (6) releasing the batch, (7) receiving a request to claim the batch, (8) transmitting information on the batch to a picker, (9) after fulfilment, computing a cost based on certain information, and (10) updated the model based on the computed cost, all of which are:
mathematical calculations (i.e. using a model comprising parameters of weight and biases values to calculate outputs, computing actual cost, updating the parameters of the model, and adjusting weights of the model based on certain information), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I,
managing personal behavior by following rules and interacting between people (i.e. determining when to release orders to pickers are at least “following rules or instructions” and communication of information is at least a “social activity”) and commercial or legal interactions (i.e. batching certain orders based on order features, predicting business metrics based on batching, releasing a batch, receiving a request, communicating necessary information for fulfilment, and providing cost feedback after fulfilment are at least “marketing or sales activities or behaviors” or “business relations”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II,
observations and evaluations (i.e. developing a model based on logged order data and a labeled cost and adjusting weights of the model), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., the “non-transitory computer readable storage medium,” “machine-learning [models]” and “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” “processor,” “(re)train[ing] the machine-learned [models],” and “client device”) implementing the identified abstract idea does not take the claim out of the mathematical concepts, certain methods of organizing human activity, and mental processes groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “mathematical calculations,” “managing personal behavior or relationships or interactions between people,” “commercial or legal interactions,” “observations,” and “evaluations,” but for the recitation of generic computer components, then it falls in mathematical concepts, certain methods of organizing human activity, and mental processes groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 21 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 21 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “non-transitory computer readable storage medium,”
(ii) “machine-learning [models]” and “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” and
(iii) “processor,”
(iv) “(re)train the machine-learning [models],” and
(v) “client device.”
The additional elements of (i) “non-transitory computer readable storage medium” (¶73 shows “a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media.” See also ¶¶74-75 discussing storing parameters and outputs.), (ii) “machine-learning [models]” and “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers” (Fig. 2 and ¶48 shows “The machine-learning training module 230 trains machine-learning models used by the online concierge system 140. . . Example machine-learning models include regression models, support vector machines, naïve bayes, decision trees, k nearest neighbors, random forest, boosting algorithms, k-means, and hierarchical clustering. The machine-learning models may also include neural networks, such as perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, or transformers. A machine-learning model may include components relating to these different general categories of model, which may be sequenced, layered, or otherwise combined in various configurations.” See also ¶75 further discussing machine learning model. Although the specification does not explicitly disclose that a “multilayer perceptron” includes a plurality of interconnected layers, a person of ordinary skill in the art would understand that a “multilayer perceptron” is defined as having a plurality of interconnected layers.), (iii) “processor” (¶73 shows “a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium”), (iv) “(re)train the machine-learned [model]” (¶51 shows “The machine-learning training module 230 may apply an iterative process to train a machine-learning model whereby the machine-learning training module 230 updates parameter values of the machine-learning model based on each of the set of training examples. . . . The machine-learning training module 230 updates the set of parameters for the machine-learning model based on the score generated by the loss function. For example, the machine-learning training module 230 may apply gradient descent to update the set of parameters.” See also ¶¶48-51 further showing that the claimed training is does not require a specific method of training, but could be performed by any known method for training a machine learning model.), and (v) “client device” (Fig. 1 and ¶18 shows “The picker client device 110 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The (i) “non-transitory computer readable storage medium,” (ii) “machine-learning [models]” and “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” (iii) “processor,” (iv) “(re)train the machine-learned [models],” and (v) “client device,” when viewed as whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. online computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) “non-transitory computer readable storage medium,” (ii) “machine-learning [models]” and “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” (iii) “processor,” (iii) “processor,” (iv) “(re)train the machine-learned [models],” and (v) “client device,” do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1-2, ¶11, ¶29, ¶48, ¶73, and ¶75 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Reasons for No Art Rejection
Claims 1-3, 6-12, 14-18, and 21-23 are not rejected over the prior art of record. Applicant has an actual and effective filing date of 02/16/2024. Prior art, as defined by 35 U.S.C. 102 and MPEP 2153, excludes art with a common assignee or inventor published within a 1-year grace period (i.e. after 02/16/2023), from being considered “prior art.”
The Closest prior art of record is:
US-20190130350-A1 (“Nguyen” Published on 05/02/2019, Later granted as US-11436554-B2, Assigned to Uber Technologies Inc.);
US-20190132702-A1 (“Ehsani” Published on 05/02/2019, Assigned to Uber Technologies Inc.);
US-20180308038-A1 (“Zhou” Published on 08/16/2022, Later granted as US-11416792-B2, Assigned to Uber Technologies Inc.);
US-20210081880-A1 (“Bivins” Published on 03/18/2021, Later granted as US-11216770-B2, Assigned to Uber Technologies Inc.);
US-20200342517-A1 (“Rajkhowa” Published on 10/10/2023, Later granted as US-11783403-B2, Assigned to Walmart Apollo, LLC.);
US-20150227888-A1 (“Levanon” Published on 07/12/2015, Assigned to Dragontail Systems, Ltd.);
US-20190180229-A1 (“Phillips” Published on 06/13/2019, Later granted as US-10783482-B2, Assigned to Capital One Services, LLC.)
US-8756119-B1 (“Andrews” Published on 06/17/2014, Assigned to T Mobile Innovations, LLC.);
US-20220045951-A1 (“Ahn” Published on 02/10/2022, Later granted as US-11818047-B2, Assigned to Coupang Corp.);
US-20220292580-A1 (“Putrevu” Published on 09/15/2022, Later granted as US-11803894-B2, Assigned to (common assignee) Maplebear, Inc.);
US-20200410864-A1 (“Ripert-864” Published on 12/31/2020, Later granted as US-11580860-B2, Assigned to (common assignee) Maplebear, Inc.);
US-20190114583-A1 (“Ripert-583” Published on 04/18/2019, Later granted as US-10818186-B2, Assigned to (common assignee) Maplebear, Inc.);
WO-2020131987-A1 (“Prestezog” Published on 06/25/2020, Assigned to Zume, Inc.);
CN-111582612-A (“Shi”) Published on 08/25/2020, Assigned to Lazas Network Technology Shanghai Co Ltd.); and
“Multilayer Perceptron in Machine Learning: A Comprehensive Guide” (Jaiswal, 02/14/2024, https://web.archive.org/web/20240214003028/https://www.datacamp.com/tutorial/multilayer-perceptrons-in-machine-learning).
The Following is an examiner’s statement of reasons for not applying prior art:
Nguyen Fig. 1 shows a method of matching orders with drivers (i.e. pickers) that utilizes batching of orders together. Fig. 1 and ¶44 shows a “requestor status store 132” which stores the status of the order. The “request handling component 128” times when an order should be released to “Matching Component 140,” which matches the orders to the drivers (i.e. making unclaimed orders available to pickers). ¶35. The “request handling component 128” works with the “batch decision logic 138” that applies certain rules to batch orders together based in part on supply and demand. ¶39. The rules could include the orders being from the same supplier, a timing component that ensures it would be practicable to combine the orders. ¶¶39-43.
Based on the “requester status store 132” and “historical information 159,” the “model development component 158” creates a plurality of “models 153.” ¶¶49-56. The models use “requester status store 132” as inputs and forecasts several parameters including future order demand over a certain time frame. ¶¶49-56. The forecasts includes “timing parameter 167” is used by “request handling component 128,” which can be “relaxed” to provide additional time for batching orders. ¶¶71-72. However, Nguyen does not discuss a specific algorithm for how the “timing parameter 167” is relaxed.
The “models 153” also output a “service value 171” (i.e. cost for order delivery) indicating the charge for delivery. ¶63. However, ¶63, ¶66, and ¶75 shows that the “service value 171” can be changed based on supply and demand, or distance traveled to complete delivery. Although batching would affect supply and demand, or distance traveled, Nguyen does not disclose counterfactual simulations or probabilities associated with the cost. Thus, although the models of Nguyen may be capable of such outputs, Nguyen does not provide the specific level of detail disclosed in Applicant’s claims.
Ehsani discloses a system and method similar to that of Nguyen.
Zhou shows grouping item deliver orders based on timing such that pending orders can be grouped with new orders. Fig. 1 and ¶¶36-37. The grouped orders “127” are delivered via “service provider 192” who receives a route provided by “provider routing and selection engine 120.” ¶37. The fees for delivery are associated with each “entity 116” (i.e. item provider). ¶35.
Bivins includes a “dynamic cost optimizer 156” that can delay the release of an order based on the expected cost and a threshold cost by forecasting supply and demand with “forecasting engine 160.” Fig. 1, ¶¶31-32, and ¶43. However, this delay relates to delaying the release of the order to the supplier (e.g. the restaurant). Additionally, Bivins does not mention batching multiple orders together.
Rajkhowa shows an operation to combine certain orders for a single deliver driver, or to have the deliver driver transport a single order. ¶29. Of the parameter’s considered, ¶¶29-31 shows that a time window constraint is used to ensure that combining orders ensures delivery within the scheduled timeslot (see ¶¶48-53 discussing scheduling the time slot). Additionally, the schedule considers the capacity of the deliver vehicle and limits the number of orders that may be batched ¶¶54-55.
Although batch pricing may be different for the delivery driver (¶33), Rajkhowa does not discuss the calculation of the price specifically. Thus, Rajkhowa utilizes certain criteria to determine if orders should be batched. However, Rajkhowa does not consider the counterfactual options of delaying the release of an order to be combined later.
Levanon ¶54 shows a “deliver delay parameter” that quantifies the extra time of optionally adding a second order to a first delivery order, batching the orders if the “deliver delay parameter” satisfies a threshold. Fig. 3A, ¶¶46-47, and ¶¶59-60 shows a method of determining optional sets of orders and then assigning the orders to a courier. The assignment process is optimized by utilizing a simulation of the delivery process. ¶¶78-82. Although Levanon considers the delay caused by combining orders, Levanon does not control this delay by optimizing cost and probability of selection.
Phillips Fig. 1A-1B and 4 shows a scheduling platform that allocates orders to couriers to be delivered from a product location to the user. Fig. 4 and ¶¶71-82 shows the determination of a delay of the order based on fulfillment time (i.e. item preparation time), estimated delivery time, and an additional delay that functions as a factor of safety. Although ¶70 considers the combination of items within an order, Phillips does not consider combination or batching of orders.
Andrews Fig. 1 shows applying “aggregation rules” to combine orders for more efficient (e.g. cost effective) shipping. Fig. 1 and C06L54-C09L30. Example rules include delaying release of an order so that it may be combined with future orders to reduce shipping cost (e.g. optimizing for full capacity shipments). C02L52-C03L05. Based on the rules, the shipments are released; however, the releasing of the orders in Andrews is the actual releasing to the courier, not merely releasing orders such that they become available to be claimed. C08L66-C09L30. Additionally, Andrews does not provide specifics as to the rules that cause delays (e.g. cost and probability analysis).
Ahn discloses a system and method for pooling deliver requests upon certain conditions. Fig. 3 and ¶22. Incoming request can be delayed if the pool is already at capacity. ¶¶3-4. However, this delay is more related to data transmission (Fig. 4 and ¶¶99-101), to address network congestion.
Putrevu uses a machine learning model to predict probability of fulfillment of an order (e.g. probability that a certain warehouse has the item). To increase efficiency, “flexible fulfillment” orders can be grouped based on a machine learned cost model. ¶¶33-34. The grouping applies a specified “time window” constraint for “flexible” orders which ensures delivery by a certain time. ¶33. See also Fig. 5 and ¶¶49-62. Although similar to the instant claims, much of the specifics are not taught by Petrevu including that the probability of Putrevu (1) is not dependent upon deliver cost or batching of orders and (2) is not associated with the estimated delivery cost.
Ripert-864 shows batching orders based on supplier location and service modeling, then based on the batches, select a delivery driver. Fig. 4 and ¶35. The models include estimating times and number of bags for orders. ¶36. The orders can be re-allocated to deliver drivers based on updated information. Fig. 5 and ¶¶38-39. Ripert-864 does not consider delaying release of an order to enable batching, but instead executes the batching procedure for each order.
Ripert-583 discloses a system and method similar to that of Ripert-864.
Prestezog shows grouping delivery orders to schedule a deliver plan. ¶¶30-31. However, the delays are primarily for delaying preparation of the item. ¶31 and ¶69. Fig. 6-7 and ¶¶72-79 shows combining orders based on scores or deliver cost. However, the orders are merely assigned to drivers (Fig. 5A-5C and ¶¶85-90) and probability of batching is not explicitly considered. batching orders.
Shi shows scheduling deliveries by determining a “target scheduling policy combination” and allocating the orders to delivery drivers. Fig. 1 and Page 8. The orders are allocated based on delivery costs from candidate deliver resources (i.e. delivery drivers) based on the lowest cost deliver resource. Fig. 4 and Page 13-14. batching orders. However, Shi does not teach the specific parameters considered in the instant claims.
Jaiswal shows that each neuron of the hidden layers in a multilayer perceptron is a mathematical equation that outputs a weighted sum of the inputs, with an additional “bias” factor.
Generally, the closest prior art teaches either (1) grouping orders for delivery (Nguyen, Ehsani, Zhou, Rajkhowa, Levanon, Andrews, Ripert-864, Ripert-583, Prestezog, and Shi), or (2) providing modeling of order deliveries (Nguyen, Ehsani, Bivins, Rajkhowa, Phillips, Ahn, Putrevu, and Shi), and (3) multilayer perceptron neural networks (Jaiswal), without the specific details claimed.
With respect to representative independent Claim 1, the closest prior art, taken individually and in an ordered combination, does not explicitly or implicitly disclose the specific ordered combination of elements that include “providing the set of features as input to a machine-learned present cost model to obtain a first estimate representing an estimated delivery cost if the order were released from the batching candidate pool to the unclaimed order pool without attempting batching . . . ; providing the set of features as input to a machine-learned counterfactual cost model to obtain a plurality of tuples, . . . each tuple comprising: a possible batch size, an estimated delivery cost if the order were released to the unclaimed order pool during a given future time window allowing for a possibility of batching the order with orders of other users, and a probability associated with the estimated delivery cost; determining, based at least in part on the first estimate and on the tuples, whether to delay release of the order to the unclaimed order pool in order to attempt batching of the order.”
Independent Claims 10 and 21 recite limitations substantially similar to the novel and non-obvious limitations of representative Claim 1. Thus, independent Claims 10 and 21 are not rejected based on the prior art.
Dependent Claims 2-3, 6-9, 11-12, 14-18, and 22-23 depend on Claims 1 and 10, and therefore are also not rejected under 35 U.S.C. 102 or 35 U.S.C. 103 via dependency.
Conclusion
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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