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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/13/2025 has been entered.
Status of Claims
Claims 1-3, 5-12, and 14-20 were rejected in the Final Office action mailed on 08/13/2025. Applicant’s amended claimset, entered on 11/13/2025, amended Claims 1 and 10, cancelled Claims 19-20, and added new Claim 21. Herein this Non-Final Office Action, Claims 1-3, 5-12, 14-18, and 21 are rejected.
Response to Arguments
Applicant’s arguments filed 11/13/2025, with respect to Rejections under 35 U.S.C. 101 for Claims 1-3, 5-12, 14-18, and 21, have been fully considered and are not persuasive.
On Pages 12-13, Applicant summarizes the multi-step multi-prong patent eligible subject matter analysis “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”. Examiner does not agree.
First, Examiner responds that Applicant has mischaracterized the patent subject matter eligibility analysis of MPEP 2106. Step 2A, as a whole, determines if the claim is “directed to” a judicial exception, e.g. abstract idea. Step 2A Prong One initially determines if the claims merely “recite” an abstract idea or other judicial exception. Then, Step 2A Prong Two determines if the claim recites “additional elements” that integrate the recited abstract idea into a practical application, in order to conclude the determination of whether the claims are “directed to” a judicial exception of Step 2A.
Examiner responds that the claims do not recite additional elements that integrate the abstract idea into a practical application or provide “significantly more” in Step 2B. MPEP 2106.05 provides a plurality of justifications for analyzing Step 2B, one of which is commonly referred to as “well-understood, routine, conventional activity.” However, Examiner’s rejection does not rely on MPEP 2106.05(d) (i.e. well-understood, routine, conventional activity). Therefore, Applicant has failed to address the rejection as discussed in greater detail below.
On Pages 13-15, Applicant refers to “Step 2A Prong One: Identifying Additional Elements.” Examiner notes, that as discussed above, Step 2A Prong One determines if the claims “recites” a judicial exception, such as an “abstract idea,” and Step 2A Prong Two determines if the recited additional elements integrate the abstract idea into a practical application.
Specifically, On Page 13 Applicant argues “Claim 1 recites limitations of "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, the machine-learned present cost model being a multilayer perceptron comprising a plurality of layers for operating on the set of features as input to the plurality of layers and for outputting the first estimate representing the estimated delivery cost without attempting batching," and "providing the set of features as input to a machine-learned counterfactual cost model to obtain a plurality of tuples for candidate batching options, the machine-learned counterfactual cost model being a multilayer perceptron comprising a plurality of layers for operating on the set of features as input to the plurality of layers and for outputting the 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." These limitations cannot be characterized as directed to any abstract idea.” Examiner does not agree.
As explained above, Step 2A Prong One does not determine if the claim is “directed to” an abstract idea, but instead asks if the claim merely “recites” an abstract idea. Even if Applicant’s arguments were agreed with by Examiner, the claims would remain ineligible because other parts of the claim would still recite an abstract idea. In order to show eligible at Step 2A Prong One, no part of the claim must “recite” an abstract idea or other judicial exception.
As discussed in greater detail below, the quoted claim limitations “recites” an abstract idea and also “recites” additional elements (i.e. not apart of the abstract idea), which fail to integrate the recited abstract idea into a practical application (Step 2A Prong Two) or provide “significantly more” (Step 2B).
Specifically, On Pages 13-14 Applicant argues “First, these limitations are not mathematical concepts. The MPEP provides that this grouping includes "mathematical relationships, mathematical formulas or equations, and mathematical calculations." MPEP § 2106.04(a)(2) I. MATHEMATICAL CONCEPTS. At best, the various limitations rely on mathematical principles, but do not themselves expressly recite the mathematical concepts. These steps relate to the application of two distinct machine learned models to transform an input set of features into the distinct outputs. Particularly, these limitations detail the architecture of the machine-learned models as multilayer perceptrons. The architecture of machine-learning models are, in essence, data structures that dictate the flow of data and computations. Although use of such models may rely on mathematical principles, the architecture recited is not expressly mathematical. As such, these steps cannot be characterized as reciting any mathematical concepts.” Examiner does not agree.
Examiner responds that the claim does “recite” an abstract idea of “mathematical concepts” as identified in the rejection section below regarding Step 2A Prong One. The architecture of the machine learned model is an additional element as a multilayer perceptron machine learned model is tied to use of a computer. However, the use of a mathematical model to take inputs and calculate an estimated batch size (i.e. numerical value), cost (i.e. numerical value), and probability (i.e. numerical value), is an abstract idea under MPEP 2106.04(a)(2)I. The additional element of the multilayer perceptron machine learned model functions, essentially, as a black box, and therefore is merely a tool used to apply the abstract idea of performing calculations under MPEP 2106.05(f) in Step 2A Prong Two and Step 2B.
Specifically, On Page 14 Applicant argues “Second, these limitations are not excluded methods of organizing human activity. The MPEP provides that the types of excluded methods include: "fundamental economic principles or practices ... ; commercial or legal interactions ... ; and managing personal behavior or relationships or interactions between people .... " MPEP § 2106.04(a)(2) II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY. The steps are not related to human activity. These steps describe applying machine-learned models comprised of weights and interconnected layers to output predictive metrics on order estimates between forgoing batching and batching. Though the concept of batching orders is the context, the actual steps of batching are not expressly recited in these limitations. As such, these limitations cannot be characterized as reciting any excluded method of organizing human activity.” Examiner does not agree.
Examiner responds that the calculation of predictive metrics using a weighted sum are apart of the “mathematical concepts” abstract idea grouping. However, the prediction of business operations, such as estimating a price based on batching or not batching orders, and estimating probabilities of an order being completed within a certain time window, are apart of the “certain methods of organizing human activity” abstract idea grouping. That said, the abstract idea groupings are not mutually exclusive per MPEP 2106.04(a). Thus, the rejection section below explicitly identifies the claim limitations that “recite” an abstract idea, and then discusses how those limitations are apart of at least one abstract idea grouping, citing to the appropriate MPEP section.
Specifically, On Page 14 Applicant argues “Third, these limitations are not mental processes. The MPEP defines "a mental process (thinking) that 'can be performed in the human mind, or by a human using a pen and paper' to be an abstract idea." MPEP § 2106.04(a)(2) III. MENTAL PROCESSES (citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011). As described above, these steps outline the architecture of machine-learned models. These models are innately based in computer technology. It is infeasible for a human mind to practically perform these limitations as the human mind is not compatible with processing the machine-learned models, in particular the multilayer perceptrons. As such, these limitations cannot be characterized as a mental processes abstract idea.” Examiner does not agree.
Examiner responds that Applicant’s abstract idea has not been identified as being apart of the “mental processes” grouping. Although the abstract idea groupings are not mutual exclusive, Applicant’s argument, again points to the identified additional element in support of Applicant’s argument that the claims do not recite an abstract idea. MPEP 2106.05 (a) and (f) addresses that claims that explicitly include computers, do not necessarily recite additional elements that integrate the recited abstract idea into a practical application in Step 2A Prong Two.
On Page 15, regarding “Step 2A, Prong Two: Integration into a Practical Application,” Applicant argues “The additional elements, embodied in the above-noted limitations, sufficiently integrate any of the alleged judicial exceptions into a practical application under Step 2A, Prong Two of the eligibility framework. The MPEP provides that "limitations the courts have found indicative that an additional element ( or combination of elements) may have integrated the exception into a practical application include: an improvement in the functioning of a computer, or an improvement to other technology or technical field .... " MPEP § 2106.04(d). Such is the case with the present claims. The additional elements embody an improvement to the field of machine-learning. The additional elements relate to a parallel structuring of two machine-learned models that operate in parallel. The first model outputs an unhatched estimate for fulfillment of an order and the second model outputs tuples for various candidate batch sizes and estimates for fulfillment of the order based on batching according to that batch size. This parallel structure is an improvement in machine-learning architecture that integrates the alleged abstract idea into a practical application. In sum, the recited technological solution thereby integrates the alleged judicial exception into a practical application-supporting a finding of eligibility under Step 2A, Prong Two.” Examiner does not agree.
MPEP 2106.05(a) states “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.”
MPEP 2106.05(a) states “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology.”
Specification ¶48 states “The machine-learning training module 230 trains machine-learning models used by the online concierge system 140. The online concierge system 140 may use machine-learning models to perform functionalities described herein. Example machine-learning models include regression models, support vector machines, naive 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.”
Fig. 4 and Specification ¶70 shows “In steps 415 and 420, some or all of the derived features are provided as input to the present cost model 305 and to the future cost model 310 to derive estimates of delivery cost if the order is assigned without delay and with delay, respectively.”
Examiner responds that the specification fails to provide the required technical explanation that shows an improvement in technology. Specification ¶48 shows that the purported invention does not improve the field of machine learning, but instead uses off-the-shelf, known, machine learning techniques to create the cost models. Put another way, if Examiner were to determine that the claims improve the field of machine learning, Applicant’s disclosure would fail to provide sufficient support under 35 U.S.C. 112(a).
As discussed in the rejection section below, the claims merely use the machine learned model in its ordinary capacity as a tool to determine predictive business operation metrics, i.e. merely applying the abstract idea under MPEP 2106.05(f). Although such claims may provide advantages, those advantages would be considered an improvement in the abstract idea itself, and do not yield patent eligible subject matter. See MPEP 2106.05(a) and (f).
Additionally, the asserted “parallel structure” is not included in the claims, and Fig. 4 and ¶70 indicates a sequential application of the “present cost model” in Step 415 and the “counterfactual cost model” in Step 420.
On Pages 15-16, regarding “Step 2B,” Applicant argues “Moreover, the additional elements are non-routine, unconventional, and not well understood activity in the technological field, thereby amounting to an inventive concept supporting eligibility under Step 2B. 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, routing, or well understood. Critically, the prior art does not teach nor suggest the additional elements, specifically the parallel architecting of the two machine-learned model, as further evidenced by the lack of rejections under 35 U.S.C. §§ 102 & 103. Accordingly, the additional elements further support finding of eligibility under Step 2B, in addition to Step 2A, Prong Two.” Examiner does not agree.
First, Examiner did not reject the claims as being well-understood, routine and conventional in Step 2B. Instead, Examiner relied, in part, on MPEP 2106.05(f). As discussed above, the application of the machine learning aspect of the claims is so widely prevalent that the Specification (¶48) need not go into explicit detail as to the structure of the claimed model, but instead references structures known in the art (e.g. multilayer perceptron). Additionally, MPEP 2106.04.I is clear that a novel abstract idea is still an abstract idea.
On Pages 16-17, Applicant argues that the claims recite patent eligible subject matter in the “Conclusion” section. Examiner does not agree. As discussed above and below, the rejection under 35 U.S.C. 101 remains.
Claim Objections
Claim 21 is objected to because of the following informalities:
Claim 21 recites a “multilayer perception” in the second and third paragraphs. Given the context within the claim, and Specification ¶48, it is clear that the claimed “perception” is a typographical error in referencing a “multilayer perceptron.”
Appropriate correction is required.
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, 5-12, 14-18, and 21 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 and 5-9 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, 5-12, 14-18, and 21 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;
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, 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; and
batching the other order for delivery along with the order.”
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, (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, (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, and (8) receiving a second order during the future time window and batching the orders, all of which are mathematical calculations (i.e. using a model to calculate outputs including cost and probability), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I, and 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 commercial or legal interactions (i.e. receiving orders, maintaining pools of orders, batching certain orders based on order features, and predicting business metrics based on batching 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. The mere the recitation of generic computer components (i.e., the “computer system,” “processor,” “computer-readable medium,” and “machine learned [models]” being “a multilayer perceptron comprising a plurality of layers”) implementing the identified abstract idea does not take the claim out of the mathematical concepts or certain methods of organizing human activity 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,” and “commercial or legal interactions” but for the recitation of generic computer components, then it falls in the mathematical concepts or certain methods of organizing human activity 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,” and
(iv) “machine learned [models]” being
(v) “a multilayer perceptron comprising a plurality of layers.”
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), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 2, ¶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,” and (iv) “machine learned [models]” being (v) “a multilayer perceptron comprising a plurality of layers,” when viewed as whole/ordered combination (Fig. 2, ¶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. 2, ¶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,” and (iv) “machine learned [models]” being (v) “a multilayer perceptron comprising a plurality of layers,” 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. 2, ¶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 and 5-9 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);
“. . . retraining the counterfactual cost model based on features obtained from data log about subsequent delivery of the batch” (Claim 5);
“. . . 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); and
“. . . 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).
Dependent Claims 2-3 and 5-9, 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 and 5-9 fail to establish claims that are not directed to an abstract idea because the further limitations (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) retraining the model based on certain data (Claim 5), (4) apportioning delivery cost between orders based on certain computations (Claims 6-7), (5) 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), and (6) limiting the extracted features to certain information (Claim 9), which are apart of the abstract idea. The elements of Claims 2-9 (i.e. (i) “computer system,” (ii) “processor,” (iii) “computer-readable medium,” and (iv) “machine learning,”) 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 and 5-9 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 and 5-9, 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 and 5-9 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 and 5-9 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-3 and 5-9 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claims 10-12 and 14-18 recite elements and limitations that are substantially similar to Claims 1-3 and 5-9. Claims 1-3 and 5-9 recite a method embodied by the elements and limitations of Claims 10-12 and 14-18. Therefore, Claims 10-12 and 14-18 are rejected under 35 U.S.C. 101 just as Claims 1-3 and 5-9 are rejected under 35 U.S.C. 101 as discussed above.
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;
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 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.”
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, (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, (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, and (5) receiving a second order during the future time window and batching the orders, all of which are mathematical calculations (i.e. using a model comprising parameters of weight and biases values to calculate outputs), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I, and 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 commercial or legal interactions (i.e. batching certain orders based on order features and predicting business metrics based on batching 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. The mere the recitation of generic computer components (i.e., the “non-transitory computer readable storage medium,” “machine-learning model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” and “processor”) implementing the identified abstract idea does not take the claim out of the mathematical concepts or certain methods of organizing human activity 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,” and “commercial or legal interactions” but for the recitation of generic computer components, then it falls in the mathematical concepts or certain methods of organizing human activity 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 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 model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” and
(iii) “processor.”
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.), and (ii) “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.), and (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”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 2, ¶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 model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” and (iii) “processor,” when viewed as whole/ordered combination (Fig. 2, ¶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. 2, ¶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 model architected as a multilayer perception and comprised of a . . . plurality of interconnected layers,” and (iii) “processor,” 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. 2, ¶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, 5-12, 14-18, and 21 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.