DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continuation
This application is a continuation of U.S. Application No. 18/528,738, filed December 4, 2023, now Patent No. 12/373,880, and Application No. 17/591,584, filed February 2, 2022, now US Patent No. 11/875,394. See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents). Claims 1-20 are pending and under examination.
Information Disclosure Statement
The Information Disclosure Statements filed on June 24, 2026 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and /n re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321 (c) or 1.321 (d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321 (b)..
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http:/Awww.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to
http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-l.jsp.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 12-13, and 20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, and 10 and 19 of U.S. Patent Application No. 11/875,394. Although the conflicting claims are not identical, they are not patentably distinct from each other because it is well settled that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before". in re Karlson, 136 USPQ 184 (CCPA 1963).
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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Specifically, claim 1 is directed to a method. Claim 13 is directed to a non-transitory computer-readable medium. Claim 20 is directed to a system. Each of the claims falls under one of the four statutory classes of invention. If the claims do fall within one of the statutory categories, they must then be determined whether the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
The claims recite the abstract idea without the bold limitations.
Claim 1 recites:
at an online system comprising one or more processors:
receiving a batched order at the online system, the order being associated with a first order and a second order;
determining a predicted benefit for delaying display of the order for fulfillment of the second order, wherein determining the predicted benefit comprises applying a machine learning model to data associated with the first order, wherein the machine learning model outputs the predicted benefit from delaying identification of the second order, wherein the machine learning model is trained by:
applying the machine learning model to training samples, each training sample including a time interval for delaying a historical order, one or more information describing the historical order, and a benefit label applied to each training sample identifying a benefit to the online system from delaying identification of the historical order;
backpropagating one or more error terms obtained from one or more loss functions associated with the machine learning model to update a set of parameters of the machine learning model, the backpropagating comprising updating one or more of the error terms based on a difference between the benefit label applied to a training sample and a predicted benefit generated by the machine learning model; and
stopping the backpropagation after the one or more loss functions satisfy one or more criteria;
selecting, based on the predicted benefit, a time interval for delaying display of second order;
evaluating the first order in the batched order based on the machine learning model; and
batching the first order with the second order, wherein the second order is withheld for display for the time interval based on the predicted benefit.
Claim2 further recites wherein determining the predicted benefit for delaying display of the order for fulfillment comprises:
applying the machine learning model to a time interval and a plurality of features of the first order;
generating a predicted benefit for the time interval from the machine learning model; and
comparing the predicted benefit to a threshold.
Claim 3 further recites wherein applying the machine learning model to training samples comprises:
receiving, for each training sample, a time interval, a set of features of a historical order, and a benefit label;
generating a predicted benefit using the machine learning model; and
computing a loss based on a difference between the predicted benefit and the benefit label.
Claim 4 further recites wherein backpropagating one or more error terms obtained from one or more loss functions comprises:
calculating gradients for one or more parameters in a neural network based on the error term;
adjusting the one or more parameters based on the gradients and a learning rate; and
repeating the adjustments for a number of epochs until a convergence criterion is met.
Claim 5 further recites wherein selecting, based on the predicted benefit, a time interval for delaying display of the second order comprises:
identifying a plurality of candidate time intervals;
applying the machine learning model to each candidate time interval; and ranking the candidate time intervals based on predicted benefit;
selecting a time interval that satisfies a constraint on predicted late fulfillment probability.
Claim 6 further recites wherein evaluating the first order in the batched order based on the machine learning model comprises:
retrieving characteristics of the first order including fulfillment location, warehouse, item count, and delivery deadline; applying a fulfillment model to the characteristics; and
generating a predicted fulfillment time for the first order.
Claim 7 further recites wherein batching the first order with the second order comprises:
identifying one or more common batching attributes between the first order and the second order;
verifying that a predicted fulfillment time for the batched order satisfies a late delivery constraint; and
grouping the first order and the second order into a batch for shopper assignment.
Claim 8 further recites wherein the machine learning model is trained using training samples derived by:
extracting data from previously fulfilled orders;
encoding orders with features including order value, item count, and geographic parameters; and
labeling each sample with a metric reflecting fulfillment efficiency gain from delayed batching.
Claim 9 further recites wherein receiving the batched order comprises:
receiving a first order including a warehouse location and item list;
receiving a second order including a destination location and time window; and storing the first order and the second order in an order management system as a batched pair.
Claim 10 further recites wherein the predicted benefit is computed using a batch benefit model comprising:
a neural network trained on labeled batching outcomes; a feature encoder that transforms raw order attributes into vector representations; and a regression head that outputs a scalar benefit prediction.
Claim 11 further recites wherein batching the first order with the second order further comprises:
confirming item compatibility constraints;
evaluating shopper availability for the batch; and
generating a shopper assignment record for the batched order.
Claim 12 further recites wherein batching the first order with the second order comprises:
identifying one or more additional orders received during the delay interval;
evaluating the additional orders for inclusion in a batch with the first order; and
generating a batch that comprises the first order, the second order, and at least one additional order.
Claim 13 recites a non-transitory computer-readable medium configured to store computer code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to:
receive a batched order at an online system, the order being associated with a first order and a second order;
determine a predicted benefit for delaying display of the order for fulfillment of the second order, wherein determining the predicted benefit comprises applying a machine learning model to data associated with the first order, wherein the machine learning model outputs the predicted benefit from delaying identification of the second order, wherein the machine learning model is trained by:
applying the machine learning model to training samples, each training sample including a time interval for delaying a historical order, one or more information describing the historical order, and a benefit label applied to each training sample identifying a benefit to the online system from delaying identification of the historical order;
backpropagating one or more error terms obtained from one or more loss functions associated with the machine learning model to update a set of parameters of the machine learning model, the backpropagating comprising updating one or more of the error terms based on a difference between the benefit label applied to a training sample and a predicted benefit generated by the machine learning model; and stopping the backpropagation after the one or more loss functions satisfy one or more criteria;
select, based on the predicted benefit, a time interval for delaying display of second order;
evaluate the first order in the batched order based on the machine learning model; and batch the first order with the second order, wherein the second order is withheld for display for the time interval based on the predicted benefit.
Claim 14 further recites wherein determining the predicted benefit for delaying display of the order for fulfillment comprises:
applying the machine learning model to a time interval and a plurality of features of the first order; generating a predicted benefit for the time interval from the machine learning model; and comparing the predicted benefit to a threshold.
Claim 15 further recites:
receiving, for each training sample, a time interval, a set of features of a historical order, and a benefit label;
generating a predicted benefit using the machine learning model; and
computing a loss based on a difference between the predicted benefit and the benefit label.
Claim 16 further recites:
calculating gradients for one or more parameters in a neural network based on the error term; adjusting the one or more parameters based on the gradients and a learning rate; and repeating the adjustments for a number of epochs until a convergence criterion is met.
Claim 17 further recites wherein selecting, based on the predicted benefit, a time interval for delaying display of the second order comprises:
identifying a plurality of candidate time intervals;
applying the machine learning model to each candidate time interval; and
ranking the candidate time intervals based on predicted benefit;
selecting a time interval that satisfies a constraint on predicted late fulfillment probability.
Claim 18 further recites:
retrieving characteristics of the first order including fulfillment location, warehouse, item count, and delivery deadline.
applying a fulfillment model to the characteristics; and
generating a predicted fulfillment time for the first order.
Claim 19 further recites wherein batching the first order with the second order comprises:
identifying one or more common batching attributes between the first order and the second order;
verifying that a predicted fulfillment time for the batched order satisfies a late delivery constraint; and
grouping the first order and the second order into a batch for shopper assignment.
Claim 20 recites:
a system comprising: one or more processors; and a non-transitory computer-readable medium configured to store computer code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:
receive a batched order at an online system, the order being associated with a first order and a second order;
determine a predicted benefit for delaying display of the order for fulfillment of the second order, wherein determining the predicted benefit comprises applying a machine learning model to data associated with the first order, wherein the machine learning model outputs the predicted benefit from delaying identification of the second order, wherein the machine learning model is trained by: applying the machine learning model to training samples, each training sample including a time interval for delaying a historical order, one or more information describing the historical order, and a benefit label applied to each training sample identifying a benefit to the online system from delaying identification of the historical order;
backpropagating one or more error terms obtained from one or more loss functions associated with the machine learning model to update a set of parameters of the machine learning model, the backpropagating comprising updating one or more of the error terms based on a difference between the benefit label applied to a training sample and a predicted benefit generated by the machine learning model; and
stopping the backpropagation after the one or more loss functions satisfy one or more criteria;
select, based on the predicted benefit, a time interval for delaying display of second order;
evaluate the first order in the batched order based on the machine learning model; and
batch the first order with the second order, wherein the second order is withheld for display for the time interval based on the predicted benefit.
The function of receiving a batched order, the order being associated with a first order and a second order are similar to concepts that have been identified as abstracts by the courts as found in buySafe which involved the creating of a contractual relationship. The functions or steps of receiving also involve a data gathering function which is an insignificant solution activity. Functions of “determining”, “applying”, “backpropagating”, “stopping”, “selecting”, involve mental processes and/or generic computer functions. The functions of "evaluating” and “batching" are also viewed as being involved mental processes and a mathematical function.
Step 2A, Prong Two: The judicial exception is not integrated into a practical application. In particular, the clams recite the above noted bolded limitations understood to be the additional limitations.
The claimed "online system”, “one or more processors”, “a machine learning model” are similarly understood in light of applicant's specification as mere usage of any arrangement of computer software or hardware intermediate components potentially using networks to communicate with instructions are properly understood to be mere instructions to apply the abstraction using a computer or device or computer system.
The claims recite the steps of receiving, determining, applying, backpropagating, stopping, selecting, evaluating and playing data.
Performing steps by a generic machine, computing device or one or more processors with memories merely limit the abstraction to a computer field by execution by generic computers. See MPEP 2106.05¢h). As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount to generally linking to a particular technological environment do not integrate a practical exception into a practical application.
Receiving and determining data are similar to Alappat, which as noted in MPEP 2106. 05(b)(1) is superseded, and the correct analysis is to look whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. The claims in the instant application are performed by one or more processors or computing devices which receive data and determine data.
Consideration of these steps as a combination does not change the analysis as they do not add anything compared to when the steps are considered separately. The claims recite a particular sequence of functions to "determining a predicted benefit for delaying display of the order for fulfillment of the second order, wherein determining the predicted benefit comprises applying a machine learning model to data associated with the first order, wherein the machine learning model outputs the predicted benefit from delaying identification of the second order". Performance of these steps or functions technologically may present a meaningful limit to the scope of the claim does not reasonably integrate the abstraction into a practical application.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Receiving data by electronic means or hardware amounts to receiving data
over a network has been recognized by the courts as routine, and conventional (See MPEP 2106.05(d)(II), citing Symantec, 835 F.3d at 1321, 120 OSPQ2d at 1362 (Utilizing an intermediary computer to forward information); TL Communications LEC v. AV Auto. LLC, 823 F.3d 607, Gl0, Ll8 USPO2d 1744, 1748 (ed. Cir. 2016) Casing a telephone for image transmission); OFF Techs., fac. v. Amazon.com, fie., 788 B.Ad 1359, 1363, LiS USPO2d 1090, 1093 (ed, Cir. 2015) (sending messages over a network), buySAFE, fie. v. Google, Inc.. 768F.3d 1350, 1355, 112 USPQ2d 1093, 1996 (Pod, Cyr. 2014) (computer receives and sends information over a network).
Positively reciting "online system”, “one or more processors”, and “a machine learning model” does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer or processor.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component. In processing the claims, it is noted that the recitation of these additional elements do not impact the analysis of the claims because these elements in combination are noted only to be a general purpose computer or processor for performing basic or routine computer functions. These claimed elements are noted to be a generic computer for receiving data and determining data, and performing routine and conventional functions. These additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer.
The judicial exception is not integrated into a practical application. In particular, the claimed "online system”, “one or more processors”, and “a machine learning model" are recited at a high level of generality such they amount to no more than mere instructions to apply the exception using generic components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, claims 1, 13 and 20 are is directed to an abstract idea.
The dependent claim(s) when analyzed and each taken as a whole remain being patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea.
Allowable Subject Matter
7. The claims would be allowable if overcome the 101 rejection.
The prior closest art to Applicant’s invention is XU et al (US Application No. 20180025408 A1) and Rajkhowa et al (US Publication No. 20180300800 A1).
- XU et al disclose providing information for on-demand service may further include: determining a second time duration based on the current location of the service provider and the second departure location; determining a second time length for completing the second order, determining a third time duration from the destination of the second order to the first departure location, determining a second time interval between the second pickup time and the current time; determining a second time difference based on the second time interval, the second time length, the second time
duration, and the third time duration, and selecting the filtered second order based on the second time difference and a second threshold time length.
- Rajkhowa et al disclose a method and system for receiving a request for a picked sales order, wherein the picked sales order is associated with a customer, the picked sales order, estimating an estimated fulfillment time interval to make ready one or more goods of the picked sales order for receipt by the customer, wherein estimating the estimated fulfillment time interval to make ready the one or more goods of the picked sales order for receipt by the customer comprises evaluating whether the picked sales order is able to be batched in a picked sales order batch using a k-means clustering to minimize a pick walk of the picked sales order batch, determining a receivable clock time at which to promise the one or more goods for receipt by the customer, wherein determining the receivable clock time at which to promise the one or more goods for receipt by the customer comprises identifying a current clock time, identifying a first available clock time of one or more predetermined clock times, wherein a difference of the first available clock time and the current clock time is greater than the estimated fulfillment time interval, and the first available clock time occurs after the current clock time and is nearest to the current clock time, and assigning the first available clock time as the receivable clock time; and communicating the receivable clock time to the customer.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. As per attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from
the examiner should be directed to ROMAIN JEANTY whose telephone number is (571)272-6732. The examiner can normally be reached M-F 9AM to 5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on 571 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624
Prosecution Insights