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 .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 18/233,252, filed on August 11, 2023. In response to Examiner’s Non-Final Rejection dated March 02, 2026, Applicant on May 18, 2026, amended claims 1, 11 and 20. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
Response to Arguments
Applicant's Arguments/Remarks filed May 18, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks regarding the pending rejections will be addressed herein below in the order in which they appear in the response filed May 18, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states though Applicant does not concede that the claims recite a judicial exception, any judicial exception recited in amended claim 1 is integrated into a practical application under Prong Two (see p. 12-13, Applicant Remarks).
The cited portion of amended claim 1 describes a specific, ordered computational architecture, that performs data processing to produce an intermediate result that gates downstream system behavior. Particularly, the first likelihood produced as the intermediate result is compared to a threshold likelihood. "Responsive to determining the first likelihood exceeds the threshold likelihood," the system applies a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders. This conditional invocation means the machine learning model is never accessed unless the gate condition is satisfied. See Specification, paragraph [0083]. The first-stage timeline computation thus serves as a computational gate that controls whether the second-stage machine learning inference pipeline is invoked at all.
This constitutes integration into a practical application because the claimed architecture addresses the technical problem of executing a computationally expensive machine learning inference pipeline on inputs for which the inference output would not be meaningful. See specification at paragraph [0083]. Without the claimed architecture, the system would apply the machine learning model indiscriminately, regardless of whether the first-stage condition is met. Amended claim 1 solves this by interposing a computational gate of computing a timeline and deriving a first probabilistic prediction from that timeline. The system only executes the second-
stage machine learning model if that prediction exceeds a threshold. The system then uses the machine learning model to produce a different prediction. Thus, amended claim 1 is integrated into a practical application under Step 2A, Prong Two.
In response, Examiner respectfully disagrees. Examiner respectfully finds Applicants remarks are directed toward the data analysis regarding predicting the likelihood that a picker servicing a batch of existing orders placed with the online concierge system will accept a batch of new orders for servicing. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity, applying a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders based on a threshold condition being met without meaningful limitations within the claims that amount to significantly more than the abstract idea itself, is considered a judicial exception (i.e. abstract idea). Examiner finds this limitation to be a conditional instruction of the abstract idea that does not recite or reflect an improvement to a technology, technological field or computer-related technology.
Examiner notes in Ex parte Desjardins, Appeal 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflected the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession and in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Examiner also notes Example 47 disclosed in the AI-related SME examples issued in 2024. Specifically, the Step 2A- Prong Two and Step 2B analysis of claim 2 of Example 47 states, in part, all uses of the recited judicial exceptions require data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). However, unlike claim 3 of Example 47 that provides for improved network security using the information from the detection to enhance security by taking proactive measures to remediate danger by detecting, dropping and blocking the source address associated with potentially malicious packets.
Examiner finds there are no similar technological improvements here. The machine learning model does not recite an improvement to the functioning of an artificial intelligence technology, computer-related technology or any technological field, thus failing to add an inventive concept to the claims. Applicant has not made any persuasive argument that would alter this analysis. Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states Under Step 2B of the two-step inquiry, an examiner must determine whether "the claim "purport(s) to improve the functioning of the computer itself or 'any other technology or technical field."' MPEP § 2106.05(a) (citing Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1976, 1984 (2014)).
The specific, unconventional arrangement of computational steps described in amended claim 1 constitutes an improvement to the functioning of the computer system itself, rather than a generic invocation of machine learning on a computer. The Examiner's own analysis confirms this. In particular, the Office Action finds that the subject matter of claim 1 distinguishable over the prior art. P. 9-10. While novelty is not dispositive of eligibility, the Federal Circuit has recognized that an unconventional arrangement of steps is relevant evidence that the claims are not directed to a well-understood, routine, or conventional activity. See BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016) (an "inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces").
In view of the above, claim 1 is patent eligible, as are claims 11 and 20, which recite
similar subject matter to claim 1. The dependent claims are also patent eligible by virtue of their dependency. Thus, this rejection should be withdrawn.
In response, Examiner respectfully disagrees. Examiner does side with Applicant that novelty is not dispositive of eligibility and further notes, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101; see MPEP 2106.05(i). Examiner notes BASCOM was found eligible based on considerations relevant to Part 2B; where claim 1 "carve[s] out a specific location for the filtering system (a remote ISP server) and require the filtering system to give users the ability to customize filtering for their individual network accounts". In contrast, the amended claims do not recite similar features. Examiner finds Applicant’s claim is not analogous to the network customization in BASCOM and the additional elements recited in the claims do not perform any unconventional functions, individually or in combination, that can be considered “significantly more” than the judicial exception. Applicant has not identified any disclosure in the claimed invention showing and/or submitting that the technology used is being improved, there was a technical problem with the technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than instructions used to predicting the likelihood that a picker servicing a batch of existing orders placed with the online concierge system will accept a batch of new orders for servicing. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-10 are directed towards a method, claims 11-19 are directed towards a computer program product and claim 20 is directed towards a computer system, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite predicting the likelihood that a picker servicing a batch of existing orders placed with the online concierge system will accept a batch of new orders for servicing.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receiving information describing a progress of a picker servicing a set of existing orders; computing a timeline for the set of existing orders based at least in part on the progress of the picker, information describing the set of existing orders, and historical data describing timespans during which the picker performed tasks involved in servicing previous orders; predicting a first likelihood that the picker will finish servicing the set of existing orders within a threshold amount of time, wherein predicting the first likelihood is based at least in part on the timeline, the progress of the picker and information describing the set of existing orders; determining whether the first likelihood exceeds a threshold likelihood; matching a plurality of sets of new orders with a plurality of pickers based at least in part on the second likelihood; determining whether one or more sets of new orders are matched with the picker; and responsive to determining the one or more sets of new orders are matched with the picker, sending one or more requests to service the one or more sets of new orders to a picker constitutes methods based on commercial interactions and managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a computer system comprising a processor and computer-readable medium, online concierge system, machine learning model, and client device does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claims 11 and 20 recite certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites sending one or more requests to service the one or more sets of new orders to a client device associated with the picker, which is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a computer system comprising a processor and computer-readable medium, online concierge system, and client device at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites responsive to determining the first likelihood exceeds the threshold likelihood, applying a machine learning model to a set of inputs to predict a second likelihood that the picker will accept the set of new orders for servicing while servicing the set of existing orders, wherein the set of inputs comprises a set of attributes of the picker and the progress of the picker, the machine learning model is trained by: receiving historical data describing acceptance, by pickers, of requests to service sets of new orders while servicing sets of existing orders, and training the machine learning model based at least in part on the historical data. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The computer program product comprising a non-transitory computer-readable storage medium having encoded instructions executable by a processor recited in claim 11 and the computer system comprising a processor and a non-transitory computer-readable storage medium storing instructions executable by the processor in claim 20 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claims 11 and 20 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a computer system comprising a processor and computer-readable medium, online concierge system, machine learning model, and client device amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0066]; [0100]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 3, 5-8, 10 and 13, 14-18 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. MPEP 2106.05(f). Claims 2, 5, 6, 12, 15 and 16 recites training an additional machine learning model and utilizing the machine learning model to predict likelihoods and set attributes, respectively. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning models disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Additionally, claims 4, 9, 10, 14 and 19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-10 and 12-19 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dutta (US 20190206008 A1) – The present disclosure relates assigning ride requests to providers based on the probability that a provider will accept the request. For example, one or more embodiments identify a first provider to assign the ride request. The system then generates a probability of acceptance for that provider. The system then determines an estimated time-to-arrival for the first provider and an alternate estimated time-to-arrival associated with a re-assigned provider. Based on the acceptance probability, the estimated time-to-arrival, and the alternate estimated time-to-arrival, the system then determines an expected time-to-arrival associated with the first provider. The system assigns ride requests to providers based on optimizing the expected time-to-arrival across multiple potential providers for a ride request.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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