DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
3/2/2026. In Applicant’s amendment, claims 1, 3, 6-7, 9, 11, 13-14, and 17-20 were amended. Claims 2, 4, 5, 15, and 16 are cancelled. Claims 1, 3, 6-14, and 17-20 are currently pending and have been rejected as follows.
Response to Amendments
Rejections under 35 USC 101 are maintained.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 23-24 that the claims are not directed to an abstract idea at Step 2A, Prong One because claim 1 describes a computer-implemented method that enables evaluation of multiple software-based user-interface variants of an online concierge application simultaneously
and in parallel while preventing cross contamination of the evaluation results. More specifically, claim 1 recites steps for retrieving machine-readable logs of past assignments for multiple A/B tests running in the same region from data sources, computing and adjusting biased probabilities using those logs, and transmitting different user-interface variants to user devices located in geographic regions while ensuring the different user-interface variants are all being tested and evaluated independently and cleanly, and automatically transmitting an software version to all devices based on the results of the multiple, concurrent experiments. These steps depend on storing and processing large datasets across concurrent tests and cannot be performed in the human mind or by pen-and-paper. They also do not organize human behavior; instead they orchestrate how different user-interface variants of an online concierge application are delivered to devices so as to enable a clean comparison for each of the concurrently running experiments.
Examiner respectfully disagrees. Processing a large quantity of information does not prevent a limitation from reciting a mathematical concept. The claims recite mathematical concepts by determining a treatment-to-control ration, comparing the ration with a target ratio, calculating a biased probability, determining swarming and conflict adjustment factors, modifying the probability using those factors, and making an assignment based on the modified probability. The office action does not assert that the claims recite mental processes. The claims set forth commercial interactions characterized by the limitations reciting the testing of interfaces for the online concierge application and order fulfillment process.
Applicant argues on p. 24-28 that the claims are eligible at Step 2A, Prong Two because it amounts to an improvement to an existing technological process by reciting the explicit coordination of software delivery across concurrent experiments in the same region that reflects the specification's described solution of decoupling assignments to prevent cross-contamination … The technical benefit or improvement provided by this approach is that it produces clean, reliable data from concurrent A/B tests running in the same region, enabling the system to determine with confidence which user-interface variant improves the order-fulfilment process and which variant does not. Applicant describes the “interference between experiments” and inability to separate the effect of one experiment from the other as a technical problem of distributed software experimentation. Applicant further explains the alleged technical solution is the implementation of a random sequential algorithm that reduces conflict and swarming while maintaining balance.Examiner respectfully disagrees. The inability to separate the effect of one experiment from the other due to “interference between experiments” describes a problem in statistical analytics, not a technical problem of interference with distributed computing. Applicant’s specification describes the problem in [0015] as “negatively affecting experiment estimates;” [0016] “estimates for one experiment are confounded by the other.” The specification establishes that the problem is in the difficulty of interpreting the results of the experiment. Further, the alleged technical solution are the very steps directed to the mathematical concepts grouping, namely applicant’s described steps of retrieving logs of previous assignments for multiple experiments and regions, computing a biased probability that trends toward a predetermined ratio, and then dynamically adjusting that probability based on both inter-region matching (swarming) and inter-experiment matching (conflict). The resulting improvement is to the accuracy/reliability of the statistical results (Specification [0088] “By balancing the assignments based on the day of the week, more accurate assessment of the adjusted variants of the online concierge system 140 can be made”), which is not a technical improvement as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a). Applicant’s specification contains only one line concerning the assertion of retaining a suboptimal version causing unnecessary resource consumption, [0005] “This may lead to a sub-optimal version of the online concierge system to remain in production, which may lead to other problems like unnecessary consumption of high network bandwidth or computing resources which may be avoidable by rolling out the updated version.” There is no technical explanation or mechanism connecting the assignment algorithm to reduced network and computational resources, nor does the claim require such a reduction. The claimed automatic updating of the application version incorporating the variant amounts to applying the selected result of the abstract analysis. Further, applicant’s excerpt of MPEP 2106.05(a) “Generally, examiners are not expected to make a qualitative judgement on the merits of the asserted improvement” establishes that the examiner must determine whether the improvement is technological. As described above, applicant’s improvement is to reducing correlation and confounding in experimental estimates, which are the steps directed to the judicial exception.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 6-14, and 17-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system, and non-transitory computer readable storage medium). Claims 1, 3, 6-14, and 17-20 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 integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1, 3-4 and 6-13 are directed toward the statutory category of a process (reciting a “method”). Claims 14-15 and 17-19 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable storage medium”). Claim 20 is directed toward the statutory category of a machine (reciting a “system”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 14 and 20 are directed to an abstract idea by reciting executing multiple A/B tests concurrently, each A/B test spanning a predetermined set of test periods and …; during each of the predetermined set of test periods for each A/B test, … in each geographic region assigned to that A/B test either a treatment version of the user-interface variant being evaluated or a control version of the online concierge application; configuring a new A/B test to evaluate a new user-interface variant, the new A/B test being run concurrently with the multiple A/B tests and assigned to a plurality of geographic regions; for a given test period of the new A/B test after a first test period and for a given geographic region assigned to the new A/B test, wherein the given geographic region is also assigned to another A/B test running concurrently and the new A/B test is assigned to at least one other geographic region: retrieving, from …, (i) a log of previous assignments for the given geographic region and the new A/B test, (ii) a log of previous assignments for the new A/B test in the at least one other geographic region to which it is assigned, and (iii) a log of previous assignments for the other A/B test that is concurrently running in the given geographic region; determining a treatment-to-control ratio in the log of previous assignments for the given geographic region and the new A/B test, comparing the treatment-to-control ratio to a predetermined target ratio, and calculating a biased probability that trends the treatment-to-control ratio toward the predetermined target ratio; determining a swarming adjustment factor for the given test period by comparing the log of previous assignments for the given geographic region and the new A/B test with the log of previous assignments for the new A/B test in the at least one other geographic region, determining a conflict adjustment factor by comparing the log of previous assignments for the given geographic region and the new A/B test with the log of previous assignments for the other A/B test that is concurrently running in the given geographic region, and modifying the biased probability based on the swarming adjustment factor and the conflict adjustment factor; assigning the given geographic region for the given test period of the new A/B test to either the treatment version or the control version of the new user-interface variant under evaluation based on the modified biased probability; and …; comparing an outcome of the new A/B test associated with the treatment version to an outcome of the new A/B test associated with the control version; upon determining, from the comparison, that the new user-interface variant under evaluation improves the order fulfilment process, […] (Example Claim 1).
The claims are considered abstract because these steps recite mathematical concepts including mathematical calculations and certain methods of organizing human activity including managing personal behavior or relationships or interactions between people. The claims recite steps for setting test periods for an A/B test, setting a biased probability, assigning test periods, comparing outcomes of the two versions, updating the system based on the comparison, and transmitting the updated variant to the interface. Applicant’s disclosure describes difficulties related to isolating parameter impacts, system performance, and decision making from running A/B testing for an online concierge system that delivers products or services in the same geographic region. It is understood that the claimed steps aim to improve decision making regarding whether to update the online concierge system globally (Applicant’s Specification, [0006]-[0007]). By this evidence, the claims recite a type of mathematical concepts including mathematical calculations and certain methods of organizing human activity including managing personal behavior or relationships or interactions between people common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., assigning test periods of geographic regions to treatment or control groups for A/B testing).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as an online concierge application; a non-transitory computer-readable storage medium; a processor; memory; from a data store presenting a different user-interface variant of the online concierge application for evaluation to user devices located in a designated set of geographic regions; transmitting to user devices; a data store; transmitting, to all user devices located in the given geographic region and in the given test period, data that causes the user devices to present either the treatment version or the control version of the new user-interface variant based on the assignment for the given test period, wherein the data further causes said all user devices located in the given geographic region in the given test period to further present either a treatment version or a control version of the different user-interface variant under evaluation in the other A/B test based on the log of previous assignments for the other A/B test that is concurrently running in the given geographic region; automatically generating an updated version of the online concierge application that incorporates the new user-interface variant) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 1, 3, 6-14, and 17-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps for “setting” test periods, “retrieving” a log, “calculating” a biased probability, “determining” an adjustment factor, “assigning” the test period, “transmitting” a first version, “transmitting” a second version, “comparing” an outcome, “generating” an updated version of the online system, and “distributing” the updated version of the online system (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1, 3, 6-14, and 17-20 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2025/0209133 A1: A computer-implemented method comprising receiving a plural number of candidate parameter value sets in a specified order, each comprising a respective candidate parameter value for at least one parameter of an optimisation algorithm, wherein the number of candidate parameter value sets is based on a processing budget; for each candidate parameter value set in the sequence: applying the optimisation algorithm, with the at least one parameter set to the respective candidate parameter value, to a plurality of initial states of a model representing a system to generate corresponding candidate updated states, and evaluating each of the candidate updated states according to an optimality metric to generate a corresponding optimality score; selecting, as an estimated optimal state of the model, the candidate updated state having the highest optimality score; and outputting the selected estimated optimal state of the model to a user interface, network interface or other application.
WO 2023/239938 A1: Provided herein are systems and methods for selecting configuration files for applications. A computing system may maintain configuration files readable by an application. Each of the plurality of configuration files may identify a respective set of content items to prompt users to perform at least one of a plurality of activities via the application towards achieving a respective endpoint of a plurality of endpoints. The computing system may determine an endpoint of the plurality of endpoints to address a condition of a user. The user may be on a medication to address the condition, at least partially in concurrence with performance of the activity via the application. The computing system may select a configuration file identifying a set of content items for an activity of the plurality of activities to be performed by the user via the application towards achieving the endpoint.
Haupt et al., Affordable Uplift: Supervised Randomization in Controlled Experiments, 2019: Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. It is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA MUNSON can be reached on (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
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