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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/27/2026 has been entered.
Status of the Claims
Claims 1-7, 9-17, and 19-22 are currently pending.
Response to Arguments
Applicant’s arguments with respect to rejections made under 101 have been fully considered but are not persuasive. Applicant argues that the claims are not abstract because the claims do not recite collecting and analyzing data and the claims do not fall under “ methods of organizing human activities”, “mathematical relationship”, or “ mental process” (Remarks 8-11).
Examiner respectfully disagrees and notes that the claims recite receiving historical driver search information, analyzing such data, and determining a second offer publishing time for delivery offers to drivers which amount to collecting and analyzing data to make decision regarding when to publish delivery offers to drivers, this characterization is consistent with the claim as a whole. The claims under BRI fall under commercial interaction ( delivery logistics) and managing interaction between people ( offers to drivers), therefore optimizing delivery logistics decision ( i.e., coordinating when to publish offers to drivers) is a fundamental economic practice/ business operation which is abstract of certain methods of organizing human activities. Also see specification [0003] In particular, the owner has to determine an optimized publish time for packages to enable the products to be delivered safely and on time. However, a number of issues need to be managed relating to publishing pick-up times, loading parcels, packages, and/or other items into a delivery truck, a shipping container, and/or the like. Also see , [0054] showing how to determine the second offer publish time criterion that reduces the driver lag time., which is forms of mathematical analysis. The OA does not recite that the claim falls under mental process. Applicant did not explain why determining a second offer publish time criterion that reduces the driver lag time does not fall within the "certain methods of organizing human activity” grouping.
Applicant further argues that the claims are integrated into a practical application because it improves the operation of computer systems used for driver scheduling. Applicant argues “ claim 1 is further amended to define a specific machine-learning architecture and deployment pipeline-including a structured multi-level model tied to domain-specific parameters, scheduled machine learning preprocessing, dual-objective training, scheduler-controlled validation, and API-based integration”. (Remarks 12-13).
Examiner respectfully disagrees and notes the additional elements were analyzed in the OA below and are merely amounts to invoking generic components as a tools to perform the abstract idea or “apply it”. Additionally, reducing driver lag time is a business process improvement and does not provide an improvement to the operation of the computer itself.
Furthermore, the additional elements “applying, using a weekly scheduler, on the machine learning training data, and to obtain machine learning data ready for a model run, one or more machine learning preprocessing techniques that include one or more of data wrangling, outlier treatment or derived features creation; validating, using the weekly scheduler and based on the model run of the machine learning model, the output of the machine learning model; executing, based on using the weekly scheduler for the machine learning model, based on validating the output of the machine learning model, and based on using an optimization model, an application program interface (API) call” are recited at a high-level of generality (in Fig. 7), which indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements. At step one, the inquiry "asks whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or, instead, on a process that qualifies as an 'abstract idea' for which computers are invoked merely as a tool." Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, the claim only requires one offer, not “tracking up to hundreds of millions of orders". Accordingly, the rejection is maintained.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-7, 9-17, 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7, 9-10 are directed to a system (i.e., a machine), claims 11-17, 19-21 are directed to a method (i.e., a process), and claim 22 is directed to non-transitory computer readable medium ( i.e., machine) . Therefore, claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1 substantially recites receiving training data that is based on historical driver search information corresponding to a first offer publish time criterion, the first offer publish time criterion including a driver lag time; building a decision that includes a first level corresponding to priority, a second level corresponding to a first type of time periods, and a third level corresponding to a second type of time periods; training based on the training data to provide an output that results in reduction of reduce lag time and to maintain an on time arrival within a particular range; validating the output; offer information regarding a second offer publish time criterion.
Independent claim 11 substantially recites building a decision based on historical driver search information corresponding to a first offer publish time criterion associated with a driver lag time; determine a second offer publish time criterion that reduces the driver lag time and mitigates delivery outside of the delivery time window based on training and based on an order associated with a delivery time window.
Independent claim 22 substantially recites training based on historical driver search information corresponding to a first offer publish time criterion associated with a driver lag time;
Based on an order associated with delivery time, offer a second offer publish time criterion that reduces the driver lag time and mitigates delivery outside of the delivery time window based on training and based on an order associated with a delivery time window.
The limitations stated above are processes/ functions that under broadest reasonable interpretation (e.g., determine a second offer publish time that reduces driver lag time) covers “certain methods of organizing human activity” (managing personal behavior or relationships or interactions between people and commercial or legal interactions and following rules or instructions) because the claims recite collecting data ( e.g., historical driver search information corresponding to a first offer publish time criterion), analyzing data (e.g., determine a second offer publish time criterion that reduces the driver lag time) and outputting the result. The claims recite concept related to mathematical relationship (i.e., adjusting the first delivery offer publish time). Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 11 and 22 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent).
Claims 1, 11 and 22 recite the additional elements:
one or more processors;
one or more non-transitory computer-readable media storing computing instructions, executed on the one or more processors;
execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media;
building a machine learning model, an optimization model, transmitting, database system, computer system;
applying, using a weekly scheduler, on the machine learning training data, and to obtain machine learning data ready for a model run, one or more machine learning preprocessing techniques that include one or more of data wrangling, outlier treatment or derived features creation;
training, based on the machine learning training data that is ready for the model run, the machine learning model;
validating, using the weekly scheduler and based on the model run of the machine learning model, the output of the machine learning model;
executing, based on using the weekly scheduler for the machine learning model, based on validating the output of the machine learning model, and based on using an optimization model, an application program interface (API) call.
These are recited at a high-level of generality in the specification. (See specification: Fig.7, [0020-30] computer system 100 may comprise a portable computer, such as a laptop computer. In certain other embodiments, computer system 100 may comprise a mobile electronic device, such as a smartphone. [0032] system 300 can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system 300 described herein.[0042-43] The one or more databases can be stored on one or more memory storage units (e.g., non-transitory computer readable media), which can be similar or identical to the one or more memory storage units (e.g., non-transitory computer readable media) described above with respect to computer system 100 (FIG. 1).[0050], [0054] the estimated parameters can come from the output (e.g., output 602) of the decision tree of the machine learning model.[0055] the optimization model can minimize Z subject to the parameters from the output 602 (FIG. 6) to determine that the publish criterion should be 25 minutes instead of the static 45 minutes), such that, when viewed as whole/ordered combination ( as shown in Fig.3) , 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)).
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), 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: one or more processors;
one or more non-transitory computer-readable media storing computing instructions, executed on the one or more processors;
execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media;
building a machine learning model, an optimization model, transmitting, database system, computer system;
applying, using a weekly scheduler, on the machine learning training data, and to obtain machine learning data ready for a model run, one or more machine learning preprocessing techniques that include one or more of data wrangling, outlier treatment or derived features creation;
training, based on the machine learning training data that is ready for the model run, the machine learning model;
validating, using the weekly scheduler and based on the model run of the machine learning model, the output of the machine learning model;
executing, based on using the weekly scheduler for the machine learning model, based on validating the output of the machine learning model, and based on using an optimization model, an application program interface (API) call.
These 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 ( as shown in Fig.3), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea (e.g., using computers to communicate data). Thus, the claims are ineligible.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. Accordingly, the claims are not directed to significantly more than the exception itself. Therefore, the dependent claims are not eligible subject matter under § 101.
Novelty/Non-Obviousness
The closest prior art of record is:
Brinig ( 20190089775 A1) reciting system and method for scheduling transmitting messages to service providers based on network performance metrics. Brining does not recite driver lag time, delivery window, and ML to determine first and second metric.
Lunderman (US 20240062141 A1) reciting system and method for predicting lead time for delivery using expected response time and expected travel time. Lunderman does not teach the offer publish time to reduce lag time.
Dutta (US 20190206008) reciting system and method for assigning rides based on probability of driver acceptance. Dutta does not teach the adjustment to the offer publish time.
Philips (US 2019/0180229 A1) reciting system and method for estimating fulfillment time associated with preparing the product for delivery and determine a delay, based on the fulfillment time and the delivery time. Philips does not teach ML to determine the first and second metric nor the adjustment to the offer publish time.
Rajkhowa (US 2020/0342395) reciting system and method for delivery order dispatch including delivery timeslots if responses are not received, it escalates by offering higher surge pricing to additional drivers. Rajkhowa recites ML to generate ranking logic used for prioritizing driver offers. Rajkhowa does not teach ML to determine the first and second metric.
Hunter (US20220092516) reciting a method and system that uses historical driver data and ML to predict driver behavior and updating models. Hunter does not describe the driver lag time optimization.
Han (US 20190130260 A1) reciting a method and system for predicting ETA of the delivery order and timing the dispatching after the order is ready for pickup. Han does not teach the driver search time.
Reiss ( US 10133995 B1) reciting a method and system for using historical order and carrier data to predict demand and timing and optimizing driver position location to reduce delay. Reiss does not teach the adjustment to the offer publish time.
Daugherty ( US 20230124968 A1) reciting a method and system for assigning deliveries to drivers and scoring desirability for delivery drivers’ offers. Daugherty does not teach the adjustment to the offer publish time.
Marco (US 20180137594 A1) reciting a method and system for reserving drivers for a transportation service and navigating drivers to service transportation requests. Marco does not teach the adjustment to the offer publish time.
In conclusion, it would not have been obvious to one of ordinary skill in the art before the effective filing date to combine the above references to teach all the limitations of the independent claims.
Conclusion
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/MANAL A. ALSAMIRI/Examiner, Art Unit 3628
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