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 .
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. Applicants argue that the claims are eligible under Step 2A prong one because they recite steps specific to machine learning acritarches and computational operations. (Remarks 9-10).
This argument is not persuasive. Under BRI, claim 1 recites determining and managing the timing at which delivery offers are made available to drivers. This constitutes management of a commercial interaction and a certain method of organizing human activity. The use of machine-learning techniques does not remove the underlying commercial/logistical activity from the abstract-idea grouping. The machine-learning system is used to determine when commercial delivery offers are published to drivers. Further, determining an optimized offer-publish time based on the identified variables involves mathematical analysis. Accordingly, claim 1 recites an abstract idea under Step 2A, Prong One.
Also, see specification ([0003] 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. [0054]), showing how to determine the second offer publish time criterion that reduces the driver lag time., which is forms of mathematical analysis. Additionally, the claims are not analogous to Example 39, they do not recite similar specific training techniques that improve facial detection technology, by making the neural network more robust to shifts, distortions, scale and rotation. The OA does not recite that the claims fall under mental process.
Applicant further argues that the claims are eligible under Step 2A Prong 2 because the claimed scheduler, preprocessing, model training, validation, optimization, and API execution improve computer functionality by reducing processor cycles, memory usage, and computational complexity. (Remarks 11-15).
Examiner respectfully disagrees and notes that claims do not recite a particular mechanism that improves processor operation, memory operation, or machine-learning technology. The “Weekly scheduler” controls when processing occurs, and “the data-wrangling, outlier-treatment, and derived-feature” limitations prepare the delivery data for use by the model, these do not provide a specific improvement to processor, memory, or scheduler functionality. Additionally, the Multi-level decision tree architecture organizes information according to priority and different time periods. Training the model to reduce driver lag and maintaining on-time arrival improves the underlying delivery process rather than the functioning of the computer or machine-learning technology. Similarly, the validation and optimization steps improve the reliability and timing of the delivery decision and the API call implements the result. Processing millions of orders demonstrates scale, but scale alone does not provide a technological improvement. The steps of preprocess delivery data, train and validate a model, determine an optimized offer time, and implement that result improves the underlying delivery-scheduling process rather than computer or machine-learning technology. Accordingly, the claims do not integrate the judicial exception into a practical application. 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, claims1-7, 9-17, 19-22 all fall within 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, 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.
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 concepts related to mathematical relationships (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.
processing millions of orders
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 [0066]), 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;
processing millions of orders,
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
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.
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/M.A.A./Examiner, Art Unit 3628
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