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 .
Response to Arguments
2. The Amendment filed on January 21, 2026, has been entered. The examiner acknowledges the amendments to claims 1, 11, and 20, the cancellation of claims 18-19, and the addition of claims 22-24.
Rejections under 35 U.S.C. § 101: Applicant argues an improvement to the technology “by eliminat[ing] the need for continuous real-time polling by using a "machine learning model
trained to predict a set of working hours for a picker during a future time period," where the
predicted working hours describe when the picker will be available in the future to service
orders, before the orders themselves have been placed.”
The Examiner notes that the specification, [0001], states that pickers, “have indicated their availability to service orders during various times of the day and days of the week in a geographical region;” the specification, [0050] describes generating a notification encouraging a picker to access information describing a set of orders placed with the online concierge system; and [0030] preferences expressed by a picker may describe how the picker prefers to receive notifications from the online concierge system. Given these facts, the Examiner is puzzled that a continuous polling model would be applied outside of an individual picker’s working hours, as polling only during work hours would already conserve network resources; the notification to the picker appears to suggest that the picker reaches out to the concierge service for tasking, as opposed to constantly being polled, and if a picker provides preferences for receiving notifications, that also appears to degrade an assumption of continuous polling. Examiner notes that cost savings or efficiency are not claimed, and the specification fails to describe this efficiency as a technology improvement. To the contrary, the specification describes elements of an agile and highly tailorable communication model designed to support both worker and client flexibility. This does not appear to go beyond running scheduling software on a computer. The basis for a concrete improvement to computer functionality is not apparent and the argument in favor of it is not compelling. The Examiner interprets the invention as an agile and highly tailorable scheduling and communication model, predicting work hours, running on a generic processor and executing on existing networks and devices. Without additional disclosure of technology improvements, the rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant argues that prior art previously cited describes changing parameters of an order-worker selection model so that, when the model receives "future data related to works or orders," the model may provide a more accurate prediction of "whether workers will select orders if offered,” concluding that prior art is referring to training a model to predict wither a user will select a particular order, and contrasting this with the claimed invention where the model predicts a set of working hours for a picker for future orders that the system "has not received." Applicant continues contrasting prior art’s prediction of worker availability based on current schedules with invention’s prediction of when workers will be available in the future for future orders, and that availability based on attributes of future time periods. Examiner finds these arguments compelling in light of additional search which failed to disclose these specific process steps elsewhere. In view of the results of this refined examination, the Examiner withdraws the rejections to claims 1, 11 and 20 under 35 U.S.C. § 103. Given the dependency of claims 3-10, 13-17 and 21-24 on independent claims 1, 11, and 20, rejections to these claims under 35 U.S.C. § 103 are also withdrawn.
Claim Rejections – 35 U.S.C. § 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, 3-11, 13-17, 20-24 are rejected under 35 U.S.C. § 101 because the claimed
invention is directed to non-statutory subject matter. The claims, 1, 3-11, 13-17, 20-24 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1, 3-11, 13-17, 20-24 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1, 3-11, 13-17, 20-24 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of improving management of online concierge systems by predicting the work hours for workers by identifying attributes of a future time period and modeling (predicting) work hours for a worker.
Claim 1 discloses: A method comprising:
identifying, at concierge, a set of attributes of one or more future time periods, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), wherein at least a subset of the attributes are contextual features describing projected conditions for the future time period; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion),
accessing a model to predict a set of working hours for a picker during a future time period, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), the set of working hours describing a predicted set of times when an availability of the picker will be available to service orders placed with concierge (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), by:
receiving historical working hours data associated with the picker,
wherein historical working hours data includes a previous set of working hours for which the picker was available to service orders placed with the concierge and contextual features for a geographical region in which the picker was willing to service orders during the previous set of working hours, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), and based at least in part on the historical working hours data; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion)
applying the model to the set of attributes of the one or more future
time periods to predict the set of working hours for the picker to fulfill future orders during the one or more future time periods, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), wherein the set of attributes are associated with the geographical region in which the picker was willing to service past orders during the previous set of working hours; wherein the future orders have not been received at a time the model is applied, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion), storing the predicted set of working hours for the picker during the one or more future time periods; and sending, at a time within the predicted set of working hours for the picker, a notification to the picker, wherein the notification includes information describing a set of orders currently available for servicing, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method for retrieving information on location of the picker and predicting availability of additional pickers to service orders associated with the region in the future time periods based on predicted working hours, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 3), where predicting additional availability of workers in the region for the time period includes retrieving their recent history with the region and predicting additional availability of pickers based on the frequency with which the picker serviced orders in the region based on the frequency of the picker servicing orders during the previous set of working hours for the picker, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion - claim 4), determining an incentive for the picker based on predicted work hours in a future time period, generating and sending the notification to the picker, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 5), assigning a picker to a cohort based on predicted working hours and a threshold level of similarity to the working hours of other pickers, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 6), where historical work hours include start and end times, the state and region or set of contextual features associated with a previous set of working hours for the picker, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 7), where the start and end times are the earliest and latest that there is a request to access information on orders placed with the system from the client, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 8), where predicting the set of working hours includes a start time, an end time or the predicted likelihood that the picker will be available during the predicted set of working hours, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 9), and receiving information about the actual working hours of the picker in the future and updating the model, where the model is a linear regression model, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion – claim 10), and wherein the contextual features include weather conditions, seasonality, and traffic conditions of the geographic region at a range of times, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion – claim 21), and determining that no working hours are predicted for at least one future time period; and storing an indication of predicted unavailability for the picker during the future time period, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion – claim 22), wherein the predicted set of working hours comprises one or more variable-length time windows having durations determined based on the set of attributes of the future time period, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion – claim 23), determining that the picker failed to access information describing available orders during a predicted set of working hours; and
updating the historical working hours data based on the failure, (economic principles and practices, following rules or instructions, observation, evaluation, judgment, opinion – claim 24).
Each of these claimed limitations involve organizing human activity, following rules or instructions, employing mental processes involving observation, evaluation, judgement, and opinion, and fundamental economic principles and practices.
Claims 11 and 13-17, and 20 recite similar abstract ideas as those identified with respect to claims 1, 3-10, and 21-24.
Thus, the concepts set forth in claims 1, 3-11, 13-17, 20-24, recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1, 3-11, 13-17, 20-24 recite additional limitations which are hardware or software elements such as a computer system comprising a processor and a computer-readable medium, an online concierge system, {training} a machine learning model, a user device, a computer program product, a non-transitory computer-readable storage medium, and a computer system comprising a processor and a non-transitory computer-readable storage medium, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1, 3-11, 13-17, 20-24 are directed to abstract ideas.
Step 2B
Claims 1, 3-11, 13-17, 20-24 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1, 3-11, 13-17, 20-24 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Conclusion
Claims 1, 11, and 20, are not rejected by prior art under 35 U.S.C. § 103.
Dependent claims 3-10, 13-17 and 21-24 are not rejected because of their inherent dependency on claims 1, 6, and 11.
The closest prior art to the invention includes Cooks, (US 20240220894 A1), “Work Management Platform,” Tanaka, (US 20160342929 A1), “Method for Determining Staffing Needs Based in part on Sensor Inputs,” and Blassin, (US 20160162478 A1), “Information Technology Platform for Language Translation and Task Management.”
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claim 1, A method, performed at a computer system comprising a processor and a computer-readable medium, Cooks teaches a work management platform, comprising:
identifying, at an online concierge system, mobile devices, a user, external systems and networks, a set of attributes of one or more future time periods; (external conditions, such as the date, time, or weather conditions), wherein at least a subset of the attributes are contextual features describing projected conditions for the future time period; (features related to the weather, such as temperature, feels-like temperature, precipitation, wind, cloud cover, visibility, humidity, or other weather conditions, the day of the week, month, or year, which also may be converted into a numeric value. Additionally, other features related to external conditions and other features that may be helpful for determining how many orders a worker will complete may also be included in the model features; Blassin teaches attributes of workers and attributes of the work assignments and products), accessing a machine learning model trained to predict a set of working hours for a picker during a future time period, the set of working hours describing a predicted set of times when the picker will be available to service orders, Cooks can determine through the work management platform, available workers. Cooks develops a prediction of the conditions that may be helpful for determining how many orders a worker will complete, predicting whether a worker will select a particular order, but does not predict working hours based on the attributes of the future time period for future orders not received. This is a prediction of order fulfilment and not a prediction of working hours needed to fulfill future orders. Blassin teaches predictions of the availability of resources and products, based on attributes of workers and task assignments. Tanaka predicts a staffing level based on sensors measuring customer traffic. None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
placed with the online concierge system, wherein the machine learning model is trained by:
receiving historical working hours data associated with the picker, wherein historical
working hours data includes a previous set of working hours for which the picker was available to service orders placed with the online concierge system, and contextual features for a geographical region in which the picker was willing to service orders during the previous set of working hours, and training the machine learning model based at least in part on the historical working hours data; applying the machine learning model to the set of attributes of the one or more future time periods to predict the set of working hours for the picker during the one or more future time periods, (Cooks models implement machine learning techniques to perform regression tasks, related to a particular order and worker to predict a likelihood that the worker would select the order, and the order-worker selection model may be changed to more accurately predict whether workers will select orders if offered. Tanaka teaches the attributes of the existing staff used to match expected demand. These do not teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.)
wherein the set of attributes are associated with the geographical region in which the picker was willing to service orders during the previous set of working hours storing the predicted set of working hours for the picker to fulfill future orders during the one or more future time periods; wherein the set of attributes are associated with the geographical region in which the picker was willing to service past orders during the previous set of working hours, wherein the online system has not received the future orders at a time the machine learning model is applied; storing the predicted set of working hours for the picker during the one or more future time periods; and sending, at a time within the predicted set of working hours for the picker, a notification to a user device of the picker, wherein the notification includes information describing a set of orders currently available for servicing.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 7:30 - 4:00.
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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.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624