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 June 10, 2026 has been entered. The examiner acknowledges the amendments to claims 1, 10-11, 20, 22 and the cancellation of claims 2, 12, 18-19.
Rejections under 35 U.S.C. § 101: Applicant argues a practical application reciting the application of a two-stage machine learning pipeline as an improvement to the ML system’s computational architecture. Argument states that the two-stage pipeline resolves a computational tradeoff intrinsic to employing complex ML models (neural networks) that extract relevant features more effectively but are computationally expensive to train, and simpler models (regression models) that are less effective in extracting features but retrain efficiently and support frequent updates in the invention.
The Examiner notes that the multiple model approach described above currently exists in the art as documented in the references cited as well as found in a simple internet search. Although the multiple model approach may be an effective system design, it does not appear to be innovative to the current art, and not an additional improvement to the technology or technical field. It is not apparent that the invention improves the functioning of a computer or integrates with a particular machine; the scheduling information derived from the operation appears to be applied through general linkage to existing elements in the technological environment. As a result, the invention does not disclose a practical application and the rejections under 35 U.S.C. § 101 will not be withdrawn.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, a set of attributes of one or more future time periods, (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; (following rules or instructions, observation, evaluation, judgement, opinion),
accessing a regression model trained to predict a set of working hours for a picker during a future time period, (following rules or instructions, observation, evaluation, judgement, opinion), the set of working hours describing a predicted set of times when the picker will be available to service orders placed with concierge, (following rules or instructions, observation, evaluation, judgement, opinion),
wherein the regression model is employs 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 and contextual features for a geographical region in which the picker was willing to service orders during the previous set of working hours, (following rules or instructions, observation, evaluation, judgement, opinion), and
training the regression model based at least in part on the historical working hours data; (following rules or instructions, observation, evaluation, judgement, opinion),
applying the regression 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, (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 future orders have not been received at a time the regression model is applied; (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;
retrieving information describing the geographical region associated with an availability of the picker to service orders placed; (following rules or instructions, observation, evaluation, judgement, opinion),
predicting, using a supply prediction model with more parameters than the regression model, an availability of a set of pickers to service orders associated with the geographical region during the one or more future time periods, based on the predicted set of working hours for the picker during the one or more future time periods; (following rules or instructions, observation, evaluation, judgement, opinion),
determining that the predicted availability of the set of pickers exceeds a threshold value; (following rules or instructions, observation, evaluation, judgement, opinion),
responsive to determining that the predicted availability exceeds the threshold value, 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; (following rules or instructions, observation, evaluation, judgement, opinion),
retraining the regression model on a first set of training examples, wherein retraining the regression model comprises, for each training example of the first set of training examples: (following rules or instructions, observation, evaluation, judgement, opinion),
accessing the training example, wherein the training example comprises input
data comprising attributes of a time period and historical working hours data associated with the picker for the time period, and a label comprising an actual set of working hours during which the picker was available to service orders during the time period; (following rules or instructions, observation, evaluation, judgement, opinion),
inputting the input data into the regression model to generate a predicted set of working hours as an output; (following rules or instructions, observation, evaluation, judgement, opinion),
computing a loss score by comparing the predicted set of working hours to the
label using a loss function; (following rules or instructions, observation, evaluation, judgement, opinion),and
updating parameters of the regression model based on the loss score; (following rules or instructions, observation, evaluation, judgement, opinion),and
wherein retraining the model comprises, for each training example of the second set of training examples:
accessing the training example, wherein the training example comprises input
data comprising predicted working hours for a set of pickers associated with the geographical region during a time period, and a label comprising an actual availability of pickers to service orders in the geographical region during the time period; (following rules or instructions, observation, evaluation, judgement, opinion),
inputting the input data into the model to generate a predicted
regional availability as an output; (following rules or instructions, observation, evaluation, judgement, opinion),
computing a loss score by comparing the predicted regional availability to the
label using a loss function; and updating parameters of the model based on the loss score, (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, (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, (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, (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, (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, (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, (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, (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, (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, (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, (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, (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, (following rules or instructions, observation, evaluation, judgment, opinion – claim 24).
Each of these claimed limitations involve organizing human activity, following rules or instructions, or employing mental processes involving observation, evaluation, judgement, and opinion.
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, a machine learning regression model, a supply prediction model comprising a neural network, a user device, retraining the neural network on a second set of training examples, 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
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 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.
Claims 1, 3-11, 13-17, 20-24 were previously not rejected under 35 U.S.C. § 103.
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 8-4:30.
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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
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