DETAILED ACTION
This Non-Final Office action is in response to Applicant’s RCE filing on 07/24/2025. Claims 1-20 are pending. The effective filing date of the claimed invention is 12/22/2022.
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 .
Claim Interpretation
Claim 1 (and similarly claims 11 and 20) has been amended to include the following amendments:
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Applicant does not refer to their own Specification at all to show support. The examiner has found some support at the conclusion of Applicant’s Spec, ADDITIONAL CONSIDERATIONS section, at [0108]:
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To note [0108] appears to be boilerplate machine learning disclosure that was put in at the conclusion of the Specification to allow for certain amendments. The examiner has interpreted the added claim amendments in light of this disclosure. See MPEP 2111.
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 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to abstract idea without significantly more.
Step 1 – Claims 1-20 relate to statutory categories. Claims 1-10 relate to process claims; claims 11-19 are manufacture claims; and claim 20 is a machine claim. Accordingly, step 1 is satisfied.
Step 2A, Prong 1 – Exemplary claim 1 (and similarly claims 11 and 20) recites abstract idea of:
accessing a first machine learning model trained to predict an acceptance likelihood that a picker will accept a service request for an order placed with an online concierge system (see MPEP 2106.04(a)(2)(I)(A) Mathematical relationships and Recentive Analytics, Inc. v. Fox Corp., App. No. 2023-2437 (Fed. Cir. 04/18/25); see also July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2; the act of “accessing” can be considered a standard commercial or legal interaction, see MPEP 2106.04(a)(2)(B) The patentee in Ultramercial claimed an eleven-step method for displaying an advertisement (ad) in exchange for access to copyrighted media, comprising steps of receiving copyrighted media, selecting an ad, offering the media in exchange for watching the selected ad, displaying the ad, allowing the consumer access to the media, and receiving payment from the sponsor of the ad. 772 F.3d. at 715, 112 USPQ2d at 1754. The Federal Circuit determined that the "combination of steps recites an abstraction—an idea, having no particular concrete or tangible form" and thus was directed to an abstract idea, which the court described as "using advertising as an exchange or currency." Id.);
applying the first machine learning model to predict the likelihood that each picker of a plurality of pickers will accept a first service request for a first order (see MPEP 2106.04(a)(2)(I)(A) Mathematical relationships; see also July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2; and Recentive Analytics, Inc. v. Fox Corp., App. No. 2023-2437 (Fed. Cir. 04/18/25));
accessing a distribution of timespans between a sending and an acceptance of a service request for one or more previous orders for one or more pickers of the plurality of pickers (see MPEP 2106.04(a)(2)(I)(A) Mathematical relationships; see also July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2; and Recentive Analytics, Inc. v. Fox Corp., App. No. 2023-2437 (Fed. Cir. 04/18/25); the act of “accessing” can be considered a standard commercial or legal interaction, see MPEP 2106.04(a)(2)(B) The patentee in Ultramercial claimed an eleven-step method for displaying an advertisement (ad) in exchange for access to copyrighted media, comprising steps of receiving copyrighted media, selecting an ad, offering the media in exchange for watching the selected ad, displaying the ad, allowing the consumer access to the media, and receiving payment from the sponsor of the ad. 772 F.3d. at 715, 112 USPQ2d at 1754. The Federal Circuit determined that the "combination of steps recites an abstraction—an idea, having no particular concrete or tangible form" and thus was directed to an abstract idea, which the court described as "using advertising as an exchange or currency." Id.);
identifying a first plurality of sets of pickers from the plurality of pickers based at least in part on a first retailer location associated with the first order, wherein each set of pickers of the first plurality of sets of pickers comprises a first number of pickers associated with a set of locations within a first radius of the first retailer location and the first number of pickers is proportional to the first radius (see MPEP 2106.04(a)(2)(III) mental processes, identifying pickers based on other data can be performed with pen/paper and/or in the human mind; see also Sanderling Management Ltd. V. Snap, Inc., No. 2021-2173 (Fed. Cir. April 12, 2023)(“ As the district court articulated, in a formulation we agree with, the claims are directed to the abstract idea "`of providing information — in this case, a processing function — based on meeting a condition,' e.g., matching a GPS location indication with a geographic location.");
generating a simulated response of each set of pickers of the first plurality of sets of pickers to the first service request by selecting from the plurality a subset of pickers associated with respective acceptance likelihoods above a threshold (see e.g. MPEP 2106.04(a)(2) referring to, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas);); randomly sampling, for each picker of the subset, from the distribution of timespans (see MPEP 2106.04(a)(2)(I)(A) Mathematical relationships, sampling data is a statistical mathematical analysis);
training a second machine learning model to predict a response of a set of pickers to a service request based at least in part on a set of attributes of the first order, the simulated response generated for each set of pickers of the first plurality of sets of pickers, and a corresponding first number of pickers and first radius, the second machine learning model stored on a computer-readable media with a set of weights wherein the training the second machine learning model updates the weights from a first set of values to a second set of values1 (see MPEP 2106.04(a)(2)(I)(A) Mathematical relationships; see also July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2; and Recentive Analytics, Inc. v. Fox Corp., App. No. 2023-2437 (Fed. Cir. 04/18/25));
receiving a new order placed with the online concierge system (see e.g. MPEP 2106.04(a)(2)(III)(C)(2) performing a mental processes of receiving data over a network);
identifying a second plurality of sets of pickers from the plurality of pickers based at least in part on a second retailer location associated with the new order, wherein each set of pickers of the second plurality of sets of pickers comprises a second number of pickers associated with a set of locations within a second radius of the second retailer location and the second number of pickers is proportional to the second radius (see MPEP 2106.04(a)(2)(III) mental processes, identifying pickers based on other data can be performed with pen/paper and/or in the human mind; see also Sanderling Management Ltd. V. Snap, Inc., No. 2021-2173 (Fed. Cir. April 12, 2023)(“ As the district court articulated, in a formulation we agree with, the claims are directed to the abstract idea "`of providing information — in this case, a processing function — based on meeting a condition,' e.g., matching a GPS location indication with a geographic location.");
for each set of pickers of the second plurality of sets of pickers, applying the second machine learning model to predict the response of a corresponding set of pickers to a second service request for the new order based at least in part on an input of the set of attributes of the new order and a corresponding second number of pickers and second radius, wherein the second machine learning model used the weights to transform the input to predicted responses of the set of pickers (see MPEP 2106.04(a)(2)(III) mental processes, identifying pickers based on other data can be performed with pen/paper and/or in the human mind; see also Sanderling Management Ltd. V. Snap, Inc., No. 2021-2173 (Fed. Cir. April 12, 2023)(“ As the district court articulated, in a formulation we agree with, the claims are directed to the abstract idea "`of providing information — in this case, a processing function — based on meeting a condition,' e.g., matching a GPS location indication with a geographic location."; MPEP 2106.04(a)(2)(I) mathematical concepts/calculation); and
determining a minimum number of at least two pickers to send the second service request for the new order based at least in part on the response predicted for each set of pickers of the second plurality of sets of pickers and a delivery time associated with the new order (see MPEP 2106.04(a)(2)(II)(B) Commercial or legal interactions - Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include:
i. structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009);
ii. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); and
iii. offer-based price optimization, which pertains to marketing, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092 (Fed. Cir. 2015)).
Accordingly, when viewed alone and in ordered combination, the examiner finds that exemplary claim 1 recites abstract idea.
Step 2A, Prong 2 – Exemplary claim 1 does not integrate the identified abstract idea with practical application. Claim 1 recites the additional elements of an online concierge system that facilitates the placement of orders; a first/second radius of a location relating to GPS location; and a computer system comprising a processor and a CRM that performs the identified abstract idea. These additional elements are recited at a high level of generality and generally act as tools to implement the abstract idea. For the claimed “online concierge system” that facilitates the placement of orders, the examiner refers to MPEP 2106.05(g)(3) insignificant extra-solution activity, Below are examples of activities that the courts have found to be insignificant extra-solution activity:
iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
ii. Taking food orders from only table-based customers or drive-through customers, Ameranth, 842 F.3d at 1241-43, 120 USPQ2d at 1854-55;
For the first/second radius relating to GPS location, the examiner refers to e.g. Sanderling Management Ltd. V. Snap Inc., No. 2021-2173 (Fed. Cir. April 12, 2023)( "If a claim's only `inventive concept' is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea." BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1290-91 [705] (Fed. Cir. 2018). The distribution rule is just that: the application of the abstract idea using common computer components. See, e.g., '412 patent 7:30-34 ("[T]he term client terminal refers to any network connected device, including, but not limited to, personal digital assistants (PDAs), tablets, electronic book readers, handheld computers, cellular phones, personal media devices (PMDs), smart phones, and/or the like."); id. 12:54-55 (noting that client terminal may include processor and main memory); id. cl. 1 (claiming use of "server," "hardware processor," and "mobile device"). "[T]he invocation of `already-available computers that are not themselves plausibly asserted to be an advance... amounts to a recitation of what is well-understood, routine, and conventional.'" Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). Sanderling's contention that the claims improve "scalability and speed" is unavailing; even if true, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer [does not] provide a sufficient inventive concept." Intell. Ventures I, 792 F.3d at 1367.). For the claimed a computer system comprising a processor and a CRM that performs the abstract idea, the examiner refers to MPEP 2106.05(f) mere instructions to apply an exception.
Accordingly, when viewed alone and in ordered combination, the examiner finds that claim 1 (and similarly claims 11 and 20) are directed to abstract idea.
Step 2B - Exemplary claim 1 does not recite significantly more than the identified abstract idea. The additional element analysis from Step 2A, Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
For the claim limitations relating to accessing, retrieving, receiving, transmitting data, this was found to be WURC - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)); MPEP 2106.05(d)(II);
For the claim limitations relating to iteratively training the model(s), this was found to be WURC - ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”);
For the claim limitations relating tostoring data, inputs, etc. found to be WURC - iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Accordingly, when viewed alone and in ordered combination, the examiner finds that claim 1 (and similarly claims 11 and 20) are directed to abstract idea.
Dependent claims – Claims 2 and 12 recite more abstract idea relating to at least MPEP 2106.04(a)(2)(III) mental processes. Claims 3 and 13 recite more abstract idea of at least MPEP 2106.04(a)(2)(I) mathematical relationships. Claims 4 and 14 recite more abstract idea of accessing a model and applying the model, see MPEP 2106.04(a)(2)(I). Claims 5 and 15 recite more abstract idea, see MPEP 2106.04(a)(2)(I). Claims 6 and 16 recite more abstract idea as shown in MPEP 2106.04(a)(2)(I). Claims 7 and 17 recite more abstract idea of collecting data, identifying/analyzing data, and sending/transmitting the data, as shown in MPEP 2106.04(III)(A)(a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)). Claims 8 and 18 recite more abstract idea of generating graphs and sending data. See id. Claims 9 and 19 recite more abstract idea of types of data, see MPEP 2106.04(a)(2)(III). Claim 10 recites more abstract idea of MPEP 2106.04(a)(2)(I).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2019/0206008 to Dutta et al. (“Dutta”) in view of U.S. Pat. No. 8,700,552 to Yu et al. (“Yu”).
With regard to claims 1, 11, and 20, Dutta discloses the claimed method comprising, at a computer system comprising a processor and a computer-readable medium:
accessing a first machine learning model trained to predict an acceptance likelihood that a picker will accept a service request for an order placed with an online concierge system (see e.g. [0025]);
applying the first machine learning model to predict the acceptance likelihood that each picker of a plurality of pickers will accept a first service request for a first order (see e.g. [0025] In some embodiments, the system manager can determine the probability that the driver will accept the ride request using a trained machine learning model.);
accessing a distribution of timespans between a sending and an acceptance of a service request for one or more previous orders for one or more pickers of the plurality of pickers (see e.g. [0030] [0066] etc.);
identifying a first plurality of sets of pickers from the plurality of pickers based at least in part on a first retailer location associated with the first order, wherein each set of pickers of the first plurality of sets of pickers comprises a first number of pickers associated with a set of locations within a first radius of the first retailer location and the first number of pickers is proportional to the first radius (see e.g. [0049] “For example, the system manager 108 may identify all drivers [i.e. pickers] currently within a predetermined distance threshold [i.e. radius] of the pick-up location 202 [first retail location] as available drivers.”);
generating a simulated response of each set of pickers of the first plurality of sets of pickers to the first service request based at least in part on the likelihood that each picker of the plurality of pickers will accept the first service request for the first order and the distribution of timespans, selecting from the plurality of pickers a subset of pickers associated with respective acceptance likelihoods above a threshold, including randomly sampling data for each picker/driver of the subset (see e.g. [0022-25] based on a probability that drivers will accept an assigned ride request; [0025]; [0035]; [0050] [0052] where data of all drivers within threshold distance is accessed, sampled, analyzed, etc. and used to determine the optimal driver to send the first request to for the required time period; see e.g. [0058] “the system manager 108 determines the expected time-to-arrival associated with each driver with respect to each of the ride requests and assigns drivers to the requests based on the determined expected time-to-arrival values to satisfy an optimization metric” - specifically disclosing sampling data for each driver [0059] etc.);
training a second machine learning model to predict a response of a set of pickers to a service request based at least in part on a set of attributes of the first order, the simulated response generated for each set of pickers of the first plurality of sets of pickers, and a corresponding first number of pickers and first radius (see e.g. [0045] where various models can be trained and retrained, and where when trained and retrained the examiner has interpreted this as turning them into different models which are then retrained, creating new models);
receiving a new order placed with the online concierge system (see e.g. [0004] When considering that modern transportation systems may receive hundreds or thousands of ride requests per minute, the effect of ride request rejections significantly reduces the efficiency and use of vehicle resources within conventional transportation systems; [0042]);
identifying a second plurality of sets of pickers from the plurality of pickers based at least in part on a second retailer location associated with the new order, wherein each set of pickers of the second plurality of sets of pickers comprises a second number of pickers associated with a set of locations within a second radius of the second retailer location and the second number of pickers is proportional to the second radius (see e.g. [0029] where multiple request come in from multiple different locations, and each request is handled separately and the picker threshold is applied to each request, see threshold as shown at [0049]);
for each set of pickers of the second plurality of sets of pickers, applying the second machine learning model to predict the response of a corresponding set of pickers to a second service request for the new order based at least in part on the set of attributes of the new order and a corresponding second number of pickers and second radius (see e.g. [0045]); and
determining a minimum number of at least two pickers (see e.g. [0059-60] driver A, B, and C) to send the second service request for the new order based at least in part on the response predicted for each set of pickers of the second plurality of sets of pickers and a delivery time associated with the new order (see e.g. [0006]; [0022]; [0065] etc.).
While Dutta does disclose machine learning techniques, e.g. [0045-46] [0080-87] [01000-108], and throughout, such as machine learning models, training the models to update the parameters, and the like. However, Dutta does not appear to disclose the generic ML language of the claim, from Applicant’s Specification at e.g. [0107-108]. This is very common in ML disclosures. For instance, see where Yu teaches at e.g. abstract, col 1 ln 27-40, col 7 ln 1-6, published claim 1, col 4 ln 65—col 5 ln 30, etc. that it would have been obvious to one of ordinary skill in the machine learning art to include the following:
the second machine learning model stored on a computer-readable media (see Yu e.g. col. 9) with a set of weights (see YU e.g. Abstract), wherein the training the second machine learning model updates the weights from a first set of values to a second set of values (see e.g. abstract, where setting weights via an error back-propagation procedure; col. 1 ln 27-40 “Generally, after inputting of each data entry, a value of each weight associated with each interconnection of each hidden layer is set via an error back-propagation procedure so that the output from the output layer matches a label assigned to the training data entry. The foregoing process is then repeated a number of times to produce the initially trained DNN.”; col 7 ln 1-16; published claim 1)
wherein the second machine learning model uses the weights to transform the input to predicted responses (see e.g. col. 4, ln 65—col. 5, ln. 30) the advantage of this being that, as indicated in Yu col 5 ln 25-30, “setting the values of said weights associated with the interconnections of each hidden layer via the error back-propagation procedure so that the output from the output layer matches the senone label assigned to the speech frame.”
Therefore, it would have been obvious to one of ordinary skill in the machine learning art before the effective filing date of the claimed invention to modify Dutta’s system that does include machine learning, to include the machine learning techniques explicitly set forth Yu (such as machine learning storing weights, receiving input, updating weighted valued based on back propagation, and then applies the updated trained model to the input values “so that the output from the output layer matches the senone label assigned to the speech frame.” See Yu, col. 5 ln. 25-30.
With regard to claims 2 and 12, Dutta further discloses where the response predicted for each set of pickers of the second plurality of sets of pickers comprises a timespan between a sending of the second service request for the new order to the corresponding set of pickers and an acceptance of the second service request for the new order by the corresponding set of pickers (see abstract, for each request, the system determines an estimated time-to-arrival which includes a timespan between sending the request and acceptance, which can cause delays obviously if rejected – [0006] [0022] etc.).
With regard to claims 3 and 13, Dutta further discloses where the minimum number of pickers to send the second service request for the new order is determined by minimizing the timespan between a sending of the second service request for the new order to the corresponding set of pickers and an acceptance of the second service request for the new order by the corresponding set of pickers (see e.g. [0004] where for instance the request can be sent to a minimum of a single driver; [0006]; [0022]; [0065] etc.).
With regard to claims 4 and 14, Dutta further discloses accessing a third machine learning model trained to predict an amount of time it will take for a picker to travel when servicing an order placed with the online concierge system; and applying the third machine learning model to predict the amount of time it will take for one or more pickers of the plurality of pickers to travel when servicing the new order (see e.g. [0025] where the system trains the model based on the incoming data, and each iteration is a new model so the third time around this would be considered the third model).
With regard to claims 5 and 15, Dutta further discloses where determining the minimum number of pickers to send the second service request for the new order is further based at least in part on the predicted amount of time it will take for one or more pickers of the plurality of pickers to travel when servicing the new order (see abstract, based on estimated time-to-arrival).
With regard to claims 6 and 16, Dutta further discloses where the minimum number of pickers to send the second service request for the new order is determined by minimizing a sum of the timespan between a sending of the second service request for the new order to the corresponding set of pickers and an acceptance of the second service request for the new order by the corresponding set of pickers and the predicted amount of time it will take for one or more pickers of the plurality of pickers to travel when servicing the new order (see e.g. [0004], [0032]).
With regard to claims 7 and 17, Dutta further discloses identifying a set of pickers of the plurality of pickers to send the second service request for the new order based at least in part on a set of constraints associated with each new order of a plurality of new orders, wherein the set of constraints comprises the minimum number of pickers to send the second service request for the new order; and sending the second service request for the new order to a set of picker client devices associated with the identified set of pickers (see e.g. [0004], [0032]).
With regard to claims 8 and 18, Dutta further discloses where determining the minimum number of pickers to send the second service request for the new order comprises: generating one or more graphs based at least in part on the response predicted for each set of pickers of the second plurality of sets of pickers; and determining the minimum number of pickers to send the second service request for the new order based at least in part on the one or more graphs and the delivery time associated with the new order (see e.g. Fig. 3B).
With regard to claims 9 and 19, Dutta further discloses where the set of attributes comprises one or more of: an amount of earnings associated with an order, a retailer associated with an order, a weight associated with an order, one or more tasks involved in servicing an order, a number of items included in an order, a volume associated with an order, a type of item included in an order, a retailer location at which one or more items included in an order are to be collected, a delivery time associated with an order, a delivery location associated with an order, and instructions specifying how one or more items included in an order are to be collected (see e.g. [0037] distance between the various locations).
With regard to claim 10, Dutta further discloses where training the second machine learning model is further based at least in part on information describing a demand side and a supply side associated with the online concierge system (see e.g. [0083]).
Response to Arguments
Applicant's arguments filed 07/24/2025 have been fully considered but they are not persuasive.
The examiner has withdrawn the 112 rejections based on the amendments provided.
Applicant argues that the claims are eligible under 35 USC 101 based on the cold start problem. The examiner has searched Applicant’s Spec and has been unable to find any reference to a cold start problem. The claims also do not mention this. Accordingly, the examiner has reviewed the claims and the findings are above. The boilerplate ML disclosure does not provide any practical application, or significantly more.
Applicant argues that the amendments render the claim over the prior art. The examiner has converted the rejection from a 102 to 103, thereby the arguments are moot. See the new reference Yu to teach the boilerplate ML disclosure and claimed limitations.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
1 The examiner refers to Applicant’s originally-filed Specification at the ADDITIONAL CONSIDERATIONS, e.g. [0108] “The description herein may describe processes and systems that use machine learning models in the performance of their described functionalities. A "machine learning model," as used herein, comprises one or more machine learning models that perform the described functionality. Machine learning models may be stored on one or more computer-readable media with a set of weights. These weights are parameters used by the machine learning model to transform input data received by the model into output data. The weights may be generated through a training process, whereby the machine learning model is trained based on a set of training examples and labels associated with the training examples. The training process may include: applying the machine learning model to a training example, comparing an output of the machine learning model to the label associated with the training example, and updating weights associated with the machine learning model through a back-propagation process. The weights may be stored on one or more computer-readable media, and are used by a system when applying the machine learning model to new data.”
Here, Applicant generally states how the machine learning is used, and how the machine learning algorithm can be used to manipulate differing weights. This limitation requires that the algorithm is stored on a CRM with set of weights, where the weights are updated based on the applying the training algorithm.