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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 28, 2026 has been entered.
Claims 1, 10, and 19 have been amended.
Claims 21-22 have been added.
Claims 2 and 11 have been cancelled.
Claims 1, 3-10, and 12-22 are pending.
The effective filing date of the claimed invention is April 29, 2023.
Response to Amendment
Amendments to Claims 1, 10, and 19 are acknowledged. Amendments to Claims 1, 10, and 19 are sufficient to overcome the 35 USC 103 rejection of Claims 1, 3-10, and 12-20.
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, 3-10, and 12-22 rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 1, 8-9, and 21-22 are processes and Claims 10 and 12-20 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 (and similarly Claims 10 and 19) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
A method comprising a processor and a computer-readable medium, comprising:
maintaining a range of values for an attribute for orders to be fulfilled by an online concierge shopping system (See MPEP 2106.04(a)(2)(III) mental processes, 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));
applying an order validation model to each value of the range of values, the order validation model determining a probability that a picker would encounter a problem fulfilling the order based at least in part on the value of the attribute (See MPEP 2106.04(a)(2)(I) mathematical concepts, v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), MPEP 2106.04(a)(2)(II), organizing human activity, 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 MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2), wherein the order validation model is trained by:
obtaining a training dataset including a plurality of training examples, each training example including a value for the attribute for one of a plurality of previous orders and a label indicating whether a picker from the training example encountered a problem fulfilling the previous order based on one or more limitations of the picker (See MPEP 2106.04(a)(2)(II), organizing human activity, 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 MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2),
applying the order validation model to each training example of the training dataset to generate a predicted probability that the picker from the training example encountered a problem fulfilling the previous order (See MPEP 2106.04(a)(2)(I) mathematical concepts, v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), MPEP 2106.04(a)(2)(II), organizing human activity, 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 MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2),
evaluating a loss function for the order validation model, for each training example, using the predicted probability and the label of the training example (See MPEP 2106.04(a)(2)(I) mathematical concepts, v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), MPEP 2106.04(a)(2)(II), organizing human activity, 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 MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2), and
updating one or more parameters of the order validation model by backpropagation based on the evaluating (See MPEP 2106.04(a)(2)(I) mathematical concepts, v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), and See July 2024 Subject Matter Eligibility Example 47, Claim 2);
selecting a value for the selected attribute from the range of values based on the probabilities that a picker would encounter a problem fulfilling the order determined from applying the order validation model to each value of the range of values of the attribute (See MPEP 2106.04(a)(2)(I) mathematical concepts, v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), MPEP 2106.04(a)(2)(II), organizing human activity, 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 MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2); and
storing the selected value as a limit for the selected attribute, wherein the limit is dynamic, wherein the order validation model is re-trained using fulfilment information as various orders are fulfilled, and wherein the re-training is configured to adjust the limit and update parameters of the order validation model based on predicted probabilities of the picker encountering problems fulfilling the order (See MPEP 2106.04(a)(2)(I) mathematical concepts, a formula for computing an alarm limit, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978) (B1=B0 (1.0–F) + PVL(F)), MPEP 2106.04(a)(2)(III) mental processes, 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), and See July 2024 Subject Matter Eligibility Example 47, Claim 2).
Claim 1 (and similarly Claims 10 and 19) is directed to a series of steps for evaluating an order for fulfillment to predict a likelihood of having problems, which is a commercial interaction and thus grouped as a certain method of organizing human interactions, being performed using mathematical calculations and mental processes. The mere nominal recitation of a computer system and a computer-readable medium does not take the claim out of the method of organizing human interactions, mathematical calculations, and mental processes. Thus, Claim 1 (and similarly Claims 10 and 19) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 (and similarly Claims 10 and 19) is not integrated into a practical application. The additional elements are: a computer system and a computer-readable medium that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 (and similarly Claims 10 and 19) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 (and similarly Claims 10 and 19) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) maintaining a range of values, b) applying an order validation model to each value of the range of values, c) obtaining a training dataset, d) applying the order validation model to each training example of the training dataset to generate a predicted probability, e) evaluating a loss function for the order validation model, f) updating one or more parameters of the order validation model, g) selecting a value for the selected attribute based on the probabilities that a picker would encounter a problem fulfilling the order, and h) storing the selected value as a limit for the selected attribute, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 1 (and similarly Claims 10 and 19) is ineligible.
Claim 3 (and similarly Claim 12) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 4 (and similarly Claim13) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 5 (and similarly Claim 14) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 (and similarly Claim 15) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 7 (and similarly Claim 16) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 8 (and similarly Claim 17) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 9 (and similarly Claim 18) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 20 recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 21 recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 22 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Prior Art
Claims 1, 3-10 and 12-22 in the instant application are allowable over the prior art because the prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A method, at a computer system comprising a processor and a computer-readable medium, comprising:
maintaining a range of values for an attribute for orders to be fulfilled by an online concierge shopping system;
applying an order validation model to each value of the range of values, the order validation model determining a probability that a picker would encounter a problem fulfilling the order based at least in part on the value of the attribute, wherein the order validation model is trained by:
obtaining a training dataset including a plurality of training examples, each training example including a value for the attribute for one of a plurality of previous orders and a label indicating whether a picker from the training example encountered a problem fulfilling the previous order based on one or more limitations of the picker, applying the order validation model to each training example of the training dataset to generate a predicted probability that the picker from the training example encountered a problem fulfilling the previous order,
evaluating a loss function for the order validation model, for each training example, using the predicted probability and the label of the training example, and
updating one or more parameters of the order validation model by backpropagation based on the evaluating;
selecting a value for the selected attribute from the range of values based on the probabilities that a picker would encounter a problem fulfilling the order determined from applying the order validation model to each value of the range of values of the attribute; and
storing the selected value as a limit for the selected attribute, wherein the limit is dynamic, wherein the order validation model is re-trained using fulfilment information as various orders are fulfilled, and wherein the re-training is configured to adjust the limit and update parameters of the order validation model based on predicted probabilities of the picker encountering problems fulfilling the order. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat Pub 2019/0325377 “Rajkhowa” discloses dual optimization of pick walk and tote fill rates in order picking, providing improved order picking speed and quality by optimizing pick routing with consideration of both proximity constraints and tote value constraints. Tote value constraints can include constraints on carrying capacity, volume, size in a particular dimension, or weight capacity.. Rajkhowa fails to disclose selecting a value for a selected attribute from the range of values based on the probabilities that a picker would encounter a problem fulfilling the order determined from applying the order validation model to each value of the range of values of the attribute; the selected value is a limit for the selected attribute, wherein the limit is dynamic, wherein the order validation model is re-trained using fulfilment information as various orders are fulfilled, and wherein the re-training is configured to adjust the limit and update parameters of the order validation model based on predicted probabilities of the picker encountering problems fulfilling the order.
US Pat Pub 2022/0114640 “Pawar” teaches an online concierge system maintains a graph of items available for purchase. The graph maintains edges between items, where an edge between an item and an additional item indicates that one or more customers have previously replaced the item with the additional item. The edge between the item and the additional item also identifies a number of times customers have replaced the item with the additional item. When a customer orders an item, the online concierge system traverses the graph of items to identify candidate replacement items for the ordered item and identifies one or more of the candidate replacement items to the customer.. Pawar fails to teach selecting a value for a selected attribute from the range of values based on the probabilities that a picker would encounter a problem fulfilling the order determined from applying the order validation model to each value of the range of values of the attribute; the selected value is a limit for the selected attribute, wherein the limit is dynamic, wherein the order validation model is re-trained using fulfilment information as various orders are fulfilled, and wherein the re-training is configured to adjust the limit and update parameters of the order validation model based on predicted probabilities of the picker encountering problems fulfilling the order.
US Pat Pub 2024/0119411 “Francis” teaches picker mobile scanning devices (MSDs) are associated with different pickers in a store. A computing system receives initial customer orders. The computing system assigns ordered items from the initial customer orders to the picker MSDs and stores a plurality of picker records, wherein each picker record includes data associated with a corresponding picker and picker MSD, and wherein each picker record includes an order limit number that indicates a number of different customer orders that can be assigned to the picker MSD.. Francis fails to teach selecting a value for a selected attribute from the range of values based on the probabilities that a picker would encounter a problem fulfilling the order determined from applying the order validation model to each value of the range of values of the attribute; the selected value is a limit for the selected attribute, wherein the limit is dynamic, wherein the order validation model is re-trained using fulfilment information as various orders are fulfilled, and wherein the re-training is configured to adjust the limit and update parameters of the order validation model based on predicted probabilities of the picker encountering problems fulfilling the order.
Response to Arguments
35 USC 101
Applicant's arguments filed April 28, 2026 have been fully considered but they are not persuasive.
Applicant argues that, like Ex Parte Desjardins, the amendments descirge how re-training provides an improvement to a machine learning model. The amendments describe how re-training the machine learning model adjusts a limit associated with a picker and updates parameters of the machine learning based on predicted probabilities of the picker encountering problems fulfilling an order. Adjusting the limit and updating the parameters allow the machine learning model to be response to orders fulfilled between a time when the machine learning model was most recently trained and a time when the model is retrained.
The claims are not found to be eligible under the Ex Parte Desjardins rationale either. In Ex Parte Desjardins, the claimed training method reduced storage requirements and preserved task performance across sequential training. The ARP characterized this as an improvement “in training the machine learning model itself,” not merely an abstract algorithm implemented on a generic computer. The panel explained that where a claimed invention improves the operation of a machine learning system, such as by enhancing its training efficiency or preserving prior learning, it is not “directed to” an abstract idea under Alice Step 1. However, the re-training of the order validation model of this application is found to be how most modeling works, as you want to continue refining the model as more data comes in. Ex Parte Desjardines was improving machine learning itself, not applying basic machine learning to improve the efficiency of an abstract idea.
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed April 28, 2026, with respect to 35 USC 103 have been fully considered and are persuasive. The 35 USC 103 rejection of Claims 1, 3-10, and 12-20 has been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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