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 .
Summary
This Final Office Action in response to the communication received on October 20, 2025.
Claims 1, 10, and 19 have been amended.
Claims 1-20 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.
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 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 1-9 are processes and Claims 10-20 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 module is re-trained using fulfilment information as various orders are fulfilled, and wherein the limit is adjusted based on the re-training (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 2 (and similarly Claims 11 and 20) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
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 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat Pub 2019/0325377 “Rajkhowa”, in view of US Pat Pub 2022/0114640 “Pawar”, in view of US Pat Pub 2024/0119411 “Francis”.
As per Claims 1, 10, and 19, Rajkhowa discloses a method, computer program product, and system, 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 (Rajkhowa: [0023] the database 152 can include information related to the weight of each item and/or can include information related to a dimension of each item such as height, weight, length, or volume.);
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 (Rajkhowa: [0035] In various embodiments, the joining (or clubbing) of orders to totes can include several iterative operations on the part of the item list optimization module 160. The item list optimization module 160 can first sort all items in each order by sequence number from lowest to highest. The item list optimization module 160 can then associate a first tote with items from a first order from lowest to highest sequence until the tote value constraints are violated. If the assignment of all items in the first order does not violate tote value or other constraints, and additional physical capacity exists on the cart, items from a second order can be added to an available tote for that picker. If the assignment of items in the first order does violate tote value constraints for the first tote, the item list optimization module 160 can begin to assign items to subsequent totes. Whenever assignment of items from an order violates tote value constraints, the item list optimization module 160 can engage in a backfill process.),
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 (Rajkhowa: [0035] In various embodiments, the joining (or clubbing) of orders to totes can include several iterative operations on the part of the item list optimization module 160. The item list optimization module 160 can first sort all items in each order by sequence number from lowest to highest. The item list optimization module 160 can then associate a first tote with items from a first order from lowest to highest sequence until the tote value constraints are violated. If the assignment of all items in the first order does not violate tote value or other constraints, and additional physical capacity exists on the cart, items from a second order can be added to an available tote for that picker. If the assignment of items in the first order does violate tote value constraints for the first tote, the item list optimization module 160 can begin to assign items to subsequent totes. Whenever assignment of items from an order violates tote value constraints, the item list optimization module 160 can engage in a backfill process.); and
storing the selected value as a limit for the selected attribute (Rajkhowa: [0027] In deciding whether items from an additional order are added to an existing batch during the optimization determination, the item list optimization module 160 can operate under a variety of assumptions and considerations. For example, the assigned order batch for each picker 105 should be less than or equal to the capacity of all of the totes 102 in the cart 104. Additionally, all of the items in any single order may be batched to a picker or pickers as part of the same wave. By forcing all items for a customer order to be picked in the same wave, the server 150 ensures that each wave is self-contained and avoids the need to account for partially picked orders in subsequent waves. This limitation also ensures that the process for each order from picking to packing is completed in a timely fashion.).
Rajkhowa fails to discloses a method, computer program product, and system, at a computer system comprising a processor and a computer-readable medium, comprising:
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,
wherein the limit is dynamic, wherein the order validation module is re-trained using fulfilment information as various orders are fulfilled, and wherein the limit is adjusted based on the re-training.
Pawar teaches a method, computer program product, and system, at a computer system comprising a processor and a computer-readable medium, comprising:
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 (Pawar: [0034] The training datasets 220 relate a variety of different factors to known item availabilities from the outcomes of previous delivery orders (e.g. if an item was previously found or previously unavailable). The training datasets 220 include the items included in previous delivery orders, whether the items in the previous delivery orders were picked, warehouses associated with the previous delivery orders, and a variety of characteristics associated with each of the items (which may be obtained from the inventory database 204). Each piece of data in the training datasets 220 includes the outcome of a previous delivery order (e.g., if the item was picked or not). The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability.),
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 (Pawar: [0032] The machine-learned item availability model 216 contains a set of functions generated by the modeling engine 218 from the training datasets 220 that relate the item, warehouse, and timing information, and/or any other relevant inputs, to the probability that the item is available at a warehouse. Thus, for a given item-warehouse pair, the machine-learned item availability model 216 outputs a probability that the item is available at the warehouse. The machine-learned item availability model 216 constructs the relationship between the input item-warehouse pair, timing, and/or any other inputs and the availability probability (also referred to as “availability”) that is generic enough to apply to any number of different item-warehouse pairs. In some embodiments, the probability output by the machine-learned item availability model 216 includes a confidence score. The confidence score may be the error or uncertainty score of the output availability probability and may be calculated using any standard statistical error measurement.),
evaluating a loss function for the order validation model, for each training example, using the predicted probability and the label of the training example (Pawar: [0032] The machine-learned item availability model 216 contains a set of functions generated by the modeling engine 218 from the training datasets 220 that relate the item, warehouse, and timing information, and/or any other relevant inputs, to the probability that the item is available at a warehouse. Thus, for a given item-warehouse pair, the machine-learned item availability model 216 outputs a probability that the item is available at the warehouse. The machine-learned item availability model 216 constructs the relationship between the input item-warehouse pair, timing, and/or any other inputs and the availability probability (also referred to as “availability”) that is generic enough to apply to any number of different item-warehouse pairs. In some embodiments, the probability output by the machine-learned item availability model 216 includes a confidence score. The confidence score may be the error or uncertainty score of the output availability probability and may be calculated using any standard statistical error measurement.), and
updating one or more parameters of the order validation model by backpropagation based on the evaluating (Pawar: [0037] the training datasets 220 may be periodically updated with recent previous delivery orders. The training datasets 220 may be updated with item availability information provided directly from shoppers 108, as described in further detail with reference to FIG. 5. Following updating of the training datasets 220, a modeling engine 218 may retrain a model with the updated training datasets 220, and produce a new machine-learned item availability model 216.),
wherein the limit is dynamic, wherein the order validation module is re-trained using fulfilment information as various orders are fulfilled, and wherein the limit is adjusted based on the re-training (Pawar: [0037] the training datasets 220 may be periodically updated with recent previous delivery orders. The training datasets 220 may be updated with item availability information provided directly from shoppers 108, as described in further detail with reference to FIG. 5. Following updating of the training datasets 220, a modeling engine 218 may retrain a model with the updated training datasets 220, and produce a new machine-learned item availability model 216.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa to include training the validation model as taught by Pawar, with the order fulfillment system as taught by Rajkhowa with the motivation of simplifying fulfillment of the received order by a shopper (Pawar: [0010]).
Rajkhowa and Pawar fail to disclose a method, computer program product, and system, at a computer system comprising a processor and a computer-readable medium, comprising:
based on one or more limitations of the picker.
Francis teaches a method, computer program product, and system, at a computer system comprising a processor and a computer-readable medium, comprising:
based on one or more limitations of the picker (Francis: [0744] FIG. 89A illustrates an example CCS 8900 that may implement various item/location constraints and filters when assigning items and/or generating routes. The CCS 8900 includes specific constraints and filters for different users/MSDs. For example, the CCS 8900 stores N sets of constraints and filters 8902 associated with N different users/MSDs. The CCS 8900 includes constraint/filter modules 8904 that may generate the constraints/filters for the users/MSDs as described herein. The constraint/filter modules 8904 may also retrieve and apply constraints/filters for users/MSDs when the route generation modules 8906 assign items/locations to the pickers (e.g., in a route).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa and Pawar to factor limitations of the picker as taught by Francis, with the order fulfillment system as taught by Rajkhowa and Pawar with the motivation to optimize picker efficiency since it may disincentivize inefficient movements for pickers (Francis: [0790]).
As per Claims 2, 11, and 20, Rajkhowa fails to disclose but Pawar teaches a method, computer program product, and system, wherein selecting a value for the selected attribute from the range of values comprises selecting a value corresponding to a probability nearest a threshold probability and less than the threshold probability (Pawar: [0048]-[0050]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa to include training the validation model as taught by Pawar, with the order fulfillment system as taught by Rajkhowa with the motivation of simplifying fulfillment of the received order by a shopper (Pawar: [0010]).
As per Claims 3 and 12, Rajkhowa fails to disclose but Pawar teaches a method and computer program product, wherein selecting a value for the selected attribute from the range of values comprises selecting a value corresponding to a probability less than a threshold probability (Pawar: [0048]-[0050]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa to include training the validation model as taught by Pawar, with the order fulfillment system as taught by Rajkhowa with the motivation of simplifying fulfillment of the received order by a shopper (Pawar: [0010]).
As per Claims 4 and 13, Rajkhowa discloses a method and computer program product, wherein storing the selected value as a limit for the selected attribute comprises modifying a stored value for the limit for the selected attribute to the selected value (Rajkhowa: [0027]).
As per Claims 5 and 14, Rajkhowa fails to disclose but Pawar teaches a method and computer program product, further comprising:
receiving an additional order for fulfillment by the computer system (Pawar: [0029]);
comparing attributes of the additional order to corresponding limits stored for the attributes of the additional order (Pawar: [0029]); and
displaying an interface to a customer from whom the additional order was received indicating the additional order cannot be fulfilled in response to at least one attribute of the additional order exceeding a corresponding limit (Pawar: [0029]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa to include training the validation model as taught by Pawar, with the order fulfillment system as taught by Rajkhowa with the motivation of simplifying fulfillment of the received order by a shopper (Pawar: [0010]).
As per Claims 6 and 15, Rajkhowa discloses a method and computer program product, wherein applying the order validation model to each training example of the training dataset comprises applying the order validation model to different combinations of values for attributes of the order, each combination of values including a different value of the range of values for the selected attribute and fixed values for other attributes of the order (Rajkhowa: [0035]).
As per Claims 7 and 16, Rajkhowa fails to disclose but Pawar teaches a method and computer program product, wherein the order validation model determines the probability of the picker encountering the problem with fulfilling the order based on attributes of the order and characteristics of the picker (Pawar: [0027]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajkhowa to include training the validation model as taught by Pawar, with the order fulfillment system as taught by Rajkhowa with the motivation of simplifying fulfillment of the received order by a shopper (Pawar: [0010]).
As per Claims 8 and 17, Rajkhowa discloses a method and computer program product, wherein applying the order validation model to each training example of the training dataset comprises applying the order validation model to different combinations of values for attributes of the order and characteristics of the picker, each combination including a different value of the range of values for the selected attribute, fixed values for other attributes of the order, and fixed values for characteristics of the picker (Rajkhowa: [0035]).
As per Claims 9 and 18, Rajkhowa discloses a method and computer program product, wherein the selected attribute of the order is one or more of: a weight of the order, a number of items in the order, dimensions of the order, and inclusion of one or more items with greater than a threshold dimension in the order (Rajkhowa: [0009]).
Response to Arguments
35 USC 101
Applicant's arguments filed October 20, 2025 have been fully considered but they are not persuasive.
Applicant argues that, like Ex Parte Desjardins, applicant’s specification describes a technical problem of static values in the prior art resulting in an inefficient allocation, and that the amendments “wherein the limit is dynamic, wherein the order validation module is re-trained using fulfilment information as various orders are fulfilled, and wherein the limit is adjusted based on the re-training,” solve this technical problem by way of a technical solution of re-training the order validation module using fulfilment information as various orders are fulfilled, thereby resulting in an adjustment of the limit based on the re-training. This enables pickers who have a change in capability to be assigned to tasks that may not have been available under prior limits, thereby integrating what is claimed into a practical application. Assigning tasks to pickers based on changes in capabilities is not considered a technical problem/solution. This amounts to an automation of a task that has been performed in the human mind or using pen and paper. For example, it is common for employees to bring employers a doctor’s note after an injury indicating restrictions for the employee’s performance. See MPEP 2106.05(a)(I), Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality.
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 October 20, 2025, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US Pat Pub 2019/0325377 “Rajkhowa”, in view of US Pat Pub 2022/0114640 “Pawar”, in view of US Pat Pub 2024/0119411 “Francis”.
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd Obeid can be reached at 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627