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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Status of Claims
Claims 1-8, 10-17, and 19-20 remain pending, and are rejected.
Claims 9 and 18 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 8/13/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 8/13/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 12-13 of the Applicant’s Remarks, arguments are made that the claims have been recited to address a technical problem faced by machine learning models, such as how feedback available to train a model that predicts acceptance of a replacement item is elicited under different conditions that carry different information about the user’s preference. Feedback elicited from a selection of an item relative to alternatives and feedback elicited by presenting a single item as a binary choice should not have a single label as the weights must be distinguished between them. The Applicant argues that this is solved by using a machine learning model with a specific structure and training the model based on the specific structure, including describing a machine learning model with a first and second layer, the first layer processing a representation of items in the prior order other than the item, generating an intermediate output, and providing the intermediate output to the second layer that is configured to determine the output score based on the intermediate output and one or more features describing the item. The label hierarchy sets the target against which the output score is compared, and determines the weights that training produces for both layers. On page 14, the claims are compared to Recentive Analytics in how they differ, such as the present claims no not recite a generic model applied to a new dataset, but recites the arrangement of the model into two layers, the derivation of the training target from the condition under which the feedback was elicited, and the updating of weights of both layers on that target.
Examiner respectfully disagrees. Feedback elicited under different conditions to feed into an algorithm of predicting acceptance of a replacement item does not describe any technical problem. The differences of feedback elicited from a selection of an item identifying a preferred item relative to the presented alternatives, and feedback elicited by a single item as a binary choice identifying only that the item was tolerable does not represent any technical activity. It merely assigns different weights to different responses of a user regarding item alternatives, and using those weights in scoring items to recommend to the user. This activity is simply about different context of actions that identify a user preference, which is a sales and marketing concept. The machine learning structure also is not recited with any particularity except that there are two layers. What underlying technology of how the layers themselves function specifically to machine learning are not recited in the claims nor disclosed in the specification. The layers merely output values of the abstract idea to use that value in another calculation. The layers merely operate as a black box to generate an output of a representation of items in a prior order and a score using one or more features of the item. These are all abstract concepts of simply calculating information to determine a score for items that are more likely to be accepted by the user. How the machine learning itself functions at any technical level is not recited in the claims. The label hierarchy merely assigns values for customer actions in the calculating of the abstract idea. As discussed above, there is not specific technical structure to the machine learning model except the general recitation that there are two layers, and there is no recitation to any particular technical functionality of how a machine learning model operates. The machine learning model merely receives information of the abstract idea to provide an output of a score for the abstract idea. The claims are similar to Recentive Analytics in that they merely recite inherent machine learning steps, and do not change or improve any machine learning functionality. Any changes in the algorithm are only of the abstract idea of scoring alternative items likely to be purchased by the user.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 8/13/2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered, and are persuasive. The claims as amended are free of the prior art and are not an obvious combination of features, as detailed below.
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 1-8, 10-17, and 19-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-8 are directed to a method, which is a process. Claims 10-17 are directed to a non-transitory computer readable storage medium, which is an article of manufacture. Claims 19-20 are directed to a computer program product, which is an apparatus. Therefore, claims 1-8, 10-17, and 19-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 19 as representative, claim 19 sets forth the following limitations reciting the abstract idea of determining a replacement item for an item in an order that is unavailable:
generate an output score that represents the likelihood that a user would accept a replacement item for in input order;
labeling each of the set of items according to a label hierarchy wherein the hierarchy includes a first type of user feedback received in response to a first type of stimulus associated with a first label value and a second type of user feedback received in response to a second type of stimulus associated with a second label value, wherein the first label value is greater than the second label value, the first type of stimulus comprising a selection by the user of a candidate replacement item from among a set of candidate replacement items presented to the user and the second type of stimulus comprising an acceptance of a single replacement item that was selected for the order and presented to the user as a binary choice to accept or reject the single replacement item;
processing a representation of items in the prior order other than the item to generate an intermediate output;
determine the output score based on the intermediate output and one or more features describing the item;
comparing the output score to a target score for the item, the target score comprising the first label value for an item that received the first type of user feedback and the second label value for an item that received the second type of user feedback;
updating a set of weights of weights based on the comparison;
receiving an order placed by a user for a fulfillment by a picker at a physical location, wherein the order includes an ordered item that is unavailable for fulfillment by the picker at the physical location;
accessing a set of contextual features about the order placed by the user, the set of contextual features including a representation of items in the order other than the ordered item;
computing a score for each of a plurality of candidate replacement items by applying the updated sets of weights to the set of contextual features;
selecting a candidate replacement item of the plurality of candidate replacement items based on the scores;
causing the selected candidate replacement item to be displayed as a suggested replacement item for the ordered item.
The recited limitations above set forth the abstract idea of determining a replacement item for an item in an order that is unavailable. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors, etc.). The claims recite steps for accessing contextual features of a user and computing a score to determine what to suggest as a replacement item when an item is unavailable in an order (see specification [0003] disclosing the problem of appropriately selecting and suggesting replacement items according to the particular shopper and order), which is a sales activity and behavior.
Such concepts have been identified by the courts as abstract ideas (see: 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 19 recites additional limitations in the claims, such as:
a processor that executes instructions;
a non-transitory computer readable storage medium having instructions executable by the processor;
generating training data for a machine learning model;
training the machine learning model on the training data by, for each item of the set of items, applying the machine learning model to a set of feature associated with the item and with a prior order in which the item received the feedback;
a first layer of the machine learning model;
providing the intermediate output as an input to a second layer of the machine learning model;
a back-propagation process;
applying the machine learning model to the set of contextual features;
Taken individually and as a whole, representative claim 19 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a processor and a non-transitory computer readable storage medium, these elements are recited with a very high level of generality. The only description of the processor come in specification paragraph [0075], which merely discloses that the processor comprises one or more processing units that, individually or together, perform the steps of instructions. The only description for the computer readable storage medium is in specification paragraph [0075], which discloses the computer readable medium comprises one or more computer-readable media that comprises instructions to cause a processor to perform the steps of the instructions. As such, it is evident that these elements are any generic computing components that are merely being leveraged to implement that abstract idea with a computing device. The elements are also only briefly recited as executing the instructions to perform the limitations of the claims. The machine learning is also recited with a very high level of generality. The claims merely recite applying the model to provide an output without any detail to the underlying technology of machine learning. Furthermore, specification paragraph [0051] discloses where the machine learning model can be any of a large number of generic models. As such, it is evident that the machine learning model is any generic machine learning model that is merely applied to the abstract idea to provide a general link to a computing environment, and the claims are wholly directed to the abstract idea.
In view of the above, under Step 2A (prong 2), claim 19 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Taken individually or as a whole, the additional elements of claim 19 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in representative claim 19 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computer device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 19 do not add anything further than when they are considered individually.
In view of the above, representative claim 9 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding claim 1 (method), claim 1 recites at least substantially similar concepts and elements as recited in claim 19 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 1 is rejected under at least similar rationale as provided above regarding claim 19.
Regarding Claim 10 (non-transitory computer readable storage medium), claim 10 recites at least substantially similar concepts and elements as recited in claim 19 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 10 is rejected under at least similar rationale as provided above regarding claim 19.
Dependent claims 2-8, 11-17, and 20 recite further complexity to the judicial exception (abstract idea) of claim 19, such as by further defining the algorithm for determining a replacement item for an item in an order that is unavailable. Thus, each of claims 2-8, 11-17, and 20 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-8, 11-17, and 20 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-8, 11-17, and 20 rely on at least similar elements as recited in claim 19. Any additional elements (e.g., a device of the picker (claim 7)) are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-8, 11-17, and 20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-8, 11-17, and 20 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 19. Thus, dependent claims 2-8, 11-17, and 20 do not add “significantly more” to the abstract idea.
Subject Matter Free of Prior Art
Claims 1-8, 10-17, and 19-20 are determined to have overcome the prior art of rejection and are free of the prior art, however, the claims remain rejected under 35 U.S.C. 101.
Claims 1-8, 10-17, and 19-20 are found to overcome the prior art rejection for the reasons as set forth below.
Claim 1 recites the claimed features of the first type of stimulus comprising a selection by the user of a candidate replacement item from among a set of candidate replacement items presented to the user and the second type of stimulus comprising an acceptance of a single replacement item that was selected for the order and presented to the user as a binary choice to accept or reject the single replacement item;
The closes prior art was found to be as follows:
Franey (US 20220237530 A1) discloses [0074] – “a determination can be made whether or not to apply an acceptance probability algorithm to select the substitute product. The server or device can determine whether or not to select the substitute product using the distance metric or an acceptance probability score or a weighted score that is a combination of the distance metric and an acceptance probability score for a substitute product. The server can make the determination based in part on a customer preference (e.g., indicated in customer profile), store policy (e.g., policy for warehouse or company), and/or a policy for a particular product or type of product. For example, certain products may have limited available substitute products or a large number of potential substitute products and the server can assign a substitute policy to the respective product or product class indicating if substitutes can be selected using the distance metric, acceptance probability score or weighted score”.
However, Franey does not disclose the first and second type of stimulus, especially where label values are different for types of stimulus of selecting an item from a set of items and when a single replacement item is accepted or rejected in binary choice. Notably, Franey also does not disclose processing items other than the item in a prior order in one layer to output to a second layer for the output score, comparing the output score with a target score, and updating weights.
Mehta (US 20170236434 A1) discloses [0039] – “process 400 can received explicit user feedback with respect to the educational content. The explicit user feedback can be used to adjust the base score or current, score. The ranking of the user can also be taken into consideration. For example, if an ‘expert’ ranked user in business accounting ‘likes’ a video lecture on state taxation currently rated ‘medium difficulty’, then the rating of the video lecture can be increased. It is noted that the ratings or other explicit behaviors of higher ranked and/or more historical active users can be weighted higher than the ratings of lower ranked and/or less historical active users. For example, a ‘Level 7’ ranked user's ‘like’ can be weighted higher with respect to calculating a score than a ‘Level 2’ ranked user's ‘like’.”
However, Mehta does not disclose the types of stimulus or processing items other than the item in a prior order in one layer to output to a second layer for the output score, comparing the output score with a target score, and updating weights.
Sheshansh (US 12,682,312 B2) discloses claim 1 – “the deep neural network takes as input features of two different orders and outputs a respective priority score for each of the two orders; training, by the order prioritization service, the deep neural network to predict priority scores for orders to be fulfilled using a plurality of training samples, each training sample generated from modeling dataset, each training sample of the plurality of training samples to adjust weights in the deep neural network, wherein training the deep neural network includes inputting different portions of the modeling dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the deep neural network”.
However, Sheshansh does not disclose the first and second type of stimulus, especially where label values are different for types of stimulus of selecting an item from a set of items and when a single replacement item is accepted or rejected in binary choice, receiving an order, accessing contextual features, and selecting candidate replacement items.
PTO Reference U (see PTO-892 Reference U mailed on 7/8/2025) discloses a method to infer networks of substitutable products using text from product reviews and specifications in order to suggest relevant items based on their opinions, context, and behavior.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
the first type of stimulus comprising a selection by the user of a candidate replacement item from among a set of candidate replacement items presented to the user and the second type of stimulus comprising an acceptance of a single replacement item that was selected for the order and presented to the user as a binary choice to accept or reject the single replacement item;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 1-8, 10-17, and 19-20 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
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 TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 8:30am - 7:00pm EST.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 10/8/2026
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