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 .
Prosecution Status
Applicant’s amendments filed 7/28/2026 have been received and reviewed. The status of the claims is as follows:
Claims 1-20 are pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts, which is considered a commercial interaction. Commercial interactions fall within a subject matter grouping of abstract ideas which the Courts have considered ineligible (Certain methods of organizing human activity). The claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claim 1-10 recite a method and at least one step. Claims 11-19 recite a non-transitory computer readable storage medium. Claim 20 recites a system comprising a processor and a non-transitory computer readable storage medium. Therefore, the claims are each directed to one of the four statutory categories of invention (process, apparatus, manufacture).
Under step 2A of the Alice/Mayo framework, it must be considered whether the claims are “directed to” an abstract idea. That is, whether the claims recite an abstract idea and fail to integrate the abstract idea into a practical application.
Regarding independent claim 1, the claim sets forth a process in which acquisition of an item the customer was previously interested in acquiring is recommended following failed attempts, in the following limitations:
during a first session, receiving a request to fulfill an order;
receiving a message indicating that an item from the order was not fulfilled;
logging the item in connection with a profile of the user
during a second session, the second session subsequent to the first session,
receiving a message indicating that the logged item is available for fulfillment;
applying an intent prediction model to output an intent score indicative of an intent of a user of the user device to acquire the logged item,
wherein the intent prediction model was trained by:
generating a set of training examples for the intent prediction model, wherein generating a training example of the set of training examples comprises:
receiving a prior order during a previous session
identifying, from the prior order, an item that was not fulfilled,
presenting the item to the other user during a subsequent session, wherein the item is presented to the other user, and
generating a label for the training example, the label indicating whether the other user interacted with the item
accessing a set of training examples including shopping intent training data, user training data, item training data, and order training data,
applying the intent prediction model to the set of training examples to generate a training output corresponding to a predicted training set of logged items and associated training intent scores,
back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the intent prediction model, and one or more of the error terms are based on a difference between the label and the predicted training set of logged items and associated training intent scores, and
stopping the back-propagation after the one or more loss functions satisfy one or more criteria;
ranking the logged item for the user based on the intent score;
generating, based on the ranking, a recommendation to acquire the logged item;
.
The above-recited limitations establish a commercial interaction with a consumer to make a recommendation to the consumer to acquire an item the customer was previously interested in acquiring after failed attempts. This arrangement amounts to both a sales activity or behavior; and business relations. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts (See MPEP 2106.04(a)).
Claim 1 does recite additional elements:
between the online system and a user device
from the user device,
at the online system,
stored in a database maintained by the online system;
between the online system and the user device
wherein the intent prediction model was trained by:
generating a set of training examples for the intent prediction model, wherein generating a training example of the set of training examples comprises:
from another user device of another user,
between the other user device and the online system,
between the other user device and the online system,
in a user interface presented on the other user device, and
generating a label for the training example, the label indicating whether the other user interacted with the item in the user interface presented on the other user device
accessing a set of training examples including shopping intent training data, user training data, item training data, and order training data,
applying the intent prediction model to the set of training examples to generate a training output corresponding to a predicted training set of logged items and associated training intent scores,
back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the intent prediction model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted training set of logged items and associated training intent scores, and
stopping the back-propagation after the one or more loss functions satisfy one or more criteria;
a user interface including and
causing the user device to display the generated user interface
These additional elements merely amount to the general application of the abstract idea to a technological environment (e.g., “between the online system and a user device”) and insignificant post-solution activity (presenting in an interface, causing to display). The Examiner notes that while the steps of the training process are abstract in their recitation, as indicated above, the process set forth (applying a set of training examples to a model, back-propagating error terms from loss functions, and stopping the back-propagation after the loss functions satisfy criteria) describe a generic machine learning-based training process. Put another way, as noted in Recentive, “Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning” (Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)). Accordingly, the recited iterative training represents the mere computerization of the intent prediction model (i.e., application to machine learning of an intent prediction model). Additionally, the specification makes clear the general-purpose nature of the technological environment. Paragraphs [0117]—0120] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements or combinations of elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim.
Therefore, considered both individually and as an ordered combination, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional limitations are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea. Accordingly, the Examiner concludes that the claim fails to integrate the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
Under step 2B of the Alice/Mayo framework, it must finally be considered whether the claim includes any additional element or combination of elements that provide an inventive concept (i.e., whether the additional element or elements are sufficient to amount to significantly more than the abstract idea). As indicated above, considered both individually and as an ordered combination, the additional elements do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea
Further, the additional elements (recited above) simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Receiving or transmitting data over a network has been repeatedly considered well-understood, routine, and conventional activity by the Courts (See MPEP 2106.05(d)). Accordingly, the Examiner asserts that the additional elements, considered both individually, and as an ordered combination, do not provide an inventive concept, and the claim is ineligible for patent.
Independent Claims 11 and 20 are parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-10
Claims 2-10 merely embellish the abstract idea of recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts. While the claims do set forth additional limitations, the recitations are similar to the additional limitations in claim 1, as they do no more than generally link the use of the abstract idea to a particular technological environment. As such, they do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 1.
Regarding Claims 12-19
Claims 12-19 merely embellish the abstract idea of recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts. While the claims do set forth additional limitations, the recitations are similar to the additional limitations in claim 11, as they do no more than generally link the use of the abstract idea to a particular technological environment. As such, they do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 11.
Potentially Allowable Subject Matter
Claims 1-20 are found to be novel and non-obvious over the prior art, and would be allowable if amended to overcome the 35 USC 101 rejection above, without broadening their scope.
Response to Arguments
Applicant’s arguments with respect to the objection to the specification have been fully considered, but they are not persuasive. Applicant asserts that (emphasis added) “"MACHINE LEARNED MODEL FOR ITEM RECOMMENDATIONS FOLLOWING FAILED ATTEMPTS," is descriptive of the claimed invention as the claimed invention generally relates to training a machine-learning model to generate a score for whether to generate a recommendation to acquire a logged item when that logged item that was not fulfilled in another order.” Here, applicant admits that the invention “generally relates to” the subject matter in the title. As noted in the objection above, the title must be clearly indicative of the invention to which the claims are directed. “Generally relates to” is not equivalent of “clearly indicative”. While the claimed invention may involve and/or make use of a machine learning model for item recommendations following failed attempts, the claims are not directed to the model. Accordingly, the objection is maintained above, and the Examiner reserves the right to amend the title via Examiner’s Amendment in the event that the application is in condition for allowance, as per MPEP 606.01.
Applicant’s arguments with respect to the 35 USC 101 rejection have been fully considered, but they are not persuasive. Applicant argues that:
The claimed invention recites an improvement to the technical field of predicting user activity across sessions between a client device and an online system using a trained machine learning model, by persisting a signal from a session that has already ended and later combining that signal with a separate session's context, both when constructing the model's own training examples and when generating a live intent score. A machine learning model that predicts user intent from within a single session has access only to that session's own context, so when the signal relevant to a prediction originates in an earlier, already-ended session, that signal is lost once the session ends unless the system persists it and later retrieves it alongside new-session context. The claims solve this by using the same persist-then-retrieve structure in two places. First, the logging step stores the first-session signal, namely the unfulfilled item, in a database maintained by the online system, and the second session then retrieves that logged item once it receives a message that the item has become available, combining the persisted first-session signal with second-session context as an input to the intent prediction model. Second, the same cross-session pattern is recited as the specific mechanism for constructing the model's own training data: receiving a prior order from a previous session, identifying the unfulfilled item from that order, presenting the item to the same user in a subsequent session, and generating a label from whether that user interacted with the item when it was presented in that later session. The back-propagation step is then expressly tied to this newly generated label, rather than to an unexplained label applied to an undefined test interaction. Because the claims recite the particular steps by which a cross-session signal is captured, persisted, and later paired with new- session context for both model training and live inference, rather than merely stating that such information exists or is available, the claims apply the recited concept in a manner that goes beyond generally linking it to a particular technological environment.
The Examiner respectfully disagrees. Here, applicant mischaracterizes the result of a shopping session as a “signal”. Neither the claims, nor the specification make any mention of persisting signals, or “context”. No description of a machine learning model persisting a signal in some technical manner is described, Instead, what the claim language actually indicates is “persisted” (i.e., logged) is an item for which a requested order was not fulfilled during a first shopping session. The invention logs that an item was not fulfilled, so that a model can predict whether the user that requested the order would be interested in the unfulfilled item in a subsequent shopping session, and ultimately so that a recommendation including items can be presented to the user. Additionally, the specification makes clear that the problem sought to be solved is customer dissatisfaction due to unavailable items. See [0001]: “Out of stock items can be detrimental to customer satisfaction of online orders. Particularly in cases where an order has already been placed, and it is discovered later that an item in the order is not available”; “As such, there may be a high chance that the customer is still interested in purchasing the item, but conventional systems often do not include a mechanism to remind the customer about the item.” Therefore, the Examiner asserts that the technical improvement purported by applicant is not supported by the disclosure.
For the above reasons, applicant’s arguments are not persuasive and the rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kruck et al. (US 20230111745 A1) discloses systems and methods for generating models for recommending replacement items for unavailable in-store purchases, including training a model to recommend products to customers in response to unfulfilled orders.
Laserson et al. (US 20220092670 A1) discloses item affinity processing, including training a machine learning model to determine replacement items for out-of-stock items, and to modify the model for recommendations in subsequent transactions.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MICHAEL A MISIASZEK whose telephone number is (571)272-6961. The examiner can normally be reached Monday-Thursday. 8:00 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL MISIASZEK/Primary Examiner, Art Unit 3688
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