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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/21/2026 has been entered.
Status of Claims
Claims 1-5 and 7-23 remain pending, and are rejected.
Claim 6 has been cancelled.
Response to Arguments
Applicant’s arguments filed on 1/21/2026 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 1/21/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 17-18 of the Applicant’s Remarks, arguments are made that the claims are not directed to identifying replacement products for an unavailable item in an order, but instead identifies a set of replacement items for automatically replacing an original set of items in the database, where each item in the original set does not satisfy a constraint related to an attribute, while each item in the set of replacement items satisfies the constraint related to the attribute. Further arguments are made that the claims integrate the judicial exception into a practical application of a computer system that automatically replaces an object in a database (cart with the original set of items) using an output of a language model and outputs of trained machine learning models, where the new object is generated such that each item of the new object satisfies a predetermined constraint for the attribute. The Applicant argues that the claim as a whole recites specific operations and functionalities integrated into the computer system for automatically generating a final output that satisfies an attribute constraint for each component of the final output given that each component of an initial output did not satisfy the attribute constraint, resulting in a specific improvement in functioning of the computer system.
Examiner respectfully disagrees. The Applicant defined invention of identifying a set of replacement items for automatically replacing an original set of items in the database, where each item in the original set does not satisfy a constraint related to an attribute, while each item in the set of replacement items satisfies the constraint related to the attribute still describes an abstract idea of a certain method of organizing human activity. The activity of replacing an item based on an attribute of the item and satisfying an arbitrary constraint is not related to any computer functionality of technical field, but is mere organizing information and finding replacement items. While the claims recite a database, a language model, and trained machine learning models, these elements are recited with a very high level of generality, and does not change or improve how a computer stores and retrieves data or the underlying technology of the machine learning models. The recitation of the database merely provides a general link to a computing environment, and the language and machine learning models are merely applied to the abstract idea to provide an output of information given an input. How these models function are not recited in the claims or in the specification. As such, the claims are still directed to an abstract idea, and do not provide any change or improvement to any computer technology.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained 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 1-5 and 7-23 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-5 and 7-13 are directed to a method, which is a process. Claims 14-19 are directed to a computer program product, which is an article of manufacture. Claims 20-23 are directed to a computer system, which is an apparatus. Therefore, claims 1-5 and 7-23 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 20 as representative, claim 20 sets forth the following limitations (emphasized in bold) reciting the abstract idea of identifying a replacement product for an unavailable item in an order:
accessing an order of a user of an online system, the order comprising an original set of items;
accessing a replacement model integrated into the computer system, wherein the replacement model is a machine-learning model trained to identify a set of candidate replacement items for an item from the original set of items;
applying the replacement computer model to identify, based at least in part on a replacement score for each item of a plurality of items, the set of candidate replacement items from the plurality of items;
selecting a subset of candidate replacement items from the identified set of candidate replacement items, based at least in part on a constraint that each candidate replacement item in the subset of candidate replacement items is associated with a value of an attribute that is lower than a value of the attribute of the item from the original set of items, wherein selecting the subset of candidate replacement items comprises:
generating a prompt for input into a language model integrated into the computer system, the prompt including information about a type of each candidate replacement item from the identified set of candidate replacement items, the value of the attribute of each candidate replacement item, and a size of each candidate replacement item;
requesting the language model to generate, based on the prompt, a response including the subset of candidate replacement items, the language model including a set of decoders, each decoder from the set of decoders performing one or more operations to a corresponding portion of the prompt to generate an output token of a sequence of output tokens, the prompt including a sequence of input tokens, and the sequence of output tokens forming the response generated by the language model;
accessing a conversion model integrated into the computer system, wherein the conversion model is a machine-learning model trained to identify, based on a predicted likelihood of conversion by the user for each candidate replacement item in the subset of candidate replacement items, a candidate replacement item from the subset of candidate replacement items;
applying the conversion model to identify, based at least in part on a conversion score for each candidate replacement item in the subset of candidate replacement items, a set of replacement items;
automatically updating a database of the online system that stores the original set of items in the order with the set of replacement items;
causing a device associated with a user of the online system to display a user interface with a cart including the set of replacement items.
The recited limitations above set forth the process for identifying a replacement product for an unavailable item in an order. 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 finding replacement items that are similar to an original item in an order that are likely to be purchased by the user (see specification [0001-0002] disclosing the problem of suggesting alternative items and automating manual human input), which is a sales and marketing activity.
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 20 recites additional elements, such as:
a processor;
a non-transitory computer-readable storage medium having instructions, that when executed by the processor, cause the computer system to perform steps;
an online system;
a replacement model integrated into the computer system, wherein the replacement model is a machine-learning model;
the language model including a set of decoders, each decoder from the set of decoders performing one or more operations to a corresponding portion of the prompt to generate an output token of a sequence of output tokens, the prompt including a sequence of input tokens, and the sequence of output tokens forming the response generated by the language model;
accessing a conversion model integrated into the computer system, wherein the conversion model is a machine-learning model;
a database of the online system;
causing a device of a user of the online system to display a user interface;
Taken individually and as a whole, representative claim 20 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.
Although the claims recite a processor and a non-transitory computer-readable storage medium, these elements are recited with a very high level of generality. Specification paragraph [0100] merely discloses the processor comprising one or more processors or processing units. Specification paragraph [0101] merely discloses the non-transitory computer-readable medium as including any embodiment of a computer program product or other data combination. As such, it is evident that these are not any particular devices or otherwise integral to the claims other than to provide a general link to a computing environment, such that the abstract idea may be implemented on a computing device. The online system is described in specification paragraph [0042] and in Figure 2, which describes various modules to perform the steps of the abstract idea. It is clear that the online system is just software to perform the calculations and steps of the abstract idea, and does not represent anything more than the abstract idea being performed over a network. The machine-learning models are described in paragraph [0102], which provides a very general description of machine-learning models without any particular description as to how they function at a technical level. The user device is disclosed in paragraph [0011], which discloses that it may be any personal or mobile computing device, such as a smartphone, laptop computer, desktop computer, etc. It is clear that the additional elements are generally applied to the abstract idea, and merely provide a general link to a computing environment. The decoders are also recited with a very high level of generality, the claims merely reciting performing one or more operations to a corresponding portion of the prompt and including input and output tokens. The specification also does not disclose any further detail except reiterating the claim limitation in paragraph [0035]. The machine learning models are also not disclosed with any particularity, the specification merely disclosing the machine-learning models are language models configured to perform one or more natural language processing tasks (specification: [0030]). Paragraph [0059] also lists that the machine-learning model may include regression models, support vector machines, naïve bayes, etc. It is evident that these models are any generic machine-learning models that are merely applied to the abstract idea to perform calculations of the abstract idea, and only provide a general link to a computing environment.
In view of the above, under Step 2A (prong 2), claim 20 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 20, taken individually or as a whole, the additional elements of claim 20 do not provide an inventive concepts (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in representative claim 20 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing 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 20 do not add anything further than when they are considered individually.
In view of the above, representative claim 20 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 20 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 20.
Regarding Claim 14 (computer program product): Claim 14 recites at least substantially similar concepts and elements as recited in claim 20 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 14 is rejected under at least similar rationale as provided above regarding claim 20.
Regarding Claim 21 (method): Claim 21 recites at least substantially similar concepts and elements as recited in claim 20 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 21 is rejected under at least similar rationale as provided above regarding claim 20.
Dependent claims 2-5, 7-13, 15-19, and 22-23 recite further complexity to the judicial exception (abstract idea) of claim 20, such as by further defining the algorithm for identifying a replacement product for an unavailable item in an order. Thus, each of claims 2-5, 7-13, 15-19, and 22-23 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 2-5, 7-13, 15-19, and 22-23 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-5, 7-13, 15-19, and 22-23 rely on at least similar elements as recited in claim 20. Further additional elements are also acknowledged; however, the additional elements of claims 2-5, 7-13, 15-19, and 22-23 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 2-5, 7-13, 15-19, and 22-23 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-5, 7-13, 15-19, and 22-23 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 20. Thus, dependent claims 2-5, 7-13, 15-19, and 22-23 do not add “significantly more” to the abstract idea.
Subject Matter Free of Prior Art
The following is a restatement of reasons for indicating subject matter free of the prior art in the previous Office Action mailed on 11/19/2025.
Claims 1-5 and 7-23 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, as set forth above.
Claims 1-5 and 7-23 are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 recites a method comprising:
requesting the language model to generate, based on the prompt, a response including the subset of candidate replacement items, the language model including a set of decoders, each decoder from the set of decoders performing one or more operations to a corresponding portion of the prompt to generate a corresponding output token of a sequence of output tokens, the prompt including a sequence of input tokens, and the sequence of output tokens forming the response generated by the language model;
The closes prior art was found to be as follows:
Cho (US 20210233143 A1) discloses [0081] – “The smart substitution computing device 102 can rank the substitute items based on the overall substitution score. In some examples, the relevance score and/or the preference score are calculated using machine learning models (e.g., the customer understanding model 512 and/or the substitution identification engine 302). As such, it can be difficult to change the results of the relevance score and/or the preference score during normal operation of the smart substitution computing device. The relevance score and/or the preference score can be optimized during offline re-training or optimization of the customer understanding model 512 and/or the substitution identification engine 302. The overall substitution score, however, can be optimized and/or modified quickly by changing and/or modifying the score parameter, λ. Thus, by changing the score parameter, λ, the ranking of substitute items produced by the substitution identification engine 302 can be changed and/or optimized quickly during normal operation if so desired.”.
Smith (US 20180182025 A1) discloses [0057] – “the displayed budget information 602 may show the price of a product and indicate whether the product falls within the budget of a user. In some embodiments, when in save money mode, computing device 120 may display alternative items 604, providing cheaper alternatives to a product identified in the field of view of computing device”.
Kuhn (US 20230245148 A1) discloses [0053] – “The demand transference engine 408 can determine such demand transference data or demand transference coefficients. In one example, the demand transference engine 408 can be connected to a substitution engine (not shown). Some retailers may have an existing substitution engine that determines customer preferences for substitute items that can be substituted or recommended to customers when an item becomes unavailable for purchase. The demand transference engine 408 may obtain substitution data from such substitution engines to determine demand transference data”.
NPL Reference U (see PTO-892 Reference U mailed on 7/3/2025) discloses modeling preferences of users with availability constraints such that the recommender takes into account the availability of items to the user.
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:
requesting the language model to generate, based on the prompt, a response including the subset of candidate replacement items, the language model including a set of decoders, each decoder from the set of decoders performing one or more operations to a corresponding portion of the prompt to generate a corresponding output token of a sequence of output tokens, the prompt including a sequence of input tokens, and the sequence of output tokens forming the response generated by the language model;
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 1-5 and 7-23 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
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: 7:30 - 5:00.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 3/13/2026