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 .
1. Applicant’s amendment filed 09/01/2026 is entered. Claims 1, 3-4, 6, 9-14, 17-18 and 20 are currently amended. Claims 5, 7, and 15 are canceled claims. Claims 1-4, 6, 8-14, 16-20 are pending.
Claim Rejections - 35 USC § 101
2. 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 1-4, 6, 8-14, 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-4, 6, 8-11 are to a process comprising a series of steps, clams 12-14, 16-19 to manufacture, and claim 20 to a system /apparatus, which are statutory (Step 1: Yes).
Step 2A Analysis:
1. (Currently amended) A method, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising:
(i) receiving an order at the computer system from a user device of a user, the order
identifying one or more items and a retailer from which the one or more items are to be obtained;
(ii) identifying an unavailable item in the order;
(iii) obtaining a set of candidate replacement items for the unavailable item;
(iv) generating a score for each candidate replacement item of the set by:
applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item;
(b) generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item;
(c) generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ;
(d) determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight;
(v) selecting one or more candidate replacement items based on the generated scores; and
(vi) sending the selected candidate replacement items to the user device of the user associated with the order, wherein sending the selected candidate replacement items causes the user device to display the selected candidate replacement items in an order based on the generated scores and an option for accepting one or more of the selected candidate replacement items to be added to the order in place of the unavailable item.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-4, 6, 8-14, 16-20 recite abstract idea.
The highlighted limitations comprising, “ (i) receiving an order from a user, the order identifying one or more items and a retailer from which the one or more items are to be obtained; (ii) identifying an unavailable item in the order; (iii)obtaining a set of candidate replacement items for the unavailable item; (iv (a)) generating a score for each candidate replacement item of the set by applying a replacement selection model to each pair of the unavailable item and a candidate replacement item, (v)selecting one or more candidate replacement items based on the generated scores; and (vi)sending the selected candidate replacement items to the user”, relate to a commercial activity of receiving order and if the ordered item is unavailable fulfilling it by selecting an available replacement item fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas. See MPEP 2106.04(a)(2), subsection II.
The highlighted limitations comprising “(ii) identifying an unavailable item in the order; (iv (a)) generating a score for each candidate replacement item of the set by applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users have previously approved a replacement of the unavailable item with the candidate replacement item; (b) generating, by a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; (c) generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ; (iv (d))determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative ; (vi) selecting one or more candidate replacement items based on the generated scores; ” fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, but for the “by a processor” language, the claim encompasses a person looking at data of an order for items and forming a simple judgement which items of the order are unavailable in the inventory, then selecting replacement items and using statistical mathematical models to generate replacement scores for replacement items . The embeddings relate to vectors which are mathematical concepts and can be compared manually and using prediction modules, which can be statistical models , can be used to predict scores based on collected data. These limitations just provide results without identifying specific claimed improvements to computer functionality. That is, other than reciting “by a processor” nothing in the claim elements precludes these steps from practically being performed in the mind. The mere nominal recitation of by a processor does not take the claim limitations out of the mental process grouping. Thus, the claim 1 recites a mental process.
The limitations comprising, “(iv a) applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item; (ivb) generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; (iv c) generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ; and (iv d) determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight; “encompasses mathematical concepts that can be performed manually using a pen and paper using mathematical approval models. See MPEP 2106.04(a)(2), subsection I. “ It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea), and C. Mathematical Calculations: A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Examples of mathematical calculations recited in a claim include:
i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018);
Thus, claim 1 with its dependent claims 2-4, 6, 8-11 recite abstract ideas. Since the other two independent claims 12 and 20 recite limitations similar to the limitations of claim 1, they are analyzed on the same basis as claim 1. Accordingly, claim 12 with its dependent claims 13-14, 16-19 and claim 20 recite abstract ideas.
Since each of the claims 1, 12, and 20 recite limitations falling under three separate groupings of abstract ideas, the Supreme Court (discussing Bilski v. Kappos, 561 U.S. 593 (2010)) has treated such claims in the same manner as claims reciting a single judicial exception. Accordingly, limitations considered under Certain Methods of Organizing Human Activity”, “Mental Processes”, and “Mathematical Concepts” are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES) for further analysis.
Thus, claims 1-4, 6, 8-14, 16-20 recite an abstract idea.
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-4, 6, 8-11, 12-14, 16-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional elements comprising a computer system executing the following steps:
(i) receiving an order at the computer system from a user device of a user, the order
identifying one or more items and a retailer from which the one or more items are to be obtained; (ii) identifying an unavailable item in the order; (iii) obtaining a set of candidate replacement items for the unavailable item; (iv) generating a score for each candidate replacement item of the set by:
a) applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item; (b) generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; (c) generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ; (d) determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight;
(v) selecting one or more candidate replacement items based on the generated scores; and (vi) sending the selected candidate replacement items to the user device of the user associated with the order, wherein sending the selected candidate replacement items causes the user device to display the selected candidate replacement items in an order based on the generated scores and an option for accepting one or more of the selected candidate replacement items to be added to the order in place of the unavailable item.
The limitations in steps” (i)receiving an order at the computer system from a user device …….; iii) obtaining a set of candidate replacement items for the unavailable item; and (vi) sending the selected candidate replacement items to the user device of the user associated with the order, wherein sending the selected candidate replacement items causes the user device to display the selected candidate replacement items in an order based on the generated scores and an option ……….”, are mere data gathering, transmitting and displaying/output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer. The computer is recited at a high level of generality and the computer is used as a tool to perform the generic computer functions of gathering, transmitting and output data. See MPEP 2106.05(f).
The limitations in steps (ii) identifying an unavailable item in the order; ( (iv) generating a score for each candidate replacement item of the set by: a) applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item; (b) generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; (c) generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding (d) determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight; and (v) selecting one or more candidate replacement items based on the generated scores;”; are recited as being performed by a computer, and the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations in step (iv)(b) recite using a set of layers which relates to using a neural network model to predict to generate embeddings from stored items attributes for both unavailable and replacement items , which merely amount to applying neural networks to implement an abstract ide. The recitation of using a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a set of layers” limits the identified judicial exceptions “generating embeddings,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, even when considered individually and in in combination, these additional elements in claim 1 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim 1 is directed to the judicial exception. (Step 2A: YES). Since the other two independent claims 12 and 20 recite limitations similar to the limitations of claim 1, they are analyzed on the same basis as directed to the judicial exception.
Dependent claims 2-4, 6, and 13-14 merely expand the scope of the limitations recited in claims 1 and 12 reciting mental process, insignificant extra-solution activity and mathematical concepts and as such do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Dependent claims 8 and 16 recite rankling the replacement items based on calculated scores and then making a selection having at least a threshold position in the ranking, which fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Thus, these limitations, even as recited being performed by a computer, amount to no more than mere instructions to apply the abstract idea using a generic computer. See MPEP 2106.05(f).
Limitations in claims 9-11 and 17-19 are directed to generic computer functions of transmitting data , displaying data, receiving feedback and receiving requests for refunds for an item, which are mere data gathering, transmitting and displaying/output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer. The computer is recited at a high level of generality and the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f).
Even when viewed individually and in combination, the additional elements in claims 1-4, 6, 8-14, 16-20 do not integrate the recited judicial exception into a practical application, because they do not add any meaningful limitations on practicing the abstract idea (Step 2A, Prong Two: NO), and the claims are directed to the judicial exception. (Step 2A: YES).
Thus, claims 1-4, 6, 8-14, 16-20 are directed to abstract idea.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-4, 6, 8-14, 16-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claims recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-4, 6, 8-14, 16-20 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The limitations reciting using a set of layers which relates to using a neural network model to predict to generate embeddings from stored items attributes for both unavailable and replacement items , merely amount to applying neural networks to implement an abstract idea. The recitation of using a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a set of layers” limits the identified judicial exceptions “generating embeddings,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Claims 1-4, 6, 8-14, 16-20 were found to recite additional elements of receiving/obtaining data, transmitting data and displaying data which were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/outputting/ displaying/ presenting data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ obtaining/transmitting/ outputting/displaying/presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here). See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2.
Even when considered individually and in combination, the additional elements in the pending claims 1-4, 6, 8-14, 16-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus, the pending claims 1-4, 6, 8-14, and 16-20 are patent ineligible.
1. (Currently amended) A method, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising:
receiving an order at the computer system from a user device of a user, the order
identifying one or more items and a retailer from which the one or more items are to be obtained;
identifying an unavailable item in the order;
obtaining a set of candidate replacement items for the unavailable item; generating a score for each candidate replacement item of the set by:
applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item;
generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item;
generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ;
determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight;
selecting one or more candidate replacement items based on the generated scores; and sending the selected candidate replacement items to the user device of the user associated with the order, wherein sending the selected candidate replacement items causes the user device to display the selected candidate replacement items in an order based on the generated scores and an option for accepting one or more of the selected candidate replacement items to be added to the order in place of the unavailable item.
3. Prior art discussion:
Reference independent claims 1, 12, and 20, the prior art of record, alone or combined, neither teaches nor renders obvious at least the limitations comprising, as a whole, “ generating a score for each candidate replacement item of the set by applying an approval prediction sub-model to the unavailable item and the candidate replacement item to produce an approval score based on interaction counts comprising a number of times users of the computer system have previously approved a replacement of the unavailable item with the candidate replacement item; generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding ; determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight; in combination with the rest of the limitations recited in the independent claims 1, 12, and 20. Claims 1-4, 6, 8-11 depend from claim 1 and claims 13-14, 16-19 depend from claim 12.
4. Allowability: If the independent claims 1, 12, and 20 are amended to overcome the 35 USC 101 rejection, the application can be placed in condition for allowance. All future amendments would be subject to reconsideration and search.
5. The prior art pertinent to the claimed invention but not considered:
(i) Balsubramanian et al. (US 20240289855 A1 published 08/29/2024 and owned by the same entity as the Application; see para 0100 and Fig.3 and cited in the Non-Final Rejection mailed 06/01/2026] describes a machine learning model (or algorithm) generating replacement score for the candidate replacement item based on the approval score from the linear model 630 and/or the similarity score from the linear model 630 including applying . Weights to the approval score and/or the similarity score by the machine learning model.
(i) Pawar [US 20220114640 A1 cited in the Non-Final Rejection mailed 11/13/2025 owned by the same Applicant Maplebear, Inc. ] describes [see paras 0005 and 0008 and abstract] that an online concierge system selects an alternative item having a maximum replacement score as the replacement item for the specific item, wherein the selected alternative item is ranked based on replacement score having at least a threshold position (e.g., a maximum position) in the ranking as the replacement item for the specific item. The replacement score is generated by a machine-learned replacement model which is configured to receive inputs characteristics about the specific item, such as brand, type, price, category, availability of the specific item, etc., and characteristics about an alternative/replacement item , such as brand, type, price, category, availability of the alternative item, etc.) a number of times the alternative item was previously selected to replace the specific item, and a distance between the specific item and the alternative item in the item graph and the replacement model outputs a replacement score that is a probability of the alternative item being selected to replace the specific item and [see para 0048] teaches that order fulfillment engine 206 sends a message and/or instruction to a customer based on the probability predicted by the machine-learned item availability model 216. Pawar fails to teach specifically “ the replacement selection model determining a score for the candidate replacement item based on a weighted sum of predicted probabilities for each of a set of events that includes one or more negative events, with a negative weight applied to predicted probabilities of negative events”.
(iii) Bell et al. cited in the Non-Final Rejection mailed 11/13/2025 [US 11, 222, 374 B1; see col.4, lines 47-52] describes an item replacement engine 218 assigning a weight to each of the factors and generate a score for each item based in part on one or more factors and rank the similar items based at least in part on the score and select a replacement item according to the ranking.
(iv) Sarma cited in the Non-Final Rejection mailed 11/13/2025 [US Patent# 8,645,221 B1; see col.6, lines 19-20, describes generating a replacement score based upon the number of times an item 136 has been replaced.
(v) Kruck et al. cited in the Non-Final Rejection mailed 11/13/2025 [US 20230111745 A1; see Abstract] describes a recommendation system identifies a recommended replacement item using a trained model for an ordered item but is unavailable.
Foreign reference:
(vi) CN 113947418 A cited in the Non-Final Rejection mailed 11/13/2025, see Abstract and page 14, describes a method for obtaining user’s feedback for a played content item information of the played content item including obtaining the prediction probability of the user generating various feedback behavior. When the prediction probability meets the target probability condition, sending the target page resource to the terminal, so that the target page resource trigger terminal returns the collected feedback information, wherein , the target probability condition is that the weighted sum value of the predicted probability of the at least one feedback behavior is greater than the target probability threshold.
NPL references:
(vii) Damian et al.; “ ProVe - Self-supervised pipeline for automated product replacement and cold-starting based on neural language models”; https://arxiv.org/abs/2006.14994 published Friday 26 June 2020; retrieved from IP. Com on 11102025 and cited in the Non-Final Rejection mailed 11/13/2025, describes propose a solution for retail industry to deal with out-of-stock items for recommending the most suitable replacements for products that are out-of-stock.
Response to Arguments
6. Applicant's arguments filed 09/01/2026, see remarks pages 1-4 have been fully considered but they are not persuasive. Applicant’s arguments are directed to Step 2A, Prong Two analysis stating that the amended claim including new limitations, “ generating, by a set of layers of a negative event prediction sub-model, a first embedding from item attributes stored in association with the unavailable item and a second embedding from item attributes stored in association with the candidate replacement item; generating, by the negative event prediction sub-model, a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of the first embedding and the second embedding”, amount to a technical improvement. Examiner disagrees, because these limitations do not amount to any technical improvement to computer functioning, hardware or software, or specific data structure, and model architecture. Instead, these limitations along with other limitations in claim 1 recite and directed to an abstract mathematical operation in the limitation, "determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight.", MPEP § 2106.04(a)(2) identifies mathematical concepts such as mathematical calculations and formulas as abstract ideas, and the use of a weighted sum is the type of calculation that fits that grouping. The claim also recites mental-process-type evaluation and selection activity, including "obtaining a set of candidate replacement items," "generating a score for each candidate replacement item," and "selecting one or more candidate replacement items based on the generated scores." Under MPEP § 2106.04(a)(2)(III), evaluations and judgments are mental processes, and the claim language is recited at a high level of generality comprising making a selection among candidates based on scores without a specific technical mechanism. The limitations reciting using a set of layers which relates to using a neural network model to predict to generate embeddings from stored items attributes for both unavailable and replacement items , merely amount to applying neural networks to implement an abstract idea. The recitation of using a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a set of layers” limits the identified judicial exceptions “generating embeddings,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Applicant’s arguments on page 3 that the limitations of claim 1 do not fall within the “Mental Process”, are not persuasive because, as indicated above, the limitations of obtaining a set of candidate replacement items," "generating a score for each candidate replacement item," and "selecting one or more candidate replacement items based on the generated scores." Recite a mental process. Examiner does not agree with the Applicant’s arguments that claim 1 limitations do not recite mathematical concept because “determining a score for the candidate replacement item based on a weighted sum of the approval score weighted by a weight and the predicted probability that a negative event will result for each of a set of events that includes one or more negative events weighted by an additional weight, wherein the additional weight is a negative weight.", MPEP § 2106.04(a)(2) identifies mathematical concepts such as mathematical calculations and formulas as abstract ideas, and the use of a weighted sum is the type of calculation that fits that grouping. Examine also does not agree with the Applicant’s arguments that the claim limitations do not recite certain methods of organizing activity because "receiving an order," "identifying an unavailable item," "generating a score," "selecting one or more candidate replacement items," and "sending the selected candidate replacement items." Do relate to a commercial activity.
In view of the foregoing, rejection of pending claims under 35 USC 101 is sustainable and maintained.
Conclusion
7. 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 YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa 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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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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