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 .
Continued Examination Under 37 CFR 1.114
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/04/2026 has been entered.
Claim 5 is currently canceled and claims 7 and 15 are previously withdrawn. Currently claims 1-4, 6, 8-14, 16-20 are pending for examination on merits. Claims 1-4, 6, 12-14, and 20 are amended. Claims 1, 12, and 20 are independent claims. Claims 2-4, 6, 8-11 depend from claim 1, claims 13-14, 16-19 depend from claim 12.
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:
Claim 1 recites:
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 a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item;
(v) 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; and
(vii) 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) 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, (vi)selecting one or more candidate replacement items based on the generated scores; and (vii)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; (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 a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v)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 from the from the available inventory, then selecting replacement items and using 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, “ 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, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v)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 ; “encompasses mathematical concepts that can be performed mentally 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 limitations:
Generic computer processor 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 applying a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item;
(v) 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; and
(vii) 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 (vii) 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; (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 a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v) 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;”; 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).
Accordingly, even when considered individually and in in combination, these additional elements 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, amounts 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.
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.
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 a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including a trained approval prediction sub-model trained 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, and a negative event prediction sub-model trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; and 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; 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 ] 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.
(viii) K. Srinivasan, P. G. Malu and G. Moakley, "Automatic multi-business transactions," in IEEE Internet Computing, vol. 7, no. 3, pp. 66-73, May-June 2003, retrieved from IP. Com on 03132026, and cited in the Final Rejection mailed 03/17/2026, see page 67 describes that in online purchasing clients place holds for an item, with the vendor indicating how long the hold can stay before the sale and permit multiple clients to place holds on the same item in the fields of airline, hotel, and rental car reservations. If one item becomes unavailable, the client can replace it with another item without losing any holds on other reservations.
Response to Arguments
6. Applicant's arguments filed 02/05/2026 against rejection of pending claims 1-4, 6, 8-14, 16-20 , see pages 20-21, have been fully considered but they are not persuasive.
Examiner respectfully disagrees with the Applicant’s arguments on pages 20-21, “ The present claims do not recite a judicial exception under Step 2A Prong 1 of the revised Alice/Mayo eligibility test. …….The specification describes a technical problem. In particular, limited interaction data for some items lead to data sparsity that reduces accuracy of making predictions about user reactions to replacement items. See Specification as Filed at 76-77. The reduced accuracy also means that the user interfaces of customer devices may not be configured to show the best replacements available at the time of an order because they will instead be populated with incorrectly selected replacement suggestions. The claimed method solves these technical problems. Claim 1 is amended herein to clarify how a specific, dual-submodel machine-learning architecture solves the technical problem of producing recommendations based only on sparse and noisy interaction history by separating an approval prediction sub-model that uses interaction history as inputs from a negative event prediction sub-model that computes a probability based on item attributes (represented by embeddings), and then combines outputs from these separate models with opposite-signed weights. One model could not individually produce the same prediction because the sub-models each deal in different data sets and modeling techniques. Therefore, the claims are not directed to judicial exception.” because Step 2A, Prong One 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. The claim 1, considered as an exemplary claim, does “set forth” and “describe” abstract ideas as explained in MPEP 2106.04 . The 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) 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, (vi)selecting one or more candidate replacement items based on the generated scores; and (vii)sending the selected candidate replacement items to the user”, do 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 limitations comprising “(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 a replacement selection model to each pair of the unavailable item and a candidate replacement item, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v) 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; ” do 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 from the from the available inventory, then selecting replacement items and using 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, “ 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, the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v) 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 ; “encompasses mathematical concepts that can be performed mentally 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.
Examiner respectfully disagrees with the Applicant’s arguments on page 21, “ Moreover, even if the present claims were directed to a judicial exception, that judicial exception would be integrated into a practical application under Step 2A, Prong 2 of the revised Alice/Mayo eligibility test. The claimed method uses the dual-submodel architecture described above to generate scores that account for predicted negative events with opposite-signed weighting. Additionally, the claimed method requires the client device to display replacement items in an order based on those scores to drive real-time decisions in the fulfillment workflow. This improves the fulfillment interface and reduces negative events and latency in human-in-the-loop selection. These limitations tie the model outputs to concrete interface controls and operational changes on the user device, thus integrating any alleged abstraction into a specific practical application that improves order-fulfillment performance. “, because these limitations encompass mathematical concepts that can be performed mentally 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);
Step 2A, Prong Two 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).
Claim 1 recites the additional elements comprising a generic computer implementing the 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 (vii) sending the selected candidate replacement items to the user device of the user associated with the order, …… ……….”, are mere data gathering, transmitting and displaying/output recited at a high level of generality, and thus are insignificant extra-solution activity, and the computer is recited at a high level of generality used as a tool to perform the generic computer functions of gathering, transmitting and displaying/output data. See MPEP 2106.05(f). Claim 1 also recites the additional limitations comprising a generic computer implementing the steps of (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 …….., the replacement selection model including: an approval prediction sub-model that is trained 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; and a negative event prediction sub-model that is trained to produce a predicted probability that a negative event will result from replacing the unavailable item with the candidate replacement item based on a comparison of an embedding representing attributes of the unavailable item and an embedding representing attributes of the candidate replacement item; (v) determining a score for the candidate replacement item based on a weighted sum of the approval score …… ; (vi) 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).
Accordingly, even when considered individually and in in combination, these additional elements 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.
In view of the foregoing, Applicant’s arguments on pages 20-21 are not found persuasive, as the limitations do not identify a specific claimed improvement to computer functionality or a specific technical improvement or an unconventional network architecture, specialized hardware. Instead, the claims are directed to using generic computer components to perform a business activity of determining right replacement items for unavailable items.
Applicant has not submitted arguments further for Step 2B analysis or separate arguments for any other claims. Accordingly, the claims are directed to an abstract idea and do not amount to a practical application. Since, as per Step 2B analysis, see paragraph 2 above for details, the additional elements 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, rejection of pending claims 1-4, 6, 8-14, 16-20 under 35 USC 101 is sustainable and maintained.
Conclusion
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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