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 02/05/2026 is entered. Claims 1, 12-13, and 20 are currently amended. Claims 7 and 15 are previously withdrawn. Claims 1-6, 8-14, and 16-20 are currently pending for examination on merits.
2. Applicant requested interview :
An applicant initiated telephone interview was conducted on 02/04/2026. The applicant’s agenda included most of the current amendments to the independent claims 1, 12 and 20. The interview summary is reproduced below:
“ Issues Discussed:
35 U.S.C. 101
Applicant's interview agenda is attached for ready reference. Examiner after reviewing the suggested amendments indicated that they relate to mathematical concepts and do not integrate the abstract idea in to a practical application because they do not add meaningful limits on practicing the abstract idea. No agreement reached. Any amendments when formally submitted will be fully reconsidered by the examiner. Examiner also suggested reference: Reference Gusev et al. {US 11, 233, 761 B1] see col.5, lines 20-43, which discloses that using machine learning models in generating embedding is well-known in the art”.
Claim Rejections - 35 USC § 101
3. 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 1-6, 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 1-6, 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. (Original) 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 the rate at which users have historically 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;
(v) determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result
(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 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 1-6, 8-14, 16-20 recite abstract idea.
(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 the rate at which users have historically 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;
(v) determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result
(vi) selecting one or more candidate replacement items based on the generated scores; and
(vi) sending the selected candidate replacement items to the user
The highlighted limitations comprising, “ 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; identifying an unavailable item in the order; obtaining a set of candidate replacement items for the unavailable item; selecting one or more candidate replacement items and sending the selected candidate replacement items to the user”, relate to a commercial activity of receiving order and fulfilling it 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 “identifying an unavailable item in the order; obtaining a set of candidate replacement items for the unavailable item; selecting one or more candidate replacement items; 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 the rate at which users have historically 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; determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result”, under their broadest reasonable interpretation” 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 . 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 the rate at which users have historically 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; determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result”, 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 206, 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 1-6, 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 1-6, 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 the rate at which users have historically 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;
(v) determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result
(vi) selecting one or more candidate replacement items based on the generated scores; and
(vii) sending the selected candidate replacement items to the user
The limitations in 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; 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 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.”, 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).
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 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 the rate at which users have historically 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; (v) determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result wherein a negative weight is applied to the predicted probabilities that negative events will result; and (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 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 2-6, and 14-15 merely expand the scope of the limitations recited in claims 1 and 12 reciting mental process 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 1-6, 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 1-6, 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 1-6, 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 1-6, 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 1-6, 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 in combination, these 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, the pending claims 1-6, 8-14, and 16-20 are patent ineligible.
4. 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 the rate at which users have historically 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, and ;determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probability that a negative event will result probabilities for each of a set of events that includes one or more negative events, with wherein a negative weight is applied to the predicted probabilities that negative events will result, in combination with the rest of the limitations recited in the independent claims 1, 12, and 20. Claims 1-6, 8-11 depend from claim 1 and claims 13-14, 16-19 depend from claim 12.
5. 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 amendments would be subject to further reconsideration and search.
6. The prior art pertinent to the claimed invention but not considered:
(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”.
(ii) 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.
(iii) 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.
(iv) 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:
(v) 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:
(vi) 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 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.
(vii) 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, 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
7. Applicant's arguments filed 02/05/2026 against rejection of pending claims 1-6, 8-14, 16-20 , see pages 20-23, have been fully considered but they are not persuasive.
Examiner respectfully disagrees with the Applicant’s arguments and claims that the amended claim 1,a s an example, “ addresses the technical problem that conventional replacement suggestion models in computer-implemented order fulfillment systems focus solely on maximizing the probability of customer approval, without accounting for the likelihood of negative events such as refund requests or negative feedback. …….”, and the currently amended limitations, “" 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 ... and a negative event prediction sub-model ...; determining a score for the candidate replacement item based on a weighted sum of the approval score and predicted probabilities ... with a negative weight applied to predicted probabilities of negative events; and selecting one or more candidate replacement items based on the generated scores; “ reflect technological improvement”, because when analyzed per Step 2A, Prong one, and Prong Two and Step 2B, the limitations when viewed alone, they do recite an abstract idea, and even when viewed in combination, do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES) and 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).
Step 2A, Prong One 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. The limitations of claim do “set forth” and “described” limitations fall within the “Certain Methods of Organizing Human Activity”, “ Mathematical concepts”, and “Mental Processes”, as already discussed and analyzed in detail above in paragraph 3 . Applicant has not provided specific arguments as why the claim 1 limitations , as analyzed by the examiner, do not recite abstract idea.
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). The additional elements recited in claim 1 were analyzed individually and in combination in paragraph 3 above as recited at a high level of generality merely amounting to data gathering, transmitting and output which are insignificant extra-solution activity, See MPEP 2106.05(g) (“whether the limitation is significant”), wherein the computer is used as a tool to perform generic computer functions . See MPEP 2106.05(f). The other additional elements recite performing mental processes using mathematical concepts including “ identifying an unavailable item in the order, generating a score a score for each candidate replacement item of the set by applying a trained replacement selection model, determining a score for the candidate replacement item based on a weighted sum of the approval score and the predicted probabilities and selecting one or more candidate replacement items based on the generated scores, wherein the computer is used to perform the 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 in combination, the 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). Applicant has not provided arguments to these Examiner’s analysis.
Step 2B 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 additional elements, as analyzed in Step 2A analysis above, the additional elements. are at insignificant extra-solution activity and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Applicant’s claims that using replacement prediction models to account for approval scores for replacement items based upon the historical data whether the replacement item is positively or negatively weighted amount to business benefits and merely using mathematical models such as trained prediction models do not add to improved computer functioning.
In view of the foregoing, rejection of pending claims 1-6, 8-14, and 16-20 under 35 USC 101 is sustainable and maintained.
Conclusion
8. Final Rejection:
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688