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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/12/2026 has been entered.
Independent claims 1, 11, and 20 are amended. Claims 2-3, and 12-13 are canceled. Claims 1, 4-11, 14-20 are pending for examination. Claims 4-10 depend from claim 1, and claims 14-19 depend from claim11.
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, 4-11, 14-20 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, 4-10 are to a process comprising a series of steps, clams 11, 14-19 are to manufacture, and claim 20 to a system, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1. A method, performed by a computer system comprising a processor and a computer-readable medium, comprising:
(i)receiving, via a
(ii)accessing item data for the initial item from an item database of the online system;
(iii)identifying a set of candidate items based on the accessed item data;
(iv)generating a replacement score for each of the candidate items by applying a replacement prediction model to the accessed item data and item data for each candidate item of the set of candidate items, wherein replacement score for a candidate item represents a likelihood that the user will replace the initial item with the candidate item in the item list, and wherein the replacement prediction model is a machine-learning model that is trained to predict a likelihood that a user will replace one item with another item in an item list based on item data for both items;
(v) wherein the replacement prediction model is trained by:
(a)obtaining historical order data describing items ordered by users of the online system.
(b)generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item,
(c)labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item, and
(d) training the replacement prediction model with the set of training examples to predict the replacement score for a candidate item as a substitute for an item to be replaced;
(vi)ranking the set of candidate items based on the generated replacement scores;
(vii))selecting a proposed replacement item for the initial item based on the ranking;
(viii))transmitting the proposed replacement item to the client device, wherein the transmitting causes the client device to display, via the
(ix)receiving, via the
(x modifying the item list by removing the initial item and adding the proposed replacement 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, 4-11, 14-20 recite abstract idea.
The highlighted limitations comprising, “identifying a set of candidate items based on the accessed item data; generating a replacement score for each of the candidate items by applying a replacement prediction model to the accessed item data and item data for each candidate item of the set of candidate items, wherein replacement score for a candidate item represents a likelihood that the user will replace the initial item with the candidate item in the item list; generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item, labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item; ranking the set of candidate items based on the generated replacement scores; selecting a proposed replacement item for the initial item based on the ranking; modifying the item list by removing the initial item and adding the proposed replacement item”, 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, a human operator can analyze accessed item data to identify items based on predetermined basis [the claim , as recited is broad, and does not provide the basis for identification but as per the Specification it could be user’s profile or historical data], use a known replacement prediction model which could be a mathematical model to determine a replacement item for an item based on the determined similarity by the model, calculate scores based on the results obtained from the model , generating training examples from the available data, labeling training examples [See Specification para 0058 which describes that such labeling can be done manually including marking training examples as converted that is user completed the order or unconverted that is the user did not complete the order can be done manually], rank them as per the scores and select the replacement items as per the ranking and making adjustments to the list by removing an initial item and adding a replacement item. The mere nominal recitation of by a computer does not take the claim limitations out of the mental process grouping. Thus, the claim 1 and therefore its dependent claims 4-10 recite a mental process.
Since the limitations of the other two independent claims 11, and 20 are similar to claim 1, they are analyzed on the same basis reciting a mental process. Accordingly, all pending claims 1, 4--10, claims 11 and its dependent claims 14-19, and claim 20 recite “Mental Process” 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 1,4-11, 14-20-20 The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer components comprising a generic computer implementing the steps of:
(i)receiving, via a user interface, interaction data from a client device describing an interaction of a user with an online system through the client device, wherein the interaction comprises the user adding an initial item to an item list corresponding to the user;
(ii)accessing item data for the initial item from an item database of the online system;
(iii)identifying a set of candidate items based on the accessed item data;
(iv)generating a replacement score for each of the candidate items by applying a replacement prediction model to the accessed item data and item data for each candidate item of the set of candidate items, wherein replacement score for a candidate item represents a likelihood that the user will replace the initial item with the candidate item in the item list, and wherein the replacement prediction model is a machine-learning model that is trained to predict a likelihood that a user will replace one item with another item in an item list based on item data for both items;
(v) wherein the replacement prediction model is trained by:
(a)obtaining historical order data describing items ordered by users of the online system.
(b)generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item,
(c)labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item, and
(d) training the replacement prediction model with the set of training examples to predict the replacement score for a candidate item as a substitute for an item to be replaced;
(vi)ranking the set of candidate items based on the generated replacement scores;
(vii))selecting a proposed replacement item for the initial item based on the ranking;
(viii))transmitting the proposed replacement item to the client device, wherein the transmitting causes the client device to display, via the user interface, the proposed replacement item to the user with an option to replace the initial item with the proposed replacement item;
(ix)receiving, via the user interface, user input selecting the option to replace the initial item; and
(x modifying the item list by removing the initial item and adding the proposed replacement item.
The limitations in steps ” (i) receiving, via a user interface, interaction data from a client device describing an interaction of a user with an online system through the client device, wherein the interaction comprises the user adding an initial item to an item list corresponding to the user; (ii) accessing item data for the initial item from an item database of the online system; and transmitting the proposed replacement item to the client device, wherein the transmitting causes the client device to display the proposed replacement item to the user, ” , “(v(a) obtaining historical order data describing items ordered by users of the online system.”, “(viii))transmitting the proposed replacement item to the client device, wherein the transmitting causes the client device to display, via the user interface, the proposed replacement item to the user with an option to replace the initial item with the proposed replacement item; and “ix)receiving, via the user interface, user input selecting the option to replace the initial item;) “ are mere data gathering and outputting/ displaying/transmitting recited at a high level of generality, and thus are insignificant extra/post-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 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/displaying/transmitting. See MPEP 2106.05. In these limitations the computer is used as a tool to perform the generic computer functions of receiving data, transmitting data, accessing/gathering data and displaying data. See MPEP 2106.05(f).
Further, limitations in steps “(iv) generating a replacement score for each of the candidate items by applying a replacement prediction model to the accessed item data and item data for each candidate item of the set of candidate items, wherein replacement score for a candidate item represents a likelihood that the user will replace the initial item with the candidate item in the item list, “(v)(b) (b)generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item, (v) (c)labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item”, (vi)ranking the set of candidate items based on the generated replacement scores; (vii))selecting a proposed replacement item for the initial item based on the ranking; (x modifying the item list by removing the initial item and adding the proposed replacement item. “; are recited as being performed by a computer. The computer is recited at a high level of generality. In these limitations, 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) wherein the replacement prediction model is a machine-learning model that is trained to predict a likelihood that a user will replace one item with another item in an item list based on item data for both items”, are s performed by a computer. These limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer, as analyzed in Step 2A, Prong one. The trained machine learning model is used to generally apply the abstract idea without placing any limits on how the trained ML model functions. Rather, these limitations only recite the outcome of “generating a replacement score for each of the candidate items” and do not include any details about how the “generating ” is accomplished. See MPEP 2106.05(f). The recitation of “using a trained ML model”” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ML model” limits the identified judicial exceptions “generating a replacement score using a trained ML model,” 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).
The limitations, in step “(v)(d) training the replacement prediction model with the set of training examples to predict the replacement score for a candidate item as a substitute for an item to be replaced;”, as recited are performed by a computer is a generic computer function of training a machine learning model as the limitations do not include details of using an algorithm with the collected data extending beyond traditional methods of training a machine learning model. The initial steps described comprising data gathering, labelling data and generating training examples can be done manually for implementing an abstract idea of predicting a likelihood that a user will replace one item with another item in an item list. The judicial exception of “ of predicting a likelihood that a user will replace one item with another item in an item list, using a prediction model, where the machine learning is a trained machine learning model ” is used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions. Rather, these limitations only recite the outcome of “making prediction of a likelihood that a user will replace one item with another item in an item list. See MPEP 2106.05(f). The recitation of “using a trained Machine learning model in steps *iv) and (v) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained Machine learning model” limits the identified judicial exceptions “making prediction of a likelihood that a user will replace one item with another item in an item list based on item data for both items or will reject the replacement,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, even 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. The claim 1 is directed to an abstract idea. Since the other two independent claims 11 and 20 recite similar limitations as claim 1, they are analyzed on the same basis as directed to an abstract idea.
Reference the dependent claims 4-5, and 14-15, they recite the functions of receiving an indication and pacing items in a shopping cart which are generic computer functions, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Reference claims 6-9, and 16-19, are directed to the functions of identifying items by applying filtering rules, generating a replacement score, and when to apply the model, and further qualifying the ranking step, which are mere expansion of the limitations already considered for claim 1 and these additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claim 10, the limitations of selecting a proposed replacement item and replacing the initial item by the selected item using a computer re generic computer functions. See MPEP 2106.05(f). These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Even when viewed in combination, the additional elements in claims 1, 4-11, 14-20 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claims 1, 4-11, 14-20 are directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 11, 4-11, 14-20 are directed to abstract ideas.
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, 4-11, 14-20 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, 4-11, 14-20 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.
As explained with respect to Step 2A, Prong Two, the additional element of “using the trained ML model” in limitations of using a prediction model to generate replacement scores are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As per MPEP 2106, a conclusion that an additional element or elements is/are extra-solution activity, or are well-understood, conventional and routine activity in step 2A should be re-evaluated in step 2B. Here the receiving, acquiring, transmitting, and displaying steps were considered are extra-solution activity, or are well-understood, conventional and routine activity activities in step 2A and thus it is re-evaluated in step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Additional elements of receiving data, transmitting data, accessing data and displaying data were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. 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 receiving, accessing, transmitting, and displaying data steps using a generic computer are a well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, accessing, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. 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).
Even when considered in combination, these additional elements in claims 1, 4-11, 14-20 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, claims 1, 4-11, 14-20 are patent ineligible.
3. Best prior art discussion:
Reference the amended independent claims 1, 11 and 20, the best prior art of record including the references US20220092670 A1 to Laserson et al Applicant NCR Corporation, hereinafter NCR cited in the IDS filed 02/18/2025, Bell et al. [US 11222374 B1], hereinafter Bell cited in the Non-Final Rejection mailed 04/25/2025 and Thangali et al. [US 2023/0245146 A1] cited in this action, alone or combined, neither teaches nor renders obvious at least the limitations, as a whole, comprising .”wherein the replacement prediction model is trained by generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item, labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item, and training the replacement prediction model with the set of training examples to predict the replacement score for a candidate item as a substitute for an item to be replaced”, in combination with the rest of the limitations in the currently amended claims 1, 11 and 20. Claims 4-10, 14-19 depend from claims 1 and 11 respectively.
4 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Some of the following cited references may recite one or two elements from the independent claims 1, 11, and 20 but they, whether alone or combined, do not teach or render obvious the limitations, as a whole, discussed above in paragraph3 above.
(i) Kruck et al. [US 20230111745 A1; see paras 0022 and 0024] describes a recommendation engine 115 applies a trained machine learning model to select and recommend one or more replacement items, wherein the “machine learning model,” is trained using training data to make predictions or provide probabilities for new data items. The
machine learning model can be trained with training data including inputs and desired outputs, wherein the inputs can include features of products or previously-placed orders.
(ii) Harvey et al. [US 20240112267 A1; paras 0064 and 0085] describes that an item replacement determiner 124 can determine a set of potential replacement items by using a trained machine learning algorithm trained by the machine learning training application 128. The
machine learning algorithm comprises a neural network (e.g., a neural network trained using the historical information to identify replacement items and the machine learning algorithm may be trained by creating multiple regression models to predict the cost of the replacement item, and then selecting the best regression model (e.g., the regression model with the least error, etc.).
(iii) Shah et al. [US 2024/0013287 A1, see para 0030] describes a server using use a machine learning model to predict a likelihood that a given replacement item will be acceptable to the user for a requested item using the user information associated with an account that is associated with the exchange history information associated with the account , and/or the item information associated with the given item, wherein an output of the machine learning model may include a likelihood that the given attribute or the given replacement item will be acceptable to the first user. For example, the output may indicate a positive or negative outcome for a given replacement to be acceptable to the first user. The output from the machine learning model can be a score , wherein a higher score indicating a positive result or a probability value for accepting the replacement item.
(iv) Joshi et al. cited in the Non-Final Rejection mailed 04/25/2025 [US 20210233145A1; see paras 0080—0081] describes that an overall substation score can predict if a customer will accept or reject the substitute item as a replacement for an ordered item and further the smart substitution computing device 102 ranks the substitute items based on the overall substitution score.
NPL reference:
(v) Afchar, Darius et al. “Explainability in Music Recommender Systems”; Published in: AI Magazine 43(2), 190-208, 2022; retrieved from IP.COM on 04/22/2025and cited in the Non-Final Rejection mailed 04/25/2025 describes [see page 8] describes that recommendation of a musical item is motivated by similarity of the item to other items previously liked by the user or by the affinity that similar users have towards the recommended item.
Foreign reference:
(vi) CN 111159573 B cited in the Non-Final Rejection mailed 04/25/2025 [See claim 1] describes a recommendation method using the improved IS-SVD slope one algorithm for content recommendation to provide prediction score for substituting an item.
5. Allowability:
If the independent claims 1, 11 and 20 are amended to overcome 35 USC 101 rejection, the application can be placed in condition for allowance, as discussed above in paragraph 3 above.. However, all amendments will be subject to further reconsideration and search.
Response to Arguments
6.1. Applicant's arguments, see pages 12-16, filed 01/12/2025 with respect to rejection of the currently amended independent claims 1, 11, and 20 under 35 USC 101 have been fully considered but they are not persuasive.
Step 2A, Prong Two:
Examiner respectfully disagrees with the applicant’s arguments on pages 14-15,
………..the recited limitations recite an improvement in training the replacement prediction model. The claim's specific training limitations for the replacement prediction model constitute a technological improvement because they define a concrete, data-efficient learning pipeline that enhances the performance and resource utilization of the computing system. By constructing training examples as pairwise item representations (item to be replaced and candidate replacement) and labeling them as converted or unconverted based on user selection events in historical order data, the system transforms the historical data into a high-fidelity supervised signal. Training with this supervised signal yields a compact model that operates on item data rather than expansively on all data available to the system. This reduces input dimensionality of machine-learning model, yielding quick computations during inference time. In short, the training limitations significantly improve computer functionality by structuring data and learning objectives to produce accurate, bias-resilient predictions. In sum, the recited technological solution thereby integrates the alleged judicial exception into a practical application-supporting a finding of eligibility under Step 2A, Prong Two. “, because the step of labeling items which are replaced or unreplaced as converted or unconverted respectively based on user selections in historical order data, under its broadest reasonable interpretation, covers performance in the mind, and falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Abstract Idea Groupings [R-07.2022] II. MENTAL PROCESSES: claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); • a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011.
Labelling items based on if they have been replaced or not do not necessitate inextricable tie to computer technology because these steps can be carried out manually and is just performing the disembodied concept on a general purpose computer, as the same is also confirmed in the Applicant’s Specification paragraph see para 0058 , “ The replacement prediction model is trained based on a set of training examples. Each of these training examples includes item data for an item to be replaced, item data for a candidate replacement item, and a label indicating whether the item to be replaced would be selected by a user for replacement with the candidate replacement item. The set of training examples may be hand-labeled or may be automatically generated by the online concierge system. For example, the online concierge system may present a candidate replacement item as a replacement for an already-selected item and generate a label for a training example with those two items based on whether the user replaced the selected item with the candidate replacement item. “.
The limitations, generating a replacement score based on the accessed item data and item data for each candidate item of the set of candidate which includes historical order data describing items ordered by users of the online system, and generating a set of training examples, each training example comprising item data for an item to be replaced and item data for a candidate replacement item and labeling each training example as converted or unconverted based on whether a user selected the candidate replacement item as a replacement for the item to be replaced, wherein a converted label indicates that the user selected the candidate replacement item and an unconverted label indicates that the user rejected the candidate replacement item”, as analyzed above, recite a mental process of generating replacement scores based on collected data and labeling items based on their history of being replaced or not and the computer is used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Regarding the step of obtaining historical data, as analyzed above, is recited at a high level of generality, and is insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). The computer in these steps is used a tool to perform generic computer functions, See MPEP 2106.05(f).
The use of machine learning model [MLM] is recited in a nominal manner for a mental process of predicting a likelihood that a user will replace one item with another item based on collected historical data which includes labeled items as replaced and unreplaced in the past. Using the trained MLM to apply the abstract idea merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained MLM” limits the identified judicial exception of “predicting a likelihood that a user will replace one item with another item” , this type of limitation merely confines the use of the abstract idea to a particular technological environment (MLM) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
In view of the foregoing, Applicant’s arguments that the referred limitations recite a technical improvement are not persuasive. Accordingly, these additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application, because they do not add any meaningful limits on practicing the abstract idea. Thus, the independent clams 1, and claims 11, and 20 which recite similar limitations s claim 1 are directed to an abstract idea.
Step 2B:
Examiner respectfully disagrees with the Applicant’s arguments on pages 15-16, “ Moreover, the additional elements are non-routine, unconventional, and not well understood activity in the technological field, thereby amounting to an inventive concept supporting eligibility under Step 2B. The MPEP provides that "an examiner should determine that an element (or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry" (italics for emphasis). MPEP §2106.05(d). The additional elements are not so widely prevalent. Wide prevalence requires a high degree of understanding among artisans in the field, such that it need not be described in detail. See id., further referencing 35 U.S.C. § 112(a). The additional elements are not so widely prevalent as to render them conventional, routing, or well understood. Critically, the prior art does not teach nor suggest the additional elements, specifically the elements relating to training of the replacement prediction model. In the prior round of prosecution, Applicant amended claim 1 to include the limitations relating to training of the replacement prediction model. Subsequently, this Office Action indicated that those amendments were successful in distinguishing the claim from the cited references. Office Action, pp. 19-20. This necessarily implies that these limitations are unconventional, non-routine, and not well understood in the field. Accordingly, the additional elements further support finding of eligibility under Step 2B, in addition to Step 2A, Prong Two. “. The Step 2B 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. As discussed above with respect to Step 2A Prong Two, the additional elements in the independent claims 1, 11, and 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. Further, as explained with respect to Step 2A, Prong Two, the additional element of “using the trained ML model” in limitations of using a prediction model to generate replacement scores are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
In view of the foregoing, claims 1, 11, and 20 under 35 USC 101 is sustainable and maintained. Applicant has not filed arguments separately for the dependent claims.
Accordingly, pending claims under 35 USC 101 are patent ineligible.
6.2. Applicant’s arguments, see pages 11-12, filed 01/12/2026, with respect to rejection of claims 1, 4-11, 14-20 under 35 USC 112(a) have been fully considered and are persuasive in view of the current amendments to the independent claims 1, 11, and 20. The rejection of claims 1, 4-11, 14-20 under 35 USC 112(a) has been withdrawn.
Conclusion
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