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 06/12/2026 is entered. Claims 1, 11, and 20 are currently amended. Claims 2-3 and 12-13 are canceled claims. Claims 4-10 depend from claim 1, claims 14-19 depend from claim 11. Clams 1, 4-10, 11, 14-19, and 20 are currently pending for examination.
2. Telephone interview:
At the request of the Applicant a telephone interview was conducted on 06/11/2026. The summary of the interview is reproduced below:
" Issues Discussed:
35 U.S.C. 101
Copy of the Applicant's interview agenda is attached for ready reference. No new amendments are indicated in the agenda. The rejection submitted 03/13/2026 was discussed. Examiner indicated the use of trained Machine learning model, as recited was not directed to a technical problem but instead to providing prediction results for replacement items in a standard way. The claims do not recite improvement the machine learning model itself or a computer functioning or hardware. No agreement reached. All future amendments will be fully reconsidered and subject to search."
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, 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. (Currently Amended) A method, performed by a computer system comprising a processor and a computer-readable medium, comprising:
(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 applying the replacement prediction model comprises: (a) generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, (b)generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item;
(v) ranking the set of candidate items based on the generated replacement scores;
(vi) selecting a proposed replacement item for the initial item based on the ranking;
(vii) 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;
(viii) receiving, via the user interface, user input selecting the option to replace the initial item; and
(ix) 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; 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 , 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.
The highlighted limitations comprising, " wherein applying the replacement prediction model comprises: generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item to predict the likelihood that a user would accept the candidate item as replacement of the initial item"; recite mathematical concepts, as generating embeddings using item embedding model is a mathematical process wherein unstructured data, such as text is translated into vectors to calculate similarity , differences, closeness, distance between different items/information and these limitations also recite mental process as such calculations can be done by humans using pen and paper.
Thus, claim 1 with its dependent claims 4-10, as analyzed above, recites both Mental processes and Mathematical concepts of groupings of abstract ideas. If a claim that includes two or more abstract ideas groupings per Step 2A, Prong One, as per MPEP 2106.04, subsection IIB, under such circumstances, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations of claim 1, as analyzed above, recite both the mental process grouping of abstract ideas, and the mathematical concepts grouping of abstract ideas. Limitations of claim 1 are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
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 applying the replacement prediction model comprises: (a) generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, (bi) generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; (v) ranking the set of candidate items based on the generated replacement scores; (vi) selecting a proposed replacement item for the initial item based on the ranking; (vii) 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; (viii) receiving, via the user interface, user input selecting the option to replace the initial item; and (ix) 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, (vii) 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 (viii) 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 " (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 applying the replacement prediction model comprises: (a) generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, (b) generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; (v) ranking the set of candidate items based on the generated replacement scores; (vi) selecting a proposed replacement item for the initial item based on the ranking; (ix) 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) (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; " recites using a trained neural network to predict a replacement item acceptable to the user using a generic computer network. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of "generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item" is performed “using the trained NN.” The trained NN is used to generally apply the abstract idea without placing any limits on how the trained NN functions. Rather, these limitations only recite the outcome of "generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item to predict the likelihood that a user would accept the candidate item as replacement of the initial item", and do not include any details about how the “generating score" and "predicting" are accomplished. See MPEP 2106.05(f). The recitation of “using a trained NN” in these limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained NN” limits the identified judicial exceptions ““generating score" and "predicting" this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, even 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 neural network” in limitations of generating replacement scores and predicting a likelihood of a replacement item to be accepted by the user 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.
4. 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 .” generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; ”, 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.
5 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) Namballa et al. [US 11,551, 440 B1; See col.4, lines 42-56] describes
Various trained neural networks to predict the likelihood that a given descriptor applies to the input visual content item 105, wherein the machine-readable representation of the visual content item can be input separately into each neural network to predict a probabilistic value representing the likelihood that its descriptor applies to the input visual content item 105.
(ii) Kruck et al. [US 20230111745 A1 cited in the Non-Final Rejection mailed 03/13/2026; 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.
(iii) Harvey et al. [US 20240112267 A1 cited in the Non-Final Rejection mailed 03/13/2026; 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.).
(iv) Shah et al. [US 2024/0013287 A1 cited in the Non-Final Rejection mailed 03/13/2026, 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.
(v) 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 references:
(vi) Y. Wang and P. Craig, "Vector space model embedding for recommender system neural networks," 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, China, 2017, pp. 599-604 retrieved from IP. Com 07142026 describes neural network enhances recommendation algorithms such as Collaborative Filtering (CF) network (NN) to make more accurate recommendations by achieving a higher score in both similarity calculation and predication. A vector space embedding method is used to vectorize users and items before a Deep Neural Network (DNN) rating prediction network model is used to predict users' rating behavior.
(vii) 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:
(viii) 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.
6. 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. However, all amendments will be subject to further reconsideration and search.
Response to Arguments
7. Applicant's arguments filed 06/12/2026, see pages 12-17, have been fully considered but they are not persuasive.
Step 2A, Prong One:
With respect to Step2A, Prong One, analysis, Examiner has fully reconsidered the applicant's arguments on pages 13-15 that the limitations in steps " wherein applying the replacement prediction model comprises: (a)generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, (bi) generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; " do not recite Mathematical Concepts and Mental Process. Examiner disagrees, because generating embeddings using item embedding model is a mathematical process wherein unstructured data, such as text is translated into vectors to calculate similarity, differences, closeness, distance between different items/information and these limitations also recite mental process as such calculations can be done by humans using pen and paper. Therefore, these limitations do recite both Mathematical Concepts which can be performed by human operators using pen and paper. That is, other than reciting “using a neural network on a generic computer” nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the “neural network on a generic computer” language, the claim encompasses a person looking at data collected related to items to be replaced with the target item and translates the text [unstructured data] describing their attributes of the items into an array of vectors to calculate and predict the similarities between the items based on their closeness or distance and then making a simple judgement to predict if the replacement item can be accepted by the user or not. The mere nominal recitation of using a neural network on a generic computer does not take the claim limitations out of the mathematical concept and mental process grouping. Thus, the limitations of claim 1, discussed above do "set forth" and 'describe" both Mathematical concepts and a Mental process.
Applicant's arguments that the claim does not recite "Certain Methods of Organizing Human Activity" are not relevant as Examiner's has not equated the claim 1limitations to "Certain Methods of Organizing Human Activity".
Step 2A, Prong Two:
Regarding Step 2A, Prong Two, the Applicant's arguments on pages 15-16 have been fully considered and they are not persuasive, Examiner disagrees with the Applicant's arguments, " The additional elements embody an improvement to the field of machine learning as applied to item-replacement prediction. Specifically, Claim 1 recites a two-stage model architecture in which a shared item embedding model first maps item data for both the initial item and each candidate replacement item into a common latent space, generating respective embeddings, and a downstream neural network then inputs the pair of embeddings to predict the likelihood that a user would accept the candidate item as a replacement.
This architecture is technically significant in two respects. First, applying a single embedding model to both the initial and candidate items produces representations in a shared latent space, enabling the neural network to assess item-pair substitutability through vector relationships rather than raw feature comparison. Second, the architecture separates the representation-learning task (handled by the item embedding model) from the relationship- scoring task (handled by the neural network), allowing each component to be optimized for its specific subtask. The result is a replacement prediction model that achieves more accurate and scalable item-replacement predictions than would be possible through conventional item- matching techniques. This constitutes a concrete improvement to machine-learning technology, not a mere application of a generic model to a routine task. 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 limitations, " a)generating an embedding for the initial item by applying an item embedding model to map the item data of the initial item into a latent space, (bi) generating, for each candidate item, an embedding for the candidate item by applying the item embedding model to map the item data of the candidate item into the latent space, and (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; ; as discussed under step 2A, Prong One, are recite Mathematical concept grouping and Mental Process grouping of abstract ideas. 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) (c) generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item; " recites using a trained neural network to predict a replacement item acceptable to the user using a generic computer network. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of "generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item into a neural network trained to predict the likelihood that a user would accept the candidate item as replacement of the initial item" is performed “using the trained NN.” The trained NN is used to generally apply the abstract idea without placing any limits on how the trained NN functions. Rather, these limitations only recite the outcome of “generating the replacement score for each candidate item by inputting the embedding of the initial item and the embedding of the candidate item to predict the likelihood that a user would accept the candidate item as replacement of the initial item", and do not include any details about how the “generating score" and "predicting" are accomplished. See MPEP 2106.05(f). The recitation of “using a trained NN” in these limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained NN” limits the identified judicial exceptions ““generating score" and "predicting" 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).
In view of the foregoing, the Applicants are not persuasive and the limitations of the independent claims 1, 11, and 20 are directed to an abstract idea.
Applicant has not filed separate arguments for any of the dependent claims.
Step 2B:
Regarding Step 2B, the Applicant's arguments on pages 16-17 have been fully considered and they are not persuasive, Examiner disagrees with the Applicant's arguments, " 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 two-stage architecture comprising a shared item embedding model that maps item data into a latent space and a downstream neural network that predicts replacement likelihood from the resulting item-pair embeddings, as further discussed below under the arguments against the rejections under 35 U.S.C. § 102. Accordingly, the additional elements further support finding of eligibility under Step 2B,."; because the arguments refer to prior art consideration, which is not relevant for Step 2B analysis. The Step 2B analysis 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. 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 neural network” in limitations of generating replacement scores and predicting a likelihood of a replacement item to be accepted by the user 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:
In view of the foregoing, the limitations of the independent claims 1, 11, and 20 do not recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim and the claims are patent ineligible.
Conclusion
Final Rejection:
8. 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
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