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 .
Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-8 are directed to a system, claims 9-16 are directed to a method, and claims 17-20 are directed to non-transitory computer-readable media. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding Claim 1:
Step 2A Prong 1:
generate, (This step for generating asset of candidate substitution elements is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
rank, (This step for ranking the set of candidate substitution elements is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. Judgment).)
select at least one substitution element from the set of candidate substitution elements; (This step for selecting at least one substitution element from the set of candidate substitution elements is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. Judgment).)
Step 2A Prong 2: This judicial exception is not integrated into a practical application
Additional elements:
A system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
receive a substitution request identifying an anchor element; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
generate,by a trained candidate selection model(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
rank,by a trained ranking model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., ranking) – see MPEP 2106.05(f).)
receive feedback data representative of a suitability of the selected at least one substitution element with respect to the anchor element; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
and update at least one of the trained candidate selection model or the trained ranking model by applying an iterative training process incorporating at least a portion of the feedback data. (Training a model (e.g. selection model) is understood as mere instructions to implement an abstract idea (e.g., select a candidate element) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of mere instructions to perform generic computer functions, in addition to merely applying an abstract idea with a classifier that are implemented to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
generate,by a trained candidate selection model(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
rank,by a trained ranking model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., ranking) – see MPEP 2106.05(f).)
receive a substitution request identifying an anchor element; (This step is directed to transmitting or receiving information and is a well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(II)(i)).)
receive feedback data representative of a suitability of the selected at least one substitution element with respect to the anchor element; This step is directed to transmitting or receiving information and is a well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(II)(i)).)
and update at least one of the trained candidate selection model or the trained ranking model by applying an iterative training process incorporating at least a portion of the feedback data. (Training a model (e.g. selection model) is understood as mere instructions to implement an abstract idea (e.g., select a candidate element) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions/instructions that are implement to perform the disclosed abstract idea above.
Regarding Claim 2:
Incorporates the rejection of claim 1.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 1, wherein the processor is configured, prior to receiving the substitution request, to read the set of instructions to: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
train the candidate selection model by applying an iterative training process to modify a classification framework based on a first training data set; (Training a model (e.g. selection model) is understood as mere instructions to implement an abstract idea (e.g., select a candidate element) on a computer – see MPEP 2106.05(f).)
and train the ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set. (Training a model (e.g. ranking model) is understood as mere instructions to implement an abstract idea (e.g., rank candidate elements) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere instructions/mere applying it with a generic classifier that are implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of mere instructions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 3:
Incorporates the rejection of claim 2.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 2, wherein the classification framework comprises a feed forward neural network. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 4:
Incorporates the rejection of claim 2.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 2, wherein the learning-to-rank framework comprises one of a pairwise ranking or a listwise ranking. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 5:
Incorporates the rejection of claim 4.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 4, wherein the learning-to-rank framework comprises one of an XGBoost framework or a LambdaMART framework. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 6:
Incorporates the rejection of claim 1.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 1, wherein the at least one substitution element comprises a set of N highest-ranked substitution elements selected from the set of candidate substitution elements. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 7:
Incorporates the rejection of claim 1.
Step 2A Prong 1: The claim recites an additional abstract idea.
The system of claim 1, wherein (This step for generating an individual relevance score for each element in the set of catalog items is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
Regarding Claim 8:
Incorporates the rejection of claim 1.
Step 2A Prong 1: The claim recites additional abstract ideas.
The system of claim 1, wherein (This step for ranking the set of candidate substation elements by a relative relevance is practically performable in the human mind and is understood to be recitation of a mental process with the aid of pen and paper (i.e. judgement).)
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., ranking) – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
Regarding Claims 9-16:
Claims 9-16 recite method claims having similar limitations as the system claims of claims 1-8. Therefore, claims 9-16 are rejected for the same reasons as disclosed for claims 1-8 above.
Regarding Claim 17:
Claim 17 recites a product claim having similar limitations to the system of claim 1. Therefore, claim 17 is rejected for the same reasons as disclosed for claim 1. The additional elements of claim 17 are analyzed below.
Step 2A Prong 1: Please see Step 2A Prong 1 analysis of claim 1.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
training a candidate selection model by applying an iterative training process to modify a classification framework based on a first training data set; (Training a model (e.g. selection model) is understood as mere instructions to implement an abstract idea (e.g., select a candidate element) on a computer – see MPEP 2106.05(f).)
training a ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set; (Training a model (e.g. ranking model) is understood as mere instructions to implement an abstract idea (e.g., ranking candidate elements) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere instructions/mere applying it with a generic classifier that are implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of mere instructions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 18:
Incorporates the rejection of claim 17
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 1 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The non-transitory computer readable medium of claim 17, wherein the classification framework comprises a feed forward neural network (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
and the learning-to-rank framework comprises one of a pairwise ranking or a listwise ranking. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 19:
Incorporates the rejection of claim 18
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 1 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The non-transitory computer readable medium of claim 18, wherein the learning-to-rank framework comprises one of an XGBoost framework or a LambdaMART framework. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 20:
Incorporates the rejection of claim 17.
Step 2A Prong 1:
The non-transitory computer readable medium of claim 17, (This step for generating an individual relevance score for each element in the set of catalog items is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
and the (This step for ranking the set of candidate substation elements by a relative relevance is practically performable in the human mind and is understood to be recitation of a mental process with the aid of pen and paper (i.e. judgement).)
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., ranking) – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely applying it with a generic classifier that is implemented to perform the disclosed abstract idea above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 7-9, and 15-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kruck et al. (US 20230111745 A1, hereinafter Kruck).
Regarding Claim 1:
Kruck discloses:
A system, comprising: a non-transitory memory; ([Para 62] discloses the system utilizing non-transitory memory.)
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: ([Para 61; Para 62] discloses a processor on the system coupled with the non-transitory memory, in paragraph 62, to perform instructions in the system.)
receive a substitution request identifying an anchor element; ([Para 23] discloses a neural network model which receives an input/signal with an identifier or attribute (i.e. anchor element) of an unfulfillable item in a BOPUS (the system in the art) order (i.e. request))
generate, by a trained candidate selection model, a set of candidate substitution elements in response to the substitution request, wherein the trained candidate selection model is configured to receive an input set including the anchor element, a feature set, and a set of catalog elements; ([Para 12; Para 25] discloses a trained model which includes a hierarchy of sub-models, which receive a set of features (i.e. feature set) to generate a candidate replacement item for the item that needs replacement (i.e. anchor element). Paragraph 25 discloses the model being able to use various data, which includes available products in a retail store (i.e. catalog elements).)
rank, by a trained ranking model, the set of candidate substitution elements, wherein the trained ranking model is configured to receive an input set including the anchor element, the feature set, and the set of candidate substitution elements; ([Para 40; Para 26; Para 12; Para 25] Paragraph 40 discloses the top recommendation model which ranks the candidates received from the sub models (i.e. candidate substitution elements) Paragraph 26 discloses the recommendation model being a model in the recommendation engine. As stated earlier, paragraph 12 discloses the recommendation engine receiving the item that needs to be replaced (i.e. anchor element), and a feature set. Paragraph 25 discloses the multitude of features that the system may use to generate and select candidates.)
select at least one substitution element from the set of candidate substitution elements; ([Para 39] discloses the recommendation model selecting a candidate replacement item from the sub-models.)
receive feedback data representative of a suitability of the selected at least one substitution element with respect to the anchor element; ([Para 52] discloses the recommendation engine receiving feedback based on the user’s acceptance or cancellation of the recommended item. This is with respect to the item the user initially requested to be replaced.)
and update at least one of the trained candidate selection model or the trained ranking model by applying an iterative training process incorporating at least a portion of the feedback data. ([Para 53; Fig 3] discloses retraining the sub-models using the feedback. Figure 3 discloses how the model is updated using customer response, which is naturally iterative as the recommendation system is utilized.)
Regarding Claim 7:
The system of claim 1, wherein the trained candidate selection model is configured to generate an individual relevance score for each element in the set of catalog items. ([Para 28; Fig 2; Para 25-26] Paragraph 28 discloses the similarity sub-model outputting a similarity (i.e. relevance) score for pairs of items. Figure 2 discloses how the similarity model is a sub model of the top ranking model which makes the selection. Paragraph 25-26 discloses how the recommendation engine encompasses the recommendation top model, which receives the items available (i.e. catalog of items). This input is passed down to the sub models.)
Regarding Claim 8:
The system of claim 1, wherein the trained ranking model is configured to rank the set of candidate substitution elements by a relative relevance. ([Para 41; Para 28] Paragraph 41 discloses the recommendation model (i.e. ranking model) utilizing scores from the sub models to rank the set of candidates. Paragraph 28 discloses the similarity sub-model which outputs a similarity score of pairs of items – which can be used to rank the candidates.)
Regarding Claims 9:
Claims 9 recites a method performs on the system as described in Claim 1. Therefore, claims 9 is rejected under the same reasons mentioned for claim 1.
Regarding Claim 15-16:
Claims 15-16 recites a method that performs on the system as described in claims 7-8. Therefore, claims 15-16 are rejected under the same reasons mentioned for claim 7-8.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-6, 10-14, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kruck et al. (US 20230111745 A1, hereinafter Kruck) in view of Mohan et al. (US 20220312256, hereinafter Mohan)
Regarding Claim 2:
Kruck discloses:
The system of claim 1, wherein the processor is configured, prior to receiving the substitution request, to read the set of instructions to: ([Para 61; Para 24; Para 26] Paragraph 61 discloses the system as disclosed in the prior art being implemented on processors for all disclosures in the art. Paragraph 24 and 26 indicates that once the model is trained, it can be used to generate replacement recommendations – i.e. that it is trained prior to requests.)
train the candidate selection model by applying an iterative training process to modify a classification framework based on a first training data set; ([Para 28; Para 29; Para 32; Para 35; Para 43] discloses the plurality of sub models trained to recommend candidates. Paragraph 43 discloses training the top recommendation model to select from the sub models. Paragraph 28 further discloses utilizing a training data set based off customer interaction.)
Kruck does not explicitly disclose: and train the ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set.
However, Mohan discloses in the same field of endeavor: and train the ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set. ([Para 84; Para 87; Para 88] discloses an iterative training process on a learning-to-rank framework, including those of SVM rank and XGBoost. Paragraph 87 discloses using a training data set to train SVM ranking framework. These training process modifies the framework to correct mistakes of previous iterations of the framework, as disclosed in paragraph 88.)
Kruck and Mohan are both analogous to the art to the present invention because both are from the same field of endeavor directed to selecting optimal elements based on a ranking framework of said elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the item recommendation system disclosed by Kruck and the server selection system disclosed by Mohan. One would be motivated to combine the substitution selection system of Kruck with the iterative training process disclosed by Mohan to improve the ranking system in the substitution recommendation system and give customers optimal substitute recommendations.
Regarding Claim 3:
Kruck in view of Mohan discloses: The system of claim 2, as disclosed in the rejection of claim 2.
Kruck further discloses:
wherein the classification framework comprises a feed forward neural network ([Para 23] discloses the model used by the recommendation engine using input from the input layer that goes through multiple layers into a final output layer. This is a feed forward process. )
Regarding Claim 4:
Kruck in view of Mohan discloses: The system of claim 2, as disclosed in the rejection of claim 2.
Kruck further discloses:
wherein the learning-to-rank framework comprises one of a pairwise ranking or a listwise ranking. ([Para 28; Para 41] Paragraph 28 discloses the similarity sub model which outputs a score of pairs (i.e. pairwise) of items. Paragraph 41 discloses the top recommendation model that ranks the items based on the outputs of the sub-models.)
Regarding Claim 5:
Kruck discloses:
The system of claim 4 as disclosed in the rejection of claim 4.
Kruck does not explicitly disclose: wherein the learning-to-rank framework comprises one of an XGBoost framework or a LambdaMART framework.
However, Mohan discloses in the same field of endeavor: wherein the learning-to-rank framework comprises one of an XGBoost framework or a LambdaMART framework. ([Para 83, 84, 88] discloses using learning-to-rank frameworks to rank servers in their server recommendation system, which includes XGBoost.)
Kruck and Mohan are both analogous to the art to the present invention because both are from the same field of endeavor directed to selecting optimal elements based on a ranking framework of said elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the item recommendation system disclosed by Kruck and the server selection system disclosed by Mohan. One would be motivated to add the feature of utilizing a ranking system to recommend items as a replacement with the learning-to-rank framework utilizing XGBoost to improve the ranking and selection process of substitutes for unavailable items as requested by a user.
Regarding Claim 6:
Kruck discloses:
The system of claim 1, wherein the at least one substitution element comprises a set of ([Para 40-41; Para 43; Fig 2] Figure 2 in addition to Paragraph 40 disclose the top recommendation model receiving candidates from sub models (i.e. set of candidates). Paragraph 41 discloses the ranking of the candidates based on scores from the sub models. Paragraph 43 discloses the top-level model being able to select one or more (i.e. N set of substitutions).)
Kruck does not explicitly disclose:
However, Mohan discloses in the same field of endeavor: ([Para 58] discloses the system of selecting servers where it can selected the highest ranked server from a plurality of servers based on the ranking methods (i.e. N highest ranked) disclosed in the art.)
Kruck and Mohan are both analogous to the art to the present invention because both are from the same field of endeavor directed to selecting optimal elements based on a ranking framework of said elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the item recommendation system disclosed by Kruck and the server selection system disclosed by Mohan. One would be motivated to add the feature of utilizing a ranking system to recommend items as a replacement with the selection system based on selecting the highest ranked option to improve the selection process of substitutes for unavailable items as requested by a user/customer.
Regarding Claim 10:
Claim 10 recites a method that performs on the system as described in claim 2. Therefore, claim 10 is rejected under the same reasons mentioned for claim 2.
Regarding Claim 11:
Claim 11 recites a method that performs on the system as described in claim 3. Therefore, claim 11 is rejected under the same reasons mentioned for claim 3.
Regarding Claim 12:
Claim 12 recites a method that performs on the system as described in claim 4. Therefore, claim 12 is rejected under the same reasons mentioned for claim 4.
Regarding Claims 13:
Claim 13 recites a method that performs on the system as described in claim 5. Therefore, claim 13 is rejected for the same reasons mentioned for claim 5.
Regarding Claim 14:
Claim 14 recites a method that performs on the system as described in claim 6. Therefore, claim 14 is rejected under the same reasons mentioned for claim 6.
Regarding Claim 17:
Kruck discloses:
A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising: ([Para 62] discloses the non-transitory media the system can be stored on, including a processor.)
training a candidate selection model by applying an iterative training process to modify a classification framework based on a first training data set; ([Para 28; Para 29; Para 32; Para 35; Para 43] discloses the plurality of sub models trained to recommend candidates. Paragraph 43 discloses training the top recommendation model to select from the sub models. Paragraph 28 further discloses utilizing a training data set based off customer interaction.)
receiving a substitution request identifying an anchor element; ([Para 23] discloses a neural network model which receives an input/signal with an identifier or attribute (i.e. anchor element) of an unfulfillable item in a BOPUS (the system in the art) order (i.e. request))
generating, by the trained candidate selection model, a set of candidate substitution elements in response to the substitution request, wherein the trained candidate selection model is configured to receive an input set including the anchor element, a feature set, and a set of catalog elements; ([Para 12; Para 25] discloses a trained model which includes a hierarchy of sub-models, which receive a set of features (i.e. feature set) to generate a candidate replacement item for the item that needs replacement (i.e. anchor element). Paragraph 25 discloses the model being able to use various data, which includes available products in a retail store (i.e. catalog elements).)
ranking, by the trained ranking model, the set of candidate substitution elements, wherein the trained ranking model is configured to receive an input set including the anchor element, the feature set, and the set of candidate substitution elements; ([Para 40; Para 26; Para 12; Para 25] Paragraph 40 discloses the top recommendation model which ranks the candidates received from the sub models (i.e. candidate substitution elements) Paragraph 26 discloses the recommendation model being a model in the recommendation engine. As stated earlier, paragraph 12 discloses the recommendation engine receiving the item that needs to be replaced (i.e. anchor element), and a feature set. Paragraph 25 discloses the multitude of features that the system may use to generate and select candidates.)
selecting at least one substitution element from the set of candidate substitution elements; ([Para 39] discloses the recommendation model selecting a candidate replacement item from the sub-models.)
receiving feedback data representative of a suitability of the selected at least one substitution element with respect to the anchor element; ([Para 52] discloses the recommendation engine receiving feedback based on the user’s acceptance or cancellation of the recommended item. This is with respect to the item the user initially requested to be replaced.)
and updating at least one of the trained candidate selection model or the trained ranking model by applying an iterative training process incorporating at least a portion of the feedback data. ([Para 53; Fig 3] discloses retraining the sub-models using the feedback. Figure 3 discloses how the model is updated using customer response, which is naturally iterative as the recommendation system is utilized.)
Kruck does not explicitly disclose: training a ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set;
However, Mohan discloses in the same field of endeavor: training a ranking model by applying an iterative training process to modify a learning-to-rank framework based on the first training data set; ([Para 84; Para 87; Para 88] discloses an iterative training process on a learning-to-rank framework, including those of SVM rank and XGBoost. Paragraph 87 discloses using a training data set to train SVM ranking framework. These training process modifies the framework to correct mistakes of previous iterations of the framework, as disclosed in paragraph 88.)
Kruck and Mohan are both analogous to the art to the present invention because both are from the same field of endeavor directed to selecting optimal elements based on a ranking framework of said elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the item recommendation system disclosed by Kruck and the server selection system disclosed by Mohan. One would be motivated to combine the substitution selection system of Kruck with the iterative training process disclosed by Mohan to improve the ranking system in the substitution recommendation system and give customers optimal substitute recommendations.
Regarding Claim 18:
Kruck in view of Mohan discloses:
The non-transitory computer readable medium of claim 17, ([Para 62] discloses the non-transitory media the system can be stored on, including a processor.)
Kruck further discloses:
wherein the classification framework comprises a feed forward neural network ([Para 23] discloses the model used by the recommendation engine using input from the input layer that goes through multiple layers into a final output layer. This is a feed forward process. )
and the learning-to-rank framework comprises one of a pairwise ranking or a listwise ranking. ([Para 28; Para 41] Paragraph 28 discloses the similarity sub model which outputs a score of pairs (i.e. pairwise) of items. Paragraph 41 discloses the top recommendation model that ranks the items based on the outputs of the sub-models.)
Regarding Claims 19:
Claim 19 recites an article of manufacture that performs on the system as described in claim 5. Therefore, claim 19 is rejected for the same reasons mentioned for claim 5.
Regarding Claim 20:
Kruck in view of Mohan discloses:
The non-transitory computer readable medium of claim 17, ([Para 62] discloses the non-transitory media the system can be stored on, including a processor.)
Kruck further discloses:
wherein the trained candidate selection model is configured to generate an individual relevance score for each element in the set of catalog items ([Para 28; Fig 2; Para 25-26] Paragraph 28 discloses the similarity sub-model outputting a similarity (i.e. relevance) score for pairs of items. Figure 2 discloses how the similarity model is a sub model of the top ranking model which makes the selection. Paragraph 25-26 discloses how the recommendation engine encompasses the recommendation top model, which receives the items available (i.e. catalog of items). This input is passed down to the sub models.)
and the trained ranking model is configured to rank the set of candidate substitution elements by a relative relevance. ([Para 41; Para 28] Paragraph 41 discloses the recommendation model (i.e. ranking model) utilizing scores from the sub models to rank the set of candidates. Paragraph 28 discloses the similarity sub-model which outputs a similarity score of pairs of items – which can be used to rank the candidates.)
Conclusion
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/SUMAIR RASHED CHOWDHURY/
Examiner, Art Unit 2127
8/19/2026
/ABDULLAH AL KAWSAR/ Supervisory Patent Examiner, Art Unit 2127
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