DETAILED ACTION
Status of Claims
This action is in reply to the claims filed on 10 July 2024.
Claims 1-20 have been examined and are pending
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 .
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 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-8 are directed to a method, claims 9-15 are directed to a product of manufacture (recited as a non-transitory computer-readable storage medium), and claims 16-20 are directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of providing a set of cold start results to a user. Specifically, representative claim 1 recites the abstract idea of:
receiving, by the system, a query from a user of the system;
identifying a candidate set of cold start results to the query, the candidate set of cold start results having been presented to the user less than threshold number of times in a previous time period;
filtering the candidate set of cold start results based on relevance to the query to generate a final set of cold start results;
generating a score for each cold start result of the final set of cold start results using a scoring baseline common to standard results, wherein the score is generated without interaction data, and wherein the scoring baseline enables comparison of cold start results without interaction data to the standard results with interaction data;
ranking the final set of cold start results with a set of standard results based on the score for each cold start result using the scoring baseline; and
causing, responsive to the query, at least a subset of the final set of cold start results to be presented with at least a subset of the set of standard results for display to the user.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of providing a set of cold start results to a user, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recite the steps of receiving a query from a user, identifying a candidate set of cold start results based on the query, filtering the candidate set of cold start results based on relevancy to the query, generating scores for each cold start result of the final set, ranking the final set of the cold start results with a set of standard results based on the score for each result, and causing the final set of cold start results to be presented to the user in response to the query, thereby making this a sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: a computer system comprising a processor and a computer-readable medium, the computing system, online system, and using a machine learning model.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., providing a set of cold start results to a user) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claims recite a machine learning model, the recitations are results based in nature and do not include details as to how the machine learning is actually functioning beyond known functions. Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Independent claims 9 and 16 are similar in nature to representative claim 1 and Step 2A, Prong 1 analysis is the same as above for representative claim 1. It is noted that in independent claim 9 includes the additional elements of a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing and independent claim 16 includes the additional elements of one or more processors and a non-transitory computer-readable storage medium storing instructions executable by the one or more processors for performing. The Applicant’s specification does not provide any discussion or description of the claimed additional elements in claims 9 and 16, as being anything other than generic elements. Thus, the claimed additional elements of claims 9 and 16 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 9 and 16 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claims 9 and 16, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 9 and 16 are ineligible.
Dependent claims 2-8, 10-15, and 17-20, depending from claims 1, 9, and 16 respectively, not aid in the eligibility of the independent claims and representative independent claim 1. The claims of 2-8, 10-15, and 17-20 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of applying the machine learning model to the final set of cold start results trained to (claims 3, 11, & 18), and the machine learning model is trained and training the machine learning model (claims 4, 12, & 19). Applicant’s specification does not provide any discussion or description of the claimed additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Although the claims recite applying the machine learning model and training the machine learning model, the recitations are results based in nature and do not include details as to how the machine learning model is actually functioning beyond known functions. Accordingly, claims 3, 4, 11, 12, 18, and 19 are directed towards an abstract idea. Additionally, the additional elements of claims 3, 4, 11, 12, 18, and 19, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 2, 5-8, 10, 13-15, 17, and 20 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, the dependent claims 2-8, 10-15, and 17-20 are ineligible.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj, A., et al. (PGP No. US 2024/0256578 A1), in view of Wu, J., et al. (Patent No US 9,959,563 B1).
Claim 1-
Bhardwaj discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, by the computing system of an online system, a query from a user of the online system (Bhardwaj, see: paragraph [0058] disclosing “request 252 can include a search request” and “operating on a search string provided in the request” and “by a user via a network interface page”);
identifying a candidate set of cold start results to the query, the candidate set of cold start results having been presented to the user (Bhardwaj, see: paragraph [0058] disclosing “request can include a search identifying the anchor item 256 and/or the anchor item 256 can be identified by a search engine”; and pargraph [0059] disclosing “generate a set of combined candidate items 264 including cold-start candidate items 266, e.g., items having no or little interaction data”);
filtering the candidate set of cold start results based on relevance to the query to generate a final set of cold start results (Bhardwaj, see: paragraph [0059] disclosing “a set of recommended items 272 [i.e., a final set of cold start results] is selected from a set of candidate items 258 based on the received anchor item 256” and “the set of recommended items 272 is generated by a graph-based cold-start (GCS) model 262 and a ranking module 270”) (Examiner’s note: It is interpreted that the anchor item is determined based on the user query of Bhardwaj, and therefore encompasses results based on relevance to the query.);
generating, using a machine learning model, for each cold start result of the final set of cold start results common to standard results, wherein generated without interaction data, and wherein enables comparison of cold start results without interaction data to the standard results with interaction data (Bhardwaj, see: paragraph [0046] disclosing “cold-item recommendations as compared to hot item recommendations”; and paragraph [0055] disclosing “neural network 100…trained, to generate a set of recommended items including cold-start recommended item” and “the comparison training value for the j-th node of the output layer 114”; and paragraph [0059] disclosing “recommended items 272 is generated by a graph-based cold-start (GCS) model 262 and a ranking module 270” and “candidate items 264 including cold-start candidate items 266, e.g., items having no or little interaction data” and “hot candidate items 268, e.g., items that have high or current interaction data”; and paragraph [0079] disclosing “ranking module 270 is configured to separately rank cold-start recommendations [i.e., standard results]”);
ranking the final set of cold start results with a set of standard results for each cold start result (Bhardwaj, see: paragraph [0079] disclosing “ranking module 270 is configured to separately rank cold-start recommendations”); and
causing, responsive to the query, at least a subset of the final set of cold start results to be presented with at least a subset of the set of standard results for display to the user (Bhardwaj, see: paragraph [0079] disclosing “set of recommended items 272 is output from the recommendation engine” and “include a set of the top ranked N items”; and paragraph [0080] disclosing “interface generation engine 254 can be configured to generate an interface including all or a subset of the items included in the set of recommended items 272”).
Although Bhardwaj discloses identifying the candidate set of cold start results to the query, as Bhardwaj describes that cold-start candidate items are generated that have little or no interaction data (Bhardwaj, paragraph [0059]), the reference does not describe that the results are presented to the user less than a threshold number of times in a previous time period. Further, Bhardwaj does not describe a score for results nor a scoring baseline.
Bhardwaj does not disclose:
results having been presented to the user less than a threshold number of times in a previous time period;
a score for each result;
a scoring baseline;
the scoring baseline;
Wu, however, does teach:
results having been presented to the user less than a threshold number of times in a previous time period (Wu, see: Col. 19, ln. 64-65 disclosing “a cold start recommendation” and “identify items to recommend for an item that has no access history or less than a threshold amount of access history”)
a score for each result (Wu, see: Col. 16, ln. 33-34 disclosing “determining whether to associate the recommendation rule with items or an item category based on the score generated for the recommendation rule”);
a scoring baseline (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule” and “where the score does not satisfy a score threshold”);
the scoring baseline (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule” and “where the score does not satisfy a score threshold”).
This step of Wu is applicable to the method of Bhardwaj, as they both share characteristics and capabilities, namely, they are directed to recommending items with little or no interaction data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bhardwaj to include the features of results having been presented to the user less than a threshold number of times in a previous time period, a score for each result, a scoring baseline, and the scoring baseline, as taught by Wu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the method of Bhardwaj to improve item recommendations by providing recommendations that would not previously have been included for a customer to potentially purchase (Wu, see: Col. 2, ln. 57-65).
Claim 2-
Bhardwaj in view of Wu teach the method of claim 1, as described above.
Bhardwaj discloses wherein identifying a candidate set of cold start results to the query comprises identifying a set of items available for purchase by the user through a multi-retailer marketplace provided by an online concierge system (Bhardwaj, see: paragraph [0085] disclosing “third-party sellers and third-party items, within an interface or associated catalog”).
Claim 3-
Bhardwaj in view of Wu teach the method of claim 1, as described above.
Bhardwaj discloses wherein for each cold start result of the final set of cold start results using the common to standard results comprises:
applying the machine learning model to the final set of cold start results trained for each of the set of cold start results without the interaction data, wherein the common to the cold start results and the standard results (Bhardwaj, see: paragraph [0046] disclosing “cold-item recommendations as compared to hot item recommendations”; and paragraph [0055] disclosing “neural network 100…trained, to generate a set of recommended items including cold-start recommended item” and “the comparison training value for the j-th node of the output layer 114”; and paragraph [0059] disclosing “recommended items 272 is generated by a graph-based cold-start (GCS) model 262 and a ranking module 270” and “candidate items 264 including cold-start candidate items 266, e.g., items having no or little interaction data” and “hot candidate items 268, e.g., items that have high or current interaction data”; and paragraph [0079] disclosing “ranking module 270 is configured to separately rank cold-start recommendations [i.e., standard results]”).
Bhardwaj does not disclose:
generating the score;
a scoring baseline;
generate the probability of conversion is the scoring baseline;
Wu, however, does teach:
generating the score (Wu, see: Col. 16, ln. 33-34 disclosing “determining whether to associate the recommendation rule with items or an item category based on the score generated for the recommendation rule”);
the scoring baseline (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule” and “where the score does not satisfy a score threshold”);
generate the probability of conversion is the scoring baseline (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule”; and Col. 17, ln. 27-29 disclosing “Performing…calculation, the value of P(S and !T), which refers to the probability that a user purchases a 3-D LED TV and a TV Accessory other than 3D glasses is 0.2.”).
This step of Wu is applicable to the method of Bhardwaj, as they both share characteristics and capabilities, namely, they are directed to recommending items with little or no interaction data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bhardwaj to include the features of generating the score, scoring baseline, and the probability of conversion is the scoring baseline, as taught by Wu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the method of Bhardwaj to improve item recommendations by providing recommendations that would not previously have been included for a customer to potentially purchase (Wu, see: Col. 2, ln. 57-65).
Claim 4-
Bhardwaj in view of Wu teach the method of claim 3, as described above.
Bhardwaj discloses wherein the online system is an online concierge system, each of the cold start results and the standard results correspond to an item available for purchase by the user through a multi-retailer marketplace provided by the online concierge system (Bhardwaj, see: paragraph [0085] disclosing “third-party sellers and third-party items, within an interface or associated catalog”), and the machine learning model is trained by:
obtaining user characteristics and user interaction history for a set of users in a training population, wherein the interaction history includes viewing history and purchase history (Bhardwaj, see: paragraph [0066] disclosing “co-viewed, items within the set of candidate items 258 based on historical interaction data”; and paragraph [0084] disclosing “user interactions, and/or other parameters. For example, additional signals, such as search query data, co-bought item data, etc. can be incorporated into a GCS model”);
obtaining product characteristics and retailer characteristics (Bhardwaj, see: paragraph [0061] disclosing “item catalog…associated with a specific category within the catalog. Categories within a catalog can include, but are not limited to, item groupings such as departments, item types, context”; and see: paragraph [0085] disclosing “third-party providers and/or third-party content, such as third-party sellers and third-party items, within an interface or associated catalog”); and
training the machine learning model without interaction data to learn model parameters indicative of causal relationships between purchases and the user characteristics and user interaction history for the set of users in the training population dependent on the product characteristics and the retailer characteristics (Bhardwaj, see: paragraph [0084] disclosing “user interactions, and/or other parameters. For example, additional signals, such as search query data, co-bought item data, etc. can be incorporated into a GCS model”).
Claim 5-
Bhardwaj in view of Wu teach the method of claim 3, as disclosed above.
Bhardwaj discloses:
wherein the machine learning model used to generate for the cold start results is different from a machine learning model used to generate the standard results, the machine learning model used to generate for the cold start results does not use interaction data for the standard results (Bhardwaj, see: paragraph [0084] disclosing “leveraging multi-hop relationships across edges between cold-cold and cold-hot item pairs” and “disclosed GCS models are scalable”), and
the machine learning model used to generate results uses the interaction data in the generation for the standard results (Bhardwaj, see: paragraph [0084] disclosing “disclosed systems and methods are extendible to additional attribute features, user interactions, and/or other parameters. For example, additional signals, such as search query data, co-bought item”).
Bhardwaj does not disclose:
the probability of conversion;
the generation of the probability of conversion;
Wu, however, does teach:
the probability of conversion (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule”; and Col. 17, ln. 27-29 disclosing “Performing…calculation, the value of P(S and !T), which refers to the probability that a user purchases a 3-D LED TV and a TV Accessory other than 3D glasses is 0.2.”);
the generation of the probability of conversion (Wu, see: Col 16, ln. 33-37 disclosing score generated for the recommendation rule”; and Col. 17, ln. 27-29 disclosing “Performing…calculation, the value of P(S and !T), which refers to the probability that a user purchases a 3-D LED TV and a TV Accessory other than 3D glasses is 0.2.”).
This step of Wu is applicable to the method of Bhardwaj, as they both share characteristics and capabilities, namely, they are directed to recommending items with little or no interaction data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bhardwaj to include the features of the probability of conversion and the generation of the probability of conversion, as taught by Wu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the method of Bhardwaj to improve item recommendations by providing recommendations that would not previously have been included for a customer to potentially purchase (Wu, see: Col. 2, ln. 57-65).
Claim 6-
Bhardwaj in view of Wu teach the method of claim 1, as described above.
Bhardwaj discloses: wherein causing at least a subset of the final set of cold start results to be presented with at least a subset of the set of standard results for display to the user comprises causing at least the subset of the final set of cold start results to be presented with the set of standard results in at least one of a grid or list for display to the user based on the ranking (Bhardwaj, see: paragraph [0079] disclosing “set of recommended items 272 is output from the recommendation engine” and “include a set of the top ranked N items”; and paragraph [0080] disclosing “interface generation engine 254 can be configured to generate an interface including all or a subset of the items included in the set of recommended items 272”; and see: Claim 8 disclosing “set of interface items is selected in descending rank order from the set of ranked items”).
Claim 7-
Bhardwaj in view of Wu teach the method of claim 1, as described above.
Bhardwaj discloses:
wherein identifying a candidate set of cold start results to the query comprises identifying cold start results that have been presented to the user in the previous time period, and that have received no interaction from the user within the previous time period (Bhardwaj, see: paragraph [0058] disclosing “request can include a search identifying the anchor item 256 and/or the anchor item 256 can be identified by a search engine”; and pargraph [0059] disclosing “generate a set of combined candidate items 264 including cold-start candidate items 266, e.g., items having no or little interaction data”).
Bhardwaj does not disclose:
presented to the user less than the threshold number of times in the previous time period,
Wu, however, does teach:
presented to the user less than the threshold number of times in the previous time period (Wu, see: Col. 19, ln. 64-65 disclosing “a cold start recommendation” and “identify items to recommend for an item that has no access history or less than a threshold amount of access history”)
This step of Wu is applicable to the method of Bhardwaj, as they both share characteristics and capabilities, namely, they are directed to recommending items with little or no interaction data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bhardwaj to include the features of presented to the user less than the threshold number of times in the previous time period, as taught by Wu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the method of Bhardwaj to improve item recommendations by providing recommendations that would not previously have been included for a customer to potentially purchase (Wu, see: Col. 2, ln. 57-65).
Claim 8-
Bhardwaj in view of Wu teach the method of claim 7, as disclosed above.
Bhardwaj discloses:
wherein the online system is an online concierge system, each of the cold start results and the standard results correspond to an item available for purchase by the user through a multi-retailer marketplace provided by the online concierge system (Bhardwaj, see: pargraph [0057] disclosing “can include an item selected from a catalog of items available for purchase on the e-commerce interface”; and see: paragraph [0085] disclosing “third-party sellers and third-party items, within an interface or associated catalog”), and
wherein at least one of the cold start results is at least one of new to the online concierge system or being offered for purchase by a retailer that is at least one of new to the user or new to the online concierge system (Bhardwaj, see: paragraph [0025] disclosing “The GCS model is configured to provide cold-start recommendations for new, unviewed, or under-utilized items in a catalog”).
Regarding claim 9, claim 9 is directed to a product of manufacture. Claim 9 recites limitations that are similar in nature to those addressed above for claim 1, which is directed towards a method. It is noted that claim 9 additionally recites the features of a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing steps, also disclosed by Bhardwaj (Bhardwaj, see: paragraph [0039] disclosing “at least one non-transitory computer-readable storage medium is provided having computer-executable instructions”). Claim 9 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 10, claim 10 is directed to a product of manufacture. Claim 10 recites limitations that are parallel in nature to those addressed above for claim 2, which is directed towards a method. Claim 10 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 11, claim 11 is directed to a product of manufacture. Claim 11 recites limitations that are similar in nature to those addressed above for claim 3, which is directed towards a method. It is noted that claim 11 recites non-cold start results, disclosed by Bhardwaj (Bhardwaj, paragraph [0059] disclosing “hot candidate items 268, e.g., items that have high or current interaction data”). Claim 11 is therefore rejected for the same reasons as set forth above for claim 3.
Regarding claim 12, claim 12 is directed to a product of manufacture. Claim 12 recites limitations that are parallel in nature to those addressed above for claim 4, which is directed towards a method. Claim 12 is therefore rejected for the same reasons as set forth above for claim 4.
Regarding claim 13, claim 13 is directed to a product of manufacture. Claim 13 recites limitations that are parallel in nature to those addressed above for claim 5, which is directed towards a method. Claim 13 is therefore rejected for the same reasons as set forth above for claim 5.
Regarding claim 14, claim 14 is directed to a product of manufacture. Claim 14 recites limitations that are parallel in nature to those addressed above for claim 7, which is directed towards a method. Claim 14 is therefore rejected for the same reasons as set forth above for claim 7.
Regarding claim 15, claim 15 is directed to a product of manufacture. Claim 15 recites limitations that are parallel in nature to those addressed above for claim 8, which is directed towards a method. Claim 15 is therefore rejected for the same reasons as set forth above for claim 8.
Regarding claim 16, claim 16 is directed to a system. Claim 16 recites limitations that are parallel in nature to those addressed above for claim 1, which is directed towards a method. Claim 16 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 17, claim 17 is directed to a system. Claim 17 recites limitations that are parallel in nature to those addressed above for claim 2, which is directed towards a method. Claim 17 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 18, claim 18 is directed to a system. Claim 18 recites limitations that are parallel in similar to those addressed above for claim 3, which is directed towards a method. It is noted that claim 18 recites non-cold start results, disclosed by Bhardwaj (Bhardwaj, paragraph [0059] disclosing “hot candidate items 268, e.g., items that have high or current interaction data”). Claim 18 is therefore rejected for the same reasons as set forth above for claim 3.
Regarding claim 19, claim 19 is directed to a system. Claim 19 recites limitations that are parallel in nature to those addressed above for claim 4, which is directed towards a method. Claim 19 is therefore rejected for the same reasons as set forth above for claim 4.
Regarding claim 20, claim 20 is directed to a system. Claim 20 recites limitations that are parallel in nature to those addressed above for claim 5, which is directed towards a method. Claim 20 is therefore rejected for the same reasons as set forth above for claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Saurav, K. (PGP No. US 2025/0094898 A1), describes systems and methods for item-seller recommendation for assortment growth.
Non-patent literature (NPL), document, titled Improving the Performance of Cold-Start Recommendation by Fusion of Attention Network and Meta-Learning, published on MDPI website, in the Journal of Electronics (2023), describes a new cold start recommendation model, combining an attention mechanism and a meta learning mechanism to improve performance for these recommendation models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY PRESTON whose telephone number is (571)272-4399. The examiner can normally be reached M-F 9-5.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688