DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 03/16/2026.
• Claims 1-7, 11-16, and 20 have been amended.
• Claims 8-10 and 17-19 have been canceled.
• Claims 1-7, 11-16, and 20 are currently pending and have been examined.
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 1-7, 11-16, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-7 and 11-12 are directed to a process, claims 13-16 are directed to a manufacture, and claim 20 is directed to a machine. Therefore, claims 1-7, 11-16, and 20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
receiving a search query associated with a user, information about the search query communicated;
searching, using the search query, maintains information about a plurality of items to retrieve a set of candidate items;
accessing a multi-objective ranking model to generate a plurality of weights for each candidate item in the set of candidate items, each of the plurality of weights associated with a respective objective of a plurality of objectives;
applying the multi-objective ranking model to the search query and one or more features of the user to generate the plurality of weights for each candidate item in the set of candidate items;
accessing a revenue adjustment model to adjust a weight of the plurality of weights that is associated with a revenue objective of the plurality of objectives;
applying the revenue adjustment model to content of a cart of the user for a current order and information about a defined number of previous searched conducted by the user to generate the adjusted weight for each candidate item in the set of candidate items;
generating a ranking score for each candidate item in the set of candidate items by applying the plurality of weights comprising the adjusted weight to a plurality of objective scores, each of the plurality of objective scores associated with the respective objective of the plurality of objectives;
selecting, using the ranking score for each candidate item, one or more items from the set of candidate items; and
causing to display the one or more items for recommendation to the user for inclusion in a cart.
The above limitations recite the concept of recommending items for purchase based on an objective analysis. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the providing of recommendations for inclusion in a cart represents marketing and sales behaviors. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the analysis of data to determine recommendations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Claims 13 and 20 recite the same abstract ideas as claim 1 and accordingly fall within the same grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 13, and 20 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 13, and 20 recite additional elements, such as a computer system comprising a processor and a computer-readable medium; a network; a device; a search interface; executing an application running on the device; an application programming interface; a database; multi-objective ranking model, wherein the multi-objective ranking model is a machine-learning model trained; a revenue adjustment model, wherein the revenue adjustment model a machine-learning model trained; a user interface; a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps; and a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 13, and 20 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 13, and 20 merely recite a commonplace business method (i.e., comparing data and vectors to determine recommendations) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 13, and 20 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 13, and 20 specifying that the abstract idea of recommending items for purchase based on an objective analysis is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 13, and 20 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 13, and 20 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 13, and 20 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 13, and 20, these claims recite additional elements, such as a computer system comprising a processor and a computer-readable medium; a network; a device; a search interface; executing an application running on the device; an application programming interface; a database; multi-objective ranking model, wherein the multi-objective ranking model is a machine-learning model trained; a revenue adjustment model, wherein the revenue adjustment model a machine-learning model trained; a user interface; a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps; and a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 13, and 20 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 13, and 20 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 13, and 20 specifying that the abstract idea of recommending items for purchase based on an objective analysis is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 13, and 20 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 13, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-7, 11-12, and 14-16, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-7, 11-12, and 14-16further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Further, these claims, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 3-5, 7, 12, and 15 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 2, 6, 11, 14, and 16 further identify additional elements, such as a budget prediction computer model trained, browsing activity, re-training the revenue adjustment computer model. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-7, 11-12, and 14-16 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 13, and 20, dependent claims 2-7, 11-12, and 14-16 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-7, 11-16, and 20 are ineligible.
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 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.
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, 6-7, 11, 13, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Sewak et al. (US 20190355041 A1), hereafter Sewak, in view of newly cited Li et al. (US 20220269678 A1), hereinafter Li, in view of previously cited Kirkby et al. (US 20140067597 A1), hereinafter Kirkby.
In regards to claim 1, Sewak discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (Sewak: [0003]; [0005])
receiving, via a network and from a device associated with a user of the computer system, a search query entered via a search interface of the computer system when executing an application running on the device associated with the user, information about the search query communicated to the computer system using an application programming interface of the computer system (Sewak: [0041] – “users may access online shopping applications or websites to search for and/or to purchase fashion products”; [0037] and Fig. 1 – “Users 46, 48, 50, 52 may access computing device 12 and cognitive fashion product recommendation process 10 (e.g., using one or more of client electronic devices 38, 40, 42, 44) directly or indirectly through network 14”; [0033] – Examples of client applications 22, 24, 26, 28 may include, but are not limited to, applications that receive queries to search for content from…a textual and/or a graphical user interface…an Application Programming Interface (API)”);
searching a database of the computer system that maintains information about a plurality of items to retrieve a set of candidate items in response to the search query (Sewak: [0040] – “cognitive fashion product recommendation process 10 may associate 200 one or more fashion products on a website with a user accessing the website. One or more recommendations may be provided 202 to the user for fashion products based upon, at least in part, one or more fashion-ability scores representative of the one or more fashion products associated with the user on the web site”; [0086] – “product recommendation process 10 may identify 724 fashion products for recommending 202 to the user with higher fashion-ability scores from the same category or sub-category of fashion products on the second website”; [0041] – “providing one or more recommendations, on a website accessed by a user, for fashion products…users may access online shopping applications or websites to search for and/or to purchase fashion products”; [0046-0047] – “cognitive fashion product recommendation process 10 may associate 200 one or more fashion products on a website with a user accessing the website by processing a user's browsing history of the website…record or otherwise monitor the user's browsing history to determine which fashion products the user…searches for”; see also [0052]; [0057]; the examiner notes the scored items on the website are interpreted to be a set of candidate items);
accessing a multi-objective ranking model, wherein the multi-objective ranking model is a machine-learning model trained to generate a plurality of weights for the set of candidate items, each of the plurality of weights associated with a respective objective of a plurality of objectives (Sewak: [0079] – “cognitive fashion product recommendation process 10 may assign different weights to the one or more recommendations based upon, at least in part, how the recommendation is generated. For example and as discussed above, cognitive fashion product recommendation process 10 may generate or provide recommendations to the user for fashion products on the website based upon, at least in part, one or more marketing objectives associated with cognitive fashion product recommendation process 10. A marketing objective as implemented by cognitive fashion product recommendation process 10 may define how various weights are assigned to the one or more recommendations for fashion products on the website…to emphasize e.g., customer engagement on a website as a marketing objective. This marketing objective may include the goal of increasing the number of users accessing the website with less emphasis on converting the user activity into immediate purchases. In this example, cognitive fashion product recommendation process 10 may assign a first weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were viewed by the user on the website. Additionally, cognitive fashion product recommendation process 10 may assign a second weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were added by the user to a wish list (e.g., wish list 328), where the second weight is greater than the first weight”; [0088] – “weights from…a neural network model 738”; [0058] – “cognitive fashion product recommendation process 10 may store the trained neural network in a repository or other data structure”);
applying the multi-objective ranking model to the search query and one or more features of the user to generate the plurality of weights for the set of candidate items (Sewak: [0079] – “cognitive fashion product recommendation process 10 may assign different weights to the one or more recommendations based upon, at least in part, how the recommendation is generated. For example and as discussed above, cognitive fashion product recommendation process 10 may generate or provide recommendations to the user for fashion products on the website based upon, at least in part, one or more marketing objectives associated with cognitive fashion product recommendation process 10. A marketing objective as implemented by cognitive fashion product recommendation process 10 may define how various weights are assigned to the one or more recommendations for fashion products on the website…to emphasize e.g., customer engagement on a website as a marketing objective. This marketing objective may include the goal of increasing the number of users accessing the website with less emphasis on converting the user activity into immediate purchases. In this example, cognitive fashion product recommendation process 10 may assign a first weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were viewed by the user on the website. Additionally, cognitive fashion product recommendation process 10 may assign a second weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were added by the user to a wish list (e.g., wish list 328), where the second weight is greater than the first weight”; [0046-0047] – “cognitive fashion product recommendation process 10 may associate 200 one or more fashion products on a website with a user accessing the website by processing a user's browsing history of the website…record or otherwise monitor the user's browsing history to determine which fashion products the user…searches for”);
accessing a revenue adjustment model, wherein the revenue adjustment model is a machine-learning model trained to adjust a weight of the plurality of weights that is associated with a revenue objective of the plurality of objectives (Sewak: [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting”; [0058] – “cognitive fashion product recommendation process 10 may store the trained neural network in a repository or other data structure”);
applying the revenue adjustment model to content of a cart of the user, to generate the adjusted weight for each candidate item in the set of candidate items (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting”);
generating a ranking score for each candidate item in the set of candidate items by applying the plurality of weights comprising the adjusted weight (Sewak: [0082] – “the fashion browsing sequence may provide a ranking or priority by which cognitive fashion product recommendation process 10 provides recommendations for one or more fashion products on the website”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting”);
selecting, using the ranking score for each candidate item, one or more items from the set of candidate items (Sewak: [0082] – “the fashion browsing sequence may provide a ranking or priority by which cognitive fashion product recommendation process 10 provides recommendations for one or more fashion products”; [0090] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the digital advertisements of the one or more fashion products on the second website…cognitive fashion product recommendation process 10 may provide 802 the next highest priority or fashion product recommendation with the next highest weight”); and
causing the device associated with the user to display a user interface with the one or more items for recommendation to the user for inclusion in a cart (Sewak: [0082] – “the fashion browsing sequence may provide a ranking or priority by which cognitive fashion product recommendation process 10 provides recommendations for one or more fashion products”; [0090] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the digital advertisements of the one or more fashion products on the second website…cognitive fashion product recommendation process 10 may provide 802 the next highest priority or fashion product recommendation with the next highest weight”; see also [0037] and Fig. 1).
Sewak further discloses weights applied to various items (Sewak: [0079]), yet Sewak does not explicitly disclose searching, using the search query; a plurality of weights for each candidate item in the set; a cart for a current order; applying a model to information about a defined number of previous searched conducted by the user; and applying weights to a plurality of objective scores, each of the plurality of objective scores associated with the respective objective of the plurality of objectives.
However, Li teaches a similar recommender system (Li: [0021]), including
searching, using the search query (Li: [0023] – “Based on the additional user input and the initial search term, the system executes a search and filtering operation on the data corpus of information items to determine a subset of the information items to present to the user”); and
applying a model to information about a defined number of previous searched conducted by the user (Kirkby: [0043] – “the historical query information 130 may be limited to a predefined number of queries (e.g., the 100 most recently submitted queries) and/or a particular time period (e.g., queries submitted in the preceding six months)”; [0021] – “train a machine learning model based on historical query data input to recommend”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the searching a query and predefined number of Li in the method of Sewak because Sewak already discloses an analysis using queried and Li is merely demonstrating how this may be done. Additionally, it would have been obvious to have included searching, using the search query; a plurality of weights for each candidate item in the set; a cart for a current order; applying a model to information about a defined number of previous searched conducted by the user as taught by Li because queries are well-known and the use of it in a recommender system would have reduced a number of information elements (Li: [0002]).
Additionally, Kirkby teaches a similar recommender system (Kirkby: [abstract]), including
a plurality of weights for each candidate item in the set (Kirkby: [0028] – “Product attributes along with product ratings (if available) or sales can be used to perform attribute weight analysis and estimate the importance of every attribute to the revenue of the retailer”; [0059] – “a scoring function is described above that takes into consideration w.sub.price and w.sub.geo which are nonlinear weighting factors that take into account the relative price of items and location”; [0021] – “the recommendation indices identify…one or more products to be recommended”; see also [0044]);
a cart for a current order (Kirkby: [0065] – “the dynamic data 103 is received which indicates…products in a shopping cart”): and
applying weights to a plurality of objective scores, each of the plurality of objective scores associated with the respective objective of the plurality of objectives (Kirkby: [0028] – “Product attributes along with product ratings (if available) or sales can be used to perform attribute weight analysis and estimate the importance of every attribute to the revenue of the retailer”; [0059] – “a scoring function is described above that takes into consideration w.sub.price and w.sub.geo which are nonlinear weighting factors that take into account the relative price of items and location”; [0021] – “the recommendation indices identify…one or more products to be recommended”; see also [0044]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the weights and current cart of Kirkby in the method of Sewak/Li because Sewak/Li already discloses weights and a cart analysis and Kirkby is merely demonstrating that there may be multiple weights per item and that a cart may be current. Additionally, it would have been obvious to have included a plurality of weights for each candidate item in the set; a cart for a current order; and applying weights to a plurality of objective scores, each of the plurality of objective scores associated with the respective objective of the plurality of objectives as taught by Kirkby because weights and current carts are well-known and the use of it in a recommender system would have improved recommendations (Kirkby: [0021]).
In regards to claim 6, Sewak/Li/Kirkby teaches the method of claim 1. Sewak further discloses wherein applying the revenue adjustment model comprises: applying the revenue adjustment model further to information about a browsing activity of the user during the current order to generate the adjusted weight associated with the revenue objective (Sewak: [0091] – “in response to the user selecting a fashion product recommendation, cognitive fashion product recommendation process 10 may determine 604 whether the recommendation was among the first or highest priority fashion product recommendations. In some embodiments, cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations to prioritize the fashion product recommendation that was actually selected by the user and/or to modify to the sequence of fashion product recommendations based upon, at least in part, the fashion product recommendations actually selected by the user. For example, cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation”).
In regards to claim 7, Sewak/Li/Kirkby teaches the method of claim 1. Sewak further discloses
wherein applying the multi-objective ranking model comprises: applying the multi-objective ranking model to generate, for the set of candidate items, the plurality of weights each associated with a relevance objective, the revenue objective, an availability objective and a repeat purchasability objective of the plurality of objectives (Sewak: [0079] – “cognitive fashion product recommendation process 10 may assign different weights to the one or more recommendations based upon, at least in part, how the recommendation is generated. For example and as discussed above, cognitive fashion product recommendation process 10 may generate or provide recommendations to the user for fashion products on the website based upon, at least in part, one or more marketing objectives associated with cognitive fashion product recommendation process 10. A marketing objective as implemented by cognitive fashion product recommendation process 10 may define how various weights are assigned to the one or more recommendations for fashion products on the website…to emphasize e.g., customer engagement on a website as a marketing objective. This marketing objective may include the goal of increasing the number of users accessing the website with less emphasis on converting the user activity into immediate purchases. In this example, cognitive fashion product recommendation process 10 may assign a first weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were viewed by the user on the website. Additionally, cognitive fashion product recommendation process 10 may assign a second weight to recommendations for fashion products that are similar (e.g., based upon, at least in part, the fashion-ability scores) to those fashion products that were added by the user to a wish list (e.g., wish list 328), where the second weight is greater than the first weight”; the examiner interprets the objectives to be a relevance objective, the revenue objective, an availability objective and a repeat purchasability objective of the plurality of objectives),
yet Sewak does not explicitly disclose generating a plurality of weights each candidate item in the set; where the weights are associated with a respective one of each objective.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
generating a plurality of weights each candidate item in the set; where the weights are associated with a respective one of each objective (Kirkby: [0028] – “Product attributes along with product ratings (if available) or sales can be used to perform attribute weight analysis and estimate the importance of every attribute to the revenue of the retailer”; [0059] – “a scoring function is described above that takes into consideration w.sub.price and w.sub.geo which are nonlinear weighting factors that take into account the relative price of items and location”; [0021] – “the recommendation indices identify…one or more products to be recommended”; see also [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak/Li for the reasons identified above with respect to claim 1.
In regards to claim 11, Sewak/Li/Kirkby teaches the method of claim 1. Sewak further discloses collecting feedback data with information about a conversion by the user of each of the one or more items; and re-training the revenue adjustment model by updating, using the collected feedback data, a set of parameters of the revenue adjustment model (Sewak: [0091] – “in response to the user selecting a fashion product recommendation, cognitive fashion product recommendation process 10 may determine 604 whether the recommendation was among the first or highest priority fashion product recommendations. In some embodiments, cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations to prioritize the fashion product recommendation that was actually selected by the user and/or to modify to the sequence of fashion product recommendations based upon, at least in part, the fashion product recommendations actually selected by the user. For example, cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation”; [0042] – “fashion-ability scores may be generated by processing the image(s) of one or more fashion products using a neural network and by training the neural network with one or more attributes associated with the fashion product”).
In regards to claim 13, claim 13 is directed to a medium. Claim 13 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Sewak/Li/Kirkby teaches the limitations of claim 1 as noted above. Sewak further discloses a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising (Sewak: [0005]). Claim 13 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 16, all the limitations in medium claim 16 are closely parallel to the limitations of method claim 6 analyzed above and rejected on the same bases.
In regards to claim 20, claim 20 is directed to a system. Claim 20 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Sewak/Li/Kirkby teaches the limitations of claim 1 as noted above. Sewak further discloses a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising (Sewak: [0005]). Claim 13 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
Claims 2-3 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sewak, in view of Li, in view of Kirkby, in view of previously cited Martin (US 20160292773 A1), hereinafter Martin.
In regards to claim 2, Sewak/Li/Kirkby teaches the method of claim 1. Sewak further discloses
an order history of the user for a defined time period (Sewak: [0049] – “record or otherwise monitor the user's browsing history (e.g., in the form of cookies or other storage mechanisms) and may process the browsing history after a pre-defined period of time”; [0047] – “monitor the user's browsing history to determine which fashion products the user views, selects, searches for, adds to the shopping cart (e.g., shopping cart 326), adds to the wish list (e.g., wish list 328), and/or purchases from the website),
wherein applying the revenue adjustment model comprises applying the revenue adjustment model further to generate the adjusted weight for each candidate item in the set of candidate items (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting”).
Yet Sewak does not explicitly disclose accessing a budget prediction model, wherein the budget prediction model is trained to predict a budget for the current order; applying the budget prediction model to information about an order history to estimate the budget for the current order; and information being the estimated budget for the current order.
However, Martin teaches a similar recommendation method (Martin: [0038]), including
accessing a budget prediction model, wherein the budget prediction model is trained to predict a budget for the current order; applying the budget prediction model to information about an order history to estimate the budget for the current order (Martin: [0052] – “after performing some pricing analysis (for the multiple items or item types specified in the budget order request) based on historical data available from the networked system 102, the budget purchasing system 150 may generate a budget estimate for the budget order request”); and
information being the estimated budget for the current order (Martin: [0038] – “a recommendation module that is configured to generate a recommendation based on the estimated budget generated by the budget module”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the budget of Martin in the method of Sewak/Li/Kirkby because Sewak/Li/Kirkby already discloses price information and Martin is merely demonstrating that this may be a budget. Additionally, it would have been obvious to have included accessing a budget prediction model, wherein the budget prediction model is trained to predict a budget for the current order; applying the budget prediction model to information about an order history to estimate the budget for the current order; and information being the estimated budget for the current order as taught by Martin because budgets are well-known and the use of it in a recommendation setting would have provided relevant recommendations (Martin: [0105]).
In regards to claim 3, Sewak/Li/Kirkby/Martin teaches the method of claim 2. Sewak further discloses
wherein applying the revenue adjustment model further comprises: applying the revenue adjustment model to generate the adjusted weight associated with the revenue objective (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting),
yet Sewak does not explicitly disclose applying to a decay function of the weight, a total monetary value of the content of the cart and the estimated budget for the current order.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
applying to a decay function of the weight (Kirkby: [0044] – “For example, a scoring function may include the following: scoring ( i k .fwdarw. i m ) = w price w geo .times. UniqueUser ( i k , i m ) .times. m = 0 M ( t m - t 0 t 1 - t 0 ) n ##EQU00004## with w.sub.price and w.sub.geo being nonlinear weighting factors that take into account the relative price of items i.sub.k and i.sub.m and also the relative distance between the place where the purchase of the pair takes place and the location where the recommendations are going to be provided…n is an exponential that determines the effect of a timeliness of a transaction.. . .The larger the value of n, the less important historical transactions become”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak for the reasons identified above with respect to claim 1.
Additionally, Martin teaches a similar recommendation method (Martin: [0038]), including
a total monetary value of the content of the cart and the estimated budget for the current order (Martin: [0052] – “after performing some pricing analysis (for the multiple items or item types specified in the budget order request) based on historical data available from the networked system 102, the budget purchasing system 150 may generate a budget estimate for the budget order request”; [0118] and Fig. 8B – “FIG. 8B illustrates another example of an estimated budget table 810. For this garden example, the estimated budget table 810 is associated with the budget order request table 800 (FIG. 8A) and the budget order request record 620 (FIG. 6C). The budget order request associated with the budget id 168902 specifies a total of 5 items representing three different item types. The item types shown in the estimated budget table 810 include planters, garden shears, and planting soil. The estimated current pricing is shown for each of the five items. The estimated budget for all five items is $50.00, which is the sum of the estimated current pricing for all of the five items”; see also [0068]; [0135]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Martin with Sewak/Li/Kirkby for the reasons identified above with respect to claim 2.
In regards to claim 14, all the limitations in medium claim 14 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 15, Sewak/Li/Kirkby/Martin teaches the medium of claim 14. Sewak further discloses
wherein the instructions further cause the processor to perform steps comprising: applying the revenue adjustment model to generate the adjusted weight associated with the revenue objective (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting),
yet Sewak does not explicitly disclose applying to a defined function of the weight, a total monetary value of the content of the cart and the estimated budget for the current order.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
applying to a defined function of the weight (Kirkby: [0044] – “For example, a scoring function may include the following: scoring ( i k .fwdarw. i m ) = w price w geo .times. UniqueUser ( i k , i m ) .times. m = 0 M ( t m - t 0 t 1 - t 0 ) n ##EQU00004## with w.sub.price and w.sub.geo being nonlinear weighting factors that take into account the relative price of items i.sub.k and i.sub.m and also the relative distance between the place where the purchase of the pair takes place and the location where the recommendations are going to be provided…n is an exponential that determines the effect of a timeliness of a transaction.. . .The larger the value of n, the less important historical transactions become”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak for the reasons identified above with respect to claim 1.
Additionally, Martin teaches a similar recommendation method (Martin: [0038]), including
a total monetary value of the content of the cart and the estimated budget for the current order (Martin: [0052] – “after performing some pricing analysis (for the multiple items or item types specified in the budget order request) based on historical data available from the networked system 102, the budget purchasing system 150 may generate a budget estimate for the budget order request”; [0118] and Fig. 8B – “FIG. 8B illustrates another example of an estimated budget table 810. For this garden example, the estimated budget table 810 is associated with the budget order request table 800 (FIG. 8A) and the budget order request record 620 (FIG. 6C). The budget order request associated with the budget id 168902 specifies a total of 5 items representing three different item types. The item types shown in the estimated budget table 810 include planters, garden shears, and planting soil. The estimated current pricing is shown for each of the five items. The estimated budget for all five items is $50.00, which is the sum of the estimated current pricing for all of the five items”; see also [0068]; [0135]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the budget of Martin in the method of Sewak/Li/Kirkby because Sewak/Li/Kirkby already discloses price information and Martin is merely demonstrating that this may be a budget. Additionally, it would have been obvious to have included a total monetary value of the content of the cart and the estimated budget for the current order as taught by Martin because budgets are well-known and the use of it in a recommendation setting would have provided relevant recommendations (Martin: [0105]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Sewak, in view of Li, in view of Kirkby, in view of Martin, in view of previously cited Olbrich et al. (US 20220155940 A1), hereinafter Olbrich.
In regards to claim 4, Sewak/Li/Kirkby/Martin teaches the method of claim 2. Sewak further discloses
wherein applying the revenue adjustment computer model further comprises: applying the revenue adjustment model to generate the adjusted weight associated with the revenue objective (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting),
yet Sewak does not explicitly disclose applying to a linear function of the weight, a total monetary value of the content of the cart and the estimated budget for the current order.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
applying to a function of the weight (Kirkby: [0044] – “For example, a scoring function may include the following: scoring ( i k .fwdarw. i m ) = w price w geo .times. UniqueUser ( i k , i m ) .times. m = 0 M ( t m - t 0 t 1 - t 0 ) n ##EQU00004## with w.sub.price and w.sub.geo being nonlinear weighting factors that take into account the relative price of items i.sub.k and i.sub.m and also the relative distance between the place where the purchase of the pair takes place and the location where the recommendations are going to be provided…n is an exponential that determines the effect of a timeliness of a transaction.. . .The larger the value of n, the less important historical transactions become”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak for the reasons identified above with respect to claim 1.
Additionally, Martin teaches a similar recommendation method (Martin: [0038]), including
a total monetary value of the content of the cart and the estimated budget for the current order (Martin: [0052] – “after performing some pricing analysis (for the multiple items or item types specified in the budget order request) based on historical data available from the networked system 102, the budget purchasing system 150 may generate a budget estimate for the budget order request”; [0118] and Fig. 8B – “FIG. 8B illustrates another example of an estimated budget table 810. For this garden example, the estimated budget table 810 is associated with the budget order request table 800 (FIG. 8A) and the budget order request record 620 (FIG. 6C). The budget order request associated with the budget id 168902 specifies a total of 5 items representing three different item types. The item types shown in the estimated budget table 810 include planters, garden shears, and planting soil. The estimated current pricing is shown for each of the five items. The estimated budget for all five items is $50.00, which is the sum of the estimated current pricing for all of the five items”; see also [0068]; [0135]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Martin with Sewak/Kirkby for the reasons identified above with respect to claim 2.
Further, Olbrich teaches a similar recommendation method (Olbrich: [0025]), including
that a function is a linear function (Olbrich: [0074] – “calculate a gradient used for determining the weights for the neural network…Various activation functions can be used as appropriate…Activation functions can also be linear ”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the linear function of Olbrich in the method of Sewak/Li/Kirkby/Martin because Sewak/Li/Kirkby/Martin already discloses a neural network and Olbrich is merely demonstrating that there may be a linear function. Additionally, it would have been obvious to have included that a function is a linear function as taught by Olbrich because linear functions are well-known and the use of it in a recommendation setting would have improved user experience (Olbrich: [0017]).
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sewak, in view of Li, in view of Kirkby, in view of previously cited Hill et al. (US 20240370940 A1), hereinafter Hill.
In regards to claim 5, Sewak/Li/Kirkby teaches the method of claim 1. Sewak further discloses
wherein applying the revenue adjustment model comprises applying the revenue adjustment model to generate the adjusted weight for each candidate item in the set of candidate items (Sewak: [0041] – “a user may select certain fashion products to place in a ‘shopping cart’”; [0091] – “cognitive fashion product recommendation process 10 may modify the weights assigned to the one or more fashion product recommendations…cognitive fashion product recommendation process 10 may compute 810 the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process 10 may compute 812 a new delta and/or change momentum and may update 814 the neural network model if the neural network model has the higher relative weighting”).
Yet Sewak does not explicitly disclose generating, using information about an order history of the user, an average budget for the user for a defined time period; and estimating, using the average budget, a budget for the current order; and applying to the estimated budget for the current order.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
information for the current order (Kirkby: [0065] – “the dynamic data 103 is received which indicates…products in a shopping cart”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak for the reasons identified above with respect to claim 1.
However, Hill teaches a similar transaction method (Hill: [abstract]), including
generating, using information about an order history of the user, an average budget for the user for a defined time period (Hill: [0055] – “reporting engine 140 may calculate, based on the pulled historical data, an estimated amount of spending over a period of time (e.g., price per day, per week, etc.). For example, the reporting engine 140 may calculate, based on an historical data for a specific location, an average amount of spending for the period of time”); and
estimating, using the average budget, a budget (Hill: [0055] – “the reporting engine 140 may determine, based on the average amount of spending, a recommended budget. By way of example, the reporting engine 140 may determine an average daily amount spent by previous users at a specified location (e.g., based on average and/or historical spending habits for the location). The reporting engine 140 may recommend a daily budget corresponding to the average daily amount. In some implementations, the reporting engine 140 may integrate known expenses into the recommended budget. For example, the reporting engine 140 may calculate the budget based on a weekly spending amount for a location by subtracting the cost of known expenses (e.g., the cost of airfare, lodging, etc.)”);
applying to the estimated budget (Hill: [0057] – “The recommendation 530 may include, but is not limited to, a total estimated budget, a recommended vendor (e.g., airline, hotel, etc.), and/or spending limits. For example, as described herein, the reporting engine 140 may request alternative dates and/or locations responsive to determining one or more data elements exceeds a threshold.”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the average budget of Hill in the method of Sewak/Li/Kirkby because Sewak/Li/Kirkby already discloses price information and Hill is merely demonstrating that this may be a budget. Additionally, it would have been obvious to have included generating, using information about an order history of the user, an average budget for the user for a defined time period; and estimating, using the average budget, a budget; and applying to the estimated budget as taught by Hill because budgets are well-known and the use of it in a recommendation setting would have increased efficiency and effectiveness (Hill: [0021]).
In regards to claim 12, Sewak/Li/Kirkby teaches the method of claim 1.
Yet Sewak does not explicitly disclose wherein displaying the user interface comprises: computing, using information about an order history of the user, an average budget for the user for a defined time period; and causing the device associated with the user to display the user interface further with a difference between the average budget and a total monetary value of the content of the cart.
However, Kirkby teaches a similar recommendation method (Kirkby: [abstract]), including
information of the content of the cart (Kirkby: [0065] – “the dynamic data 103 is received which indicates…products in a shopping cart”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kirkby with Sewak for the reasons identified above with respect to claim 1.
However, Hill teaches a similar transaction method (Hill: [abstract]), including
wherein displaying the user interface comprises: computing, using information about an order history of the user, an average budget for the user for a defined time period (Hill: [0055] – “reporting engine 140 may calculate, based on the pulled historical data, an estimated amount of spending over a period of time (e.g., price per day, per week, etc.). For example, the reporting engine 140 may calculate, based on an historical data for a specific location, an average amount of spending for the period of time”); and
causing the device associated with the user to display the user interface further with a difference between the average budget and a total monetary value (Hill: [0057] – “the reporting engine 140 may pull data from the travel database 115 indicating a set weekly budget (e.g., over 7 days) of $1000 for a location. The reporting engine 140 may determine, based on the received travel data via the third party API, a lowest price of transportation is $500. The reporting engine 140 may determine a daily spending limit of $70 to maintain within the set budget. The reporting engine 140 may transmit a notification to the customer device 160 indicating the budget”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the budget of Hill in the method of Sewak/Li/Kirkby because Sewak/Li/Kirkby already discloses price information and Hill is merely demonstrating that this may be a budget. Additionally, it would have been obvious to have included wherein displaying the user interface comprises: computing, using information about an order history of the user, an average budget for the user for a defined time period; and causing the device associated with the user to display the user interface further with a difference between the average budget and a total monetary value as taught by Hill because budgets are well-known and the use of it in a recommendation setting would have increased efficiency and effectiveness (Hill: [0021]).
Response to Arguments
Applicant’s arguments, filed 03/16/2026, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims are integrated into a practical application because the claims recite “recite specific operations that are rooted in computer system technology and thus impose meaningful limits on practicing the abstract idea” (Remarks pages 12-13). The examiner disagrees. The MPEP sets forth, in Step 2A Prong Two, that a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole “integrates the recited judicial exception into a practical application of that exception.” The evaluation of Prong Two requires the use of the considerations (e.g. improving technology, effecting a particular treatment or prophylaxis, implementing with a particular machine, etc.) identified by the Supreme Court and the Federal Circuit, to ensure that the claim as a whole ‘integrates [the] judicial exception into a practical application [that] will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.’ In the instant case, the claims include additional elements such as a computer system comprising a processor and a computer-readable medium; a network; a device; a search interface; executing an application running on the device; an application programming interface; a database; multi-objective ranking model, wherein the multi-objective ranking model is a machine-learning model trained; a revenue adjustment model, wherein the revenue adjustment model a machine-learning model trained; a user interface; a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps; and a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps. While these elements are recited, they are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of recommending items for purchase based on an objective analysis in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. Applicant’s disclosure does not articulate or suggest how these additional elements function, individually or in combination, in any manner other than using generic functionality nor does the disclosure articulate how the elements provide a technical improvement. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they merely amount to using the software architecture as a tool to perform the abstract idea.
Applicant argues claims 13, 20, and the dependent claims are eligible for the same reasons as claim 1 (Remarks page 13). The examiner disagrees. As discussed in the 101 rejection and response to remarks above, claim 1 is ineligible and claims 13, 20, and the dependent claims are ineligible for the same reasons.
35 U.S.C. § 103
Applicant argues the claims are allowable because the cited art does not teach or disclose “the cognitive fashion product recommendation process utilizes information about a defined number of previous searched conducted by the user to generate an adjusted weight for each fashion product.” (Remarks pages 13-15). The examiner disagrees. Initially, the examiner notes that the amendments have necessitated a new grounds of rejection and a new reference has been cited to teach applying a model to information about a defined number of previous searched conducted by the user. Furthermore, Sewak discloses applying the revenue adjustment model to content of a cart of the user, to generate the adjusted weight for each candidate item in the set of candidate items. Sewak discloses this in [0041] and [0091], disclosing a user may select certain fashion products to place in a ‘shopping cart’, where cognitive fashion product recommendation process may modify the weights assigned to the one or more fashion product recommendation and compute the delta in weights to arrive at weights that give the higher priority towards the actually selected fashion product recommendation. In some embodiments, cognitive fashion product recommendation process may compute a new delta and/or change momentum and may update the neural network model if the neural network model has the higher relative weighting. Accordingly, the cited art teaches this limitation in the claim.
Applicant argues claims 13, 20, and the dependent claims are allowable for the same reasons as claim 1 (Remarks page 15). The examiner disagrees. As discussed in the 103 rejection and response to remarks above, claim 1 is not allowable and claims 13, 20, and the dependent claims are not allowable for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL reference U, initially cited in the Office action dated 01/06/2026, teaches recommendations for products. Products may be recommended based on current items in a cart. Recommendations are personalized to a user.
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 ANNA MAE MITROS whose telephone number is (571)272-3969. The examiner can normally be reached Monday-Friday from 9:30-6.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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