DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 07/06/2026.
• Claims 1-20 are amended.
• Claims 1-20 are currently pending and have been examined.
Priority
The applicant’s claim for benefit of Provisional Patent Application Serial No. 63/620,634 filed 01/12/2024 has been received and acknowledged.
Response to Amendment
In light of Applicant’s amendments, filed on 07/06/2026, the claim objections to claims 8, 13-14, and 18 have been withdrawn.
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 Objections
Claim 16 is objected to because of the following informalities:
With regards to claim 16, the claim being dependent on the “method of claim 8” appears to be a typo. Claim 8 is not a method. The limitation should likely read a dependency on the “method of claim 9.”
Appropriate correction is required.
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 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 1-8 are directed to a machine, claims 9-16 are directed to a method, and claims 17-20 are directed to a manufacture. Therefore, claims 1-20 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:
store historical customer data associated with a customer;
a model, using the historical customer data, to form a model that evaluates a plurality of visual similarities between a plurality of products associated with a corresponding plurality of different product types, wherein each respective product in the plurality of products is associated with at least one corresponding product in the plurality of products based at least in part on a purchase or view history between the respective product and the at least one corresponding product;
receive, from a user, an indication of a customer's selection of a first product;
parse and extract complementary product type data from product data stored, the complementary product type data associated with the first product and including a plurality of complementary product types;
generate, using the model, look data based on the historical customer data, the complementary product type data, and the first product, the look data including the first product and a plurality of different products each from a different complementary product type of the plurality of complementary product types;
receive purchase data indicating purchase, by the customer, of at least one purchased product of the plurality of different products;
input the purchase data into the model, wherein the model compares the at least one purchased product to the plurality of different products to generate a comparison value indicating a deviation of the at least one purchased product from the plurality of different products; and
the model based on the purchase data, wherein the model comprises inputting the comparison value into the model to refine the model to increase an accuracy of the model.
The above limitations recite the concept of creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item. 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, generating a look of products is a sales and marketing activity. This is further illustrated in paragraph [0022] of the Specification, describing the invention relates to recommending products for purchase. 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 receiving of data, analysis, and providing a look are observations, evaluations, and judgements. Independent claims 9 and 17 recite similar limitations as claim 1 and as such, claim 8 falls within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1, 9, and 17 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 9, and 17 recite additional elements, such as a system, comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to; a database; train a machine learning model, a trained machine learning model, a user interface; retraining the trained machine learning model; and a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising. 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, 9, and 17 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, 9, and 17 merely recite a commonplace business method (i.e., creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 9, and 17 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, 9, and 17 specifying that the abstract idea of creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item 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, 9, and 17 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 9, and 17 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 9, and 17 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, 9, and 17, these claims recite additional elements, such as a system, comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to; a database; train a machine learning model, a trained machine learning model, a user interface; retraining the trained machine learning model; and a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising. 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, 9 and 17 are manual processes, e.g., receiving information, analyzing 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, 9, and 17 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 specifying that the abstract idea of creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item 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, 9, and 17 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, 9, and 17 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 2-8, 10-16, and 18-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-8, 10-16, and 18-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. These claims additionally 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 2, 6-8, 10, 14-16, 18, and 20 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 3-5, 11-13, and 19 further identify additional elements, such as a large language model; a ranking engine; and a variant engine. 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 2-8, 10-16, and 18-20 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 9, and 17, dependent claims 2-8, 10-16, and 18-20 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 1-20 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 1-2, 6-10, 14-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Scaff et al. (US 20250054044 A1), hereafter Scaff, in view of newly cited Zalmanson et al. (US 20240005084 A1), hereinafter Zalmanson, in view of newly cited Kuhn et al. (US 20220366474 A1), hereinafter Kuhn.
In regards to claim 1, Scaff discloses a system comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to (Scaff: [0044]; [0106]):
store, in a database, historical customer data associated with a customer (Scaff: [0072] – “one or more styles associated with a user submitting the request are accessed (e.g., from user data of the online marketplace 114)…based on one or more of a purchase history, browsing history, likes, comments, demographics, social media behavior, images uploaded and/or specified by the user…leverage historical style information to curate the outfit(s) 150 in one or more implementations”; [0053] and Fig. 1 – “the online marketplace 114 includes storage device 116…storage device 116 may represent one or more databases and/or other types of storage capable of storing”; [0058] – “storage device 130 may represent one or more databases and/or other types of storage capable of storing the preconfigured text 132 and/or other data used by the generative artificial intelligence 122 to curate outfits in real-time from the clothing items on the online marketplace 114”);
a trained machine learning model that evaluates a plurality of visual similarities between a plurality of products associated with a corresponding plurality of different product types (Scaff: [0065] – “the generative artificial intelligence 122 is trained”; [0064-0065] – “a search to locate complementary clothing items for the outfit that are complementary to the seed clothing item 136 and that are available on the online marketplace 114”; [0036] – “search results contain listings of the complementary clothing items for the outfit that are currently available in the database of listings” [0077] – “the prompt 138 for the generative artificial intelligence 122 excludes a clothing type of the seed clothing item 136. For example, if the seed clothing item is a red shirt, then the prompt 138 asks the generative artificial intelligence 122 to locate clothing items other than shirts which complement the seed clothing item 136”; [0024-0025] – “return…items that complement or otherwise match the style…of the seed…item…a prompt for input to generative artificial intelligence to create an outfit that includes the seed clothing item, the prompt including clothing attributes of the seed clothing item; providing the prompt to the generative artificial intelligence to cause the generative artificial intelligence to initiate a search of an online marketplace to locate complementary clothing items for the outfit”; [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”);
receive, from a user interface, an indication of a customer's selection of a first product (Scaff: [0059] and Fig. 1 – “the automated outfit curation system 106 receives a request 134 to locate clothing items for an outfit. In at least one variation, the request 134 identifies or otherwise indicates a seed clothing item 136. By way of example and not limitation, the request 134 includes an image of the seed clothing item 136, text describing the seed clothing item 136, and/or a selection of the seed clothing item 136, e.g., a selection of a listing in the real-time listing data 118 which lists the seed clothing item 136”; [0050] – “Through interaction of a user with the computing device 102, the application 112 receives user input via one or more user interfaces of the online marketplace 114”);
parse and extract complementary product type data from product data stored within the database, the complementary product type data associated with the first product and comprising a plurality of complementary product types (Scaff: [0064-0065] – “determine the clothing attributes 140 of the seed clothing item 136…the feature description generator 126 may extract the clothing attributes 140…use the obtained data as the clothing attributes 140 for incorporation into the prompt 138 and/or the feature description generator 126 may further process the obtained information to extract the clothing attributes…the automated outfit curation system 106 provides the prompt 138 as input to the generative artificial intelligence 122. Providing the prompt 138 as input to the generative artificial intelligence 122 causes the generative artificial intelligence 122 to initiate a search of the online marketplace 114, such as a search to locate complementary clothing items for the outfit that are complementary to the seed clothing item 136 and that are available on the online marketplace 114”; [0036] – “search results contain listings of the complementary clothing items for the outfit that are currently available in the database of listings” [0077] – “the prompt 138 for the generative artificial intelligence 122 excludes a clothing type of the seed clothing item 136. For example, if the seed clothing item is a red shirt, then the prompt 138 asks the generative artificial intelligence 122 to locate clothing items other than shirts which complement the seed clothing item 136”; [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”);
generate, using the trained machine learning model, look data based on the historical customer data, the complementary product type data, and the first product, the look data including the first product and a plurality of different products each from a different complementary product type of the plurality of complementary product types (Scaff: [0068] – “the complementary clothing item listings 146 are arranged to create outfit(s) 150 for a user interface, where the complementary clothing item listings 146 that are combined to form the outfit(s) 150 are selectable (e.g., to add to a cart and/or purchase). In at least one implementation, the generative artificial intelligence 122 arranges the complementary clothing item listings 146 into one or more outfits, e.g., a number of outfits specified in the prompt 138”; [0065] – “locate complementary clothing items for the outfit that are complementary to the seed clothing item 136”; [0072] – “one or more styles associated with a user submitting the request are accessed (e.g., from user data of the online marketplace 114), and the prompt 138 is generated based at least in part on those one or more styles. For instance, a style or styles of a user may be learned by the online marketplace 114 over time, such as based on one or more of a purchase history, browsing history, likes, comments, demographics, social media behavior, images uploaded and/or specified by the user, determined similar users, and so on. Thus, the automated outfit curation system 106 may, at least in part, leverage historical style information to curate the outfit(s) 150 in one or more implementations”; [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”; [0065] – “the generative artificial intelligence 122 is trained”); and
receive data, by the customer, of at least one product of the plurality of different products (Scaff: [0070] – “interactive elements may be selectable to perform an action in relation only to the respective clothing item, such as to add the respective clothing item to an electronic shopping cart or electronic shopping bag of the online marketplace 114, purchase the respective clothing item… an interactive element that is selectable to initiate a purchase of all the clothing items which form one of the outfit(s) 150”).
Scaff further discloses that the model uses historical user data (Scaff: [0072]), yet Scaff does not explicitly disclose train a machine learning model, using the historical customer data, to form a trained machine learning model, wherein each respective product in the plurality of products is associated with at least one corresponding product in the plurality of products based at least in part on a purchase or view history between the respective product and the at least one corresponding product; receive purchase data indicating purchase of at least one purchased product; input the purchase data into the trained machine learning model, wherein the trained machine learning model compares the at least one purchased product to the plurality of different products to generate a comparison value indicating a deviation of the at least one purchased product from the plurality of different products; and retrain the trained machine learning model based on the purchase data, wherein the retraining the trained machine learning model comprises inputting the comparison value into the trained machine learning model to refine the trained machine learning model to increase an accuracy of the machine learning model.
However, Zalmanson teaches a similar recommendation system (Zalmanson: [abstract]), including
train a machine learning model, using the historical customer data, to form a trained machine learning model (Zalmanson: [0019] – “historical documents of a user may be used to train a machine learning model to predict items (e.g., indicating products or services) that the user will add to new documents (e.g., invoices)”);
wherein each respective product in the plurality of products is associated with at least one corresponding product in the plurality of products based at least in part on a purchase or view history between the respective product and the at least one corresponding product (Zalmanson: [0066] and Table 420 – “Table 420 is an example of a co-occurrence matrix based on the data in table 410. As shown in table 420, item 2 has a co-occurrence rate of 1 with item 5 (e.g., because item 5 also appears in 100% of the invoices that include item 2), 0 with item 7 (because item 7 appears in none of the invoices in which item 2 appears), and 1 with item 8 (because item 8 also appears in 100% of the invoices in which item 2 appears)”; [0034] – “co-occurrence probabilities. For example, the probability of each item in the user's inventory other than item #1 appearing in the same invoice as item #1 may be determined based on historical invoices of the user. A dynamic score may then be calculated for each given item based on the score output by the model for the given item and the likelihood of the given item co-occurring with item #1. Item #2 may be selected based on determining that it has a highest dynamic score of the remaining items and/or based on determining that its score and/or dynamic score exceed one or more thresholds);
receive purchase data indicating purchase of at least one purchased product; input the purchase data into the trained machine learning model (Zalmanson: [0038] – “once the user adds recommended items and/or declines recommendations and completes the invoice, the invoice may be added to the set of historical invoices used to generate training data. Updated training data may be generated based on the invoice, and the machine learning model may be re-trained based on the updated training data for improved subsequent recommendations”);
retrain the trained machine learning model based on the purchase data, wherein the retraining the trained machine learning model comprises inputting into the trained machine learning model to refine the trained machine learning model to increase an accuracy of the machine learning model (Zalmanson: [0038] – “once the user adds recommended items and/or declines recommendations and completes the invoice, the invoice may be added to the set of historical invoices used to generate training data. Updated training data may be generated based on the invoice, and the machine learning model may be re-trained based on the updated training data for improved subsequent recommendations”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the training and retraining of Zalmanson in the system of Scaff because Scaff already discloses a trained machine learning model and Zalmanson is merely demonstrating what the training entails. Additionally, it would have been obvious to have included train a machine learning model, using the historical customer data, to form a trained machine learning model, wherein each respective product in the plurality of products is associated with at least one corresponding product in the plurality of products based at least in part on a purchase or view history between the respective product and the at least one corresponding product; receive purchase data indicating purchase of at least one purchased product; input the purchase data into the trained machine learning model; and retrain the trained machine learning model based on the purchase data, wherein the retraining the trained machine learning model comprises inputting into the trained machine learning model to refine the trained machine learning model to increase an accuracy of the machine learning model as taught by Zalmanson because training and re-training value is well-known and the use of it in a recommendation system would have improved subsequent recommendations (Zalmanson: [0038]).
Additionally, Kuhn teaches a similar recommendation system (Kuhn: [abstract]), including
wherein the trained machine learning model compares the at least one purchased product to the plurality of different products to generate a comparison value indicating a deviation of the at least one purchased product from the plurality of different products (Kuhn: [0070] – “the backend processing platform 206 can perform method 254 for assessing accuracy of rankings and recommendations made by the system 200. In some examples, the method 254 begins with generating an enrollment/recommendation analysis that can compare subscription product recommendations to actual enrollment selections made by members 108 (260). In some implementations, to maintain member privacy, recommendation/enrollment selection combinations may be stored with a deidentified identification code in a data repository (262). In some examples, the deidentified recommendation/enrollment selection data can be used to generate accuracy statistics for the system 200 (264). In some examples, the accuracy statistics can be used to target refining and retraining of data models to adjust cost-driving factors, cluster assignments, or other training variables that impact the results output by the system 200”); and
wherein the retraining comprises inputting the comparison value (Kuhn: [0070] – “the backend processing platform 206 can perform method 254 for assessing accuracy of rankings and recommendations made by the system 200. In some examples, the method 254 begins with generating an enrollment/recommendation analysis that can compare subscription product recommendations to actual enrollment selections made by members 108 (260). In some implementations, to maintain member privacy, recommendation/enrollment selection combinations may be stored with a deidentified identification code in a data repository (262). In some examples, the deidentified recommendation/enrollment selection data can be used to generate accuracy statistics for the system 200 (264). In some examples, the accuracy statistics can be used to target refining and retraining of data models to adjust cost-driving factors, cluster assignments, or other training variables that impact the results output by the system 200”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the comparison of Kuhn in the system of Scaff/Zalmanson because Scaff/Zalmanson already discloses an analysis and Kuhn is merely demonstrating what the analysis entails. Additionally, it would have been obvious to have included wherein the trained machine learning model compares the at least one purchased product to the plurality of different products to generate a comparison value indicating a deviation of the at least one purchased product from the plurality of different products; and wherein the retraining comprises inputting the comparison value as taught by Kuhn because comparison is well-known and the use of it in a recommendation system would have improved accuracy of recommendations (Kuhn: [0045]).
In regards to claim 2, Scaff/Zalmanson/Kuhn teaches the system of claim 1. Scaff further discloses wherein the instructions cause the processor to: generate cohesive look data, by the trained machine learning model, the cohesive look data including one or more cohesive looks, wherein the trained machine learning model compares a corresponding plurality of visual similarities between each different product from the plurality of different products (Scaff: [0003] – “Outfit curation by generative artificial intelligence”; [0065] – “initiate a search of the online marketplace 114, such as a search to locate complementary clothing items for the outfit that are complementary to the seed clothing item 136”; [0043] – “wherein the clothing attributes of the seed clothing item are extracted from an image of the seed clothing item”; [0024-0025] – “return…items that complement or otherwise match the style…of the seed…item…a prompt for input to generative artificial intelligence to create an outfit that includes the seed clothing item, the prompt including clothing attributes of the seed clothing item; providing the prompt to the generative artificial intelligence to cause the generative artificial intelligence to initiate a search of an online marketplace to locate complementary clothing items for the outfit”; [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”; [0065] – “the generative artificial intelligence 122 is trained”).
In regards to claim 6, Scaff/Zalmanson/Kuhn teaches the system of claim 1. Scaff further discloses wherein the plurality of complementary product types includes a plurality of product sub-types (Scaff: [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”; the examiner notes types of clothing are product sub-types).
In regards to claim 7, Scaff/Zalmanson/Kuhn teaches the system of claim 6. Scaff further discloses wherein the look data includes a plurality of products from each of the plurality of product sub-types (Scaff: [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”; [0068] – “the complementary clothing item listings 146 are arranged to create outfit(s) 150 for a user interface, where the complementary clothing item listings 146 that are combined to form the outfit(s) 150 are selectable (e.g., to add to a cart and/or purchase). In at least one implementation, the generative artificial intelligence 122 arranges the complementary clothing item listings 146 into one or more outfits, e.g., a number of outfits specified in the prompt 138”).
In regards to claim 8, Scaff/Zalmanson/Kuhn teaches the system of claim 6. Scaff further discloses wherein the instructions further cause the processor to: receive feedback of a look from a customer; and replace one or more products of the plurality of different products with another product from the same product type or the product sub-type (Scaff: [0071] – “a user may be able to provide input to deselect and/or select various clothing items from the arranged complementary clothing items 148 included in the outfit(s) 150 to change which of the arranged complementary clothing items 148 are simulated on the avatar…user interface 152 may be configured in a grid, such that a column of the arranged complementary clothing items 148 corresponds to a combination of clothing items that form an outfit and such that a row of the arranged complementary clothing items 148 correspond to different options for a portion of the outfit. For example, a row may include images of different tops such that a user can select from different recommended tops in the user interface 152 to form an outfit. In one or more implementations, the user interface 152 may allow a user to horizontally scroll through an individual row to view the different options of the category while the other clothing items of the outfit (e.g., the other portions of the outfit in the vertical arrangement) remain stationary”; see also [0094] and Fig. 8).
In regards to claim 9, claim 9 is directed to a method. Claim 9 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Scaff/Zalmanson/Kuhn teaches the limitations of claim 1 as noted above. Scaff further discloses a method (Scaff: [0025]). Claim 9 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 10 and 14-16, all the limitations in method claims 10 and 14-16 are closely parallel to the limitations of method claims 2 and 6-8 analyzed above and rejected on the same bases.
In regards to claim 17, claim 17 is directed to a medium. Claim 17 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Scaff/Zalmanson/Kuhn teaches the limitations of claim 1 as noted above. Scaff further discloses a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising (Scaff: [0105-0108]). Claim 17 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 18 and 20, all the limitations in medium claims 18 and 20 are closely parallel to the limitations of method claims 2 and 8 analyzed above and rejected on the same bases.
Claims 3-4 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Scaff, in view of Zalmanson, in view of Kuhn, in view of previously cited Wang et al. (US 20230368268 A1), hereinafter Wang.
In regards to claim 3, Scaff/Zalmanson/Kuhn teaches the system of claim 2. Scaff further discloses generative AI (Scaff: [0003]). Yet Scaff does not explicitly disclose wherein the trained machine learning model is a large language model.
However, Wang teaches a similar fashion information system (Wang: [0129]), including
wherein the trained machine learning model is a large language model (Wang: [0061] – “controller 504 may use a large language model”; [0047] – “the model training engine may train the multi-modal model”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the large language model of Wang in the system of Scaff/Zalmanson/Kuhn because Scaff/Zalmanson/Kuhn already discloses generative AI and Wang is merely demonstrating a type of AI. Additionally, it would have been obvious to have included wherein the trained machine learning model is a large language model as taught by Wang because LLMs are well-known and the use of it in a fashion system would have improved accuracy and precision of analysis for retail-specific applications (Scaff: [0030]).
In regards to claim 4, Scaff/Zalmanson/Kuhn teaches the system of claim 2. Scaff further discloses cohesive looks and cohesive look data (Scaff: [0003]; [0024]). Yet Scaff does not explicitly disclose wherein the instructions further cause the processor to: transmit the data to a ranking engine, wherein the ranking engine: assigns weights to each of the one or more looks; and transmits one or more of the highest weighted looks to a user interface for display to the customer.
However, Wang teaches a similar fashion information system (Wang: [0129]), including
wherein the instructions further cause the processor to: transmit the data to a ranking engine, wherein the ranking engine: assigns weights to each of the one or more looks; and transmits one or more of the highest weighted looks to a user interface for display to the customer (Wang: [0080] – “the recommender system 104 may recommend items (step 618). In some embodiments, the recommender system 104 may use the set of similarity scores S to recommend items. For example, the recommender system 104 may select items having the greatest similarity score (e.g., items associated with embeddings that are nearest in the latent space to the target embedding) to recommend. In an example, the recommender system 104 may select a predetermined number of such items (e.g., as set by an administrator of the recommender system 104 or based on characteristics of the user interface, such as display size, or based on a required threshold similarity”; [0097] – “similarity scores may also be weighed”; [0129] – “deployed in a particular context, such as within the context of fashion items”; [0071] – “the recommender system 104 may generate an embedding for the selected item by inputting one or more of text or visual data into a model that generates embeddings”; [0078] – “determine similarity scores for the target embedding and embeddings from the collection of embeddings”; see also [0123]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the ranking of Wang in the system of Scaff/Zalmanson/Kuhn because Scaff/Zalmanson/Kuhn already discloses an analysis and Wang is merely demonstrating a type of analysis. Additionally, it would have been obvious to have included wherein the instructions further cause the processor to: transmit the data to a ranking engine, wherein the ranking engine: assigns weights to each of the one or more looks; and transmits one or more of the highest weighted looks to a user interface for display to the customer as taught by Wang because rankings are well-known and the use of it in a fashion system would have improved accuracy and precision for retail-specific applications (Scaff: [0030]).
In regards to claims 11-12, all the limitations in method claims 11-12 are closely parallel to the limitations of method claims 3-4 analyzed above and rejected on the same bases.
Claims 5, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Scaff, in view of Zalmanson, in view of Kuhn, in view of previously cited Park et al. (US 20200134694 A1), hereinafter Park.
In regards to claim 5, Scaff/Zalmanson/Kuhn teaches the system of claim 1. Scaff further discloses wherein the instructions further cause the processor to: generate look data based on the historical customer data, the complementary product type data, the look data including a subset of the plurality of different products, each respective different product in the subset of the plurality of different products from a different product type of the plurality of product types (Scaff: [0068] – “the complementary clothing item listings 146 are arranged to create outfit(s) 150 for a user interface, where the complementary clothing item listings 146 that are combined to form the outfit(s) 150 are selectable (e.g., to add to a cart and/or purchase). In at least one implementation, the generative artificial intelligence 122 arranges the complementary clothing item listings 146 into one or more outfits, e.g., a number of outfits specified in the prompt 138”; [0065] – “locate complementary clothing items for the outfit that are complementary to the seed clothing item 136”; [0072] – “one or more styles associated with a user submitting the request are accessed (e.g., from user data of the online marketplace 114), and the prompt 138 is generated based at least in part on those one or more styles. For instance, a style or styles of a user may be learned by the online marketplace 114 over time, such as based on one or more of a purchase history, browsing history, likes, comments, demographics, social media behavior, images uploaded and/or specified by the user, determined similar users, and so on. Thus, the automated outfit curation system 106 may, at least in part, leverage historical style information to curate the outfit(s) 150 in one or more implementations”; [0039] – “the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item”).
Yet Scaff does not explicitly disclose replace the customer's selection of the first product with a second product, by a variant engine, wherein the second product is visually similar to the first product; and generate look data based on the second product, the look data including the second product.
However, Park teaches a similar fashion recommendation system (Park: [0001]), including
replace the customer's selection of the first product with a second product, by a variant engine, wherein the second product is visually similar to the first product; and generate look data based on the second product, the look data including the second product (Park: [0125-0126] – “select an item found during actual Internet search or an item photographed in a store as a query item 410 such that recommended fashion outfit 460 are presented to the user. In the case in which the identification key (iid) of the item found during actual Internet search or the item photographed in the store is not stored in the vendor item DB 100, the query item may be replaced with an item having a vector that is the most similar to the vector of the query item, and then recommended fashion outfit 460 may be composed and presented”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the second product of Park in the system of Scaff/Zalmanson/Kuhn because Scaff/Zalmanson/Kuhn already discloses an outfit and Park is merely demonstrating an outfit with a second product. Additionally, it would have been obvious to have included replace the customer's selection of the first product with a second product, by a variant engine, wherein the second product is visually similar to the first product; and generate look data based on the second product, the look data including the second product as taught by Park because second products are well-known and the use of it in a fashion system would have allowed for outfit compositions based on items that are not in the database (Park: [0125-0126]).
In regards to claim 13, all the limitations in method claim 13 are closely parallel to the limitations of system claim 5 analyzed above and rejected on the same bases.
In regards to claim 19, all the limitations in medium claim 19 are closely parallel to the limitations of system claim 5 analyzed above and rejected on the same bases.
Response to Arguments
Applicant’s arguments, filed 07/06/2026, have been fully considered.
Claim Objections
Applicant argues that the amendments cure the issues discussed in the claim objections. (Remarks page 10). The examiner agrees in part, but disagrees as to claim 16. Claim 16 has not been amended to cure the claim objection and thus, the objection is maintained.
35 U.S.C. § 101
Applicant argues that the claims do not recite an abstract idea because the invention “is not a method of organizing human activity…contain limitations that cannot be practically performed in the human mind.” Remarks pages 11-12. The examiner disagrees. As shown in the rejection of claims under 35 U.S.C. 101 above, the limitations directed to the abstract idea are directly quoted and concepts within the identified as belonging to the Certain Methods of Organizing Human Activity and Mental Processes groupings of abstract ideas. With respect to the instant claims, a system, comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to; a database; train a machine learning model, a trained machine learning model, a user interface; retraining the trained machine learning model; and a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising have been analyzed as additional elements and accordingly are not analyzed under Step 2A, Prong 1. The claims recite creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item. These claims fall into the Methods of Organizing Human Activity grouping, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, generating a look of products for purchase and obtaining purchase information for refining an analysis is a sales and marketing activity. This is further illustrated in paragraph [0022] of the Specification, describing the invention relates to recommending products for purchase. These claims further fall within the Mental Processes grouping of abstract ideas. Specifically, the determinations, recommendations, and analysis 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. It is noted that a human, using pen and paper, may generate look data and further refine an analysis based on purchase data. Accordingly, these claims recite an abstract idea.
Applicant argues the claims are integrated into a practical application because “this concept of processing efficiency and conservation of resources is recited in a specific manner that represents a technical improvement over systems that are configured to process and transmit recommended items in an unrestricted manner…claimed solution improves the machine learning model itself” (Remarks pages 12-14). The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item. While the Examiner acknowledges Applicant’s arguments regarding an improvement, the Examiner notes that improving an analysis with different data is merely an improvement to the abstract idea. Further, the retraining of a model is merely recited at a high level and are thus insufficient to show a technological improvement.
Although the claims include computer technology such as a system, comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to; a database; train a machine learning model, a trained machine learning model, a user interface; retraining the trained machine learning model; and a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising, such elements 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 creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item. The claimed process, while arguably resulting in a better process for sending recommendations, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to purchases while still employing the same server and/or computer components used in conventional systems to improve creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application.
Applicant argues the claims provide significantly more because the claims “an unconventional combination of features that confine the claims to a particular useful application” (Remarks page 14). The examiner disagrees. The MPEP sets forth that if a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, Applicant's claims merely recite steps of a method with generic computer components being recited in a generic manner. While electronic elements such as a system, comprising: a processor; and a non-transitory memory storing instructions, that when executed, cause the processor to; a database; train a machine learning model, a trained machine learning model, a user interface; retraining the trained machine learning model; and a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising are included within the claims, they are claimed in a generic manner and merely perform generic functions. The additional elements 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 creating a look with multiple complementary products based on a user selection and updating a model based on purchase of an item 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 amount to significantly more because they merely amount to using the additional elements as a tool to perform the abstract idea. Accordingly, the claims do not provide significantly more.
35 U.S.C. § 102/103
Applicant argues the claims are allowable because the cited art does not disclose “training the machine learning model, using the historical customer data, to form the trained machine learning model that evaluates a plurality of visual similarities between products associated with one another based on the purchase or view history between them, and that purchase data indicating at least one purchased product is inputted into the trained machine learning model to generate the comparison value indicating the deviation of the at least one purchased product from the plurality of different products that is inputted into the trained machine learning model to retrain and refine the trained machine learning model” (Remarks page 14). The examiner disagrees. Initially, the examiner notes that the amendments have necessitated a new grounds of rejection and new references have been cited to teach train a machine learning model, using the historical customer data, to form a trained machine learning model, wherein each respective product in the plurality of products is associated with at least one corresponding product in the plurality of products based at least in part on a purchase or view history between the respective product and the at least one corresponding product; input the purchase data into the trained machine learning model, wherein the trained machine learning model compares the at least one purchased product to the plurality of different products to generate a comparison value indicating a deviation of the at least one purchased product from the plurality of different products; and retrain the trained machine learning model based on the purchase data, wherein the retraining the trained machine learning model comprises inputting the comparison value into the trained machine learning model to refine the trained machine learning model to increase an accuracy of the machine learning model. Furthermore, Scaff discloses a trained machine learning model that evaluates a plurality of visual similarities between a plurality of products associated with a corresponding plurality of different product types. Scaff discloses this at least in [0064-0065], disclosing that the generative artificial intelligence is trained and a search to locate complementary clothing items for the outfit that are complementary to the seed clothing item is performed. Scaff additionally discloses in [0077] that the prompt for the generative artificial intelligence excludes a clothing type of the seed clothing item. For example, if the seed clothing item is a red shirt, then the prompt asks the generative artificial intelligence to locate clothing items other than shirts which complement the seed clothing item. Thus, a plurality of different product types are determined. Scaff further discloses in [0024-0025] that items that complement or otherwise match the style of the seed item are returned, where a prompt for input to generative artificial intelligence to create an outfit that includes the seed clothing item, the prompt including clothing attributes of the seed clothing item; providing the prompt to the generative artificial intelligence to cause the generative artificial intelligence to initiate a search of an online marketplace to locate complementary clothing items for the outfit. Scaff additionally discloses in [0039] that the seed clothing item corresponds to a category of clothing items, wherein the category of the seed clothing item includes one of headwear, a top layer, a bottom layer, shoes, or an accessory, and wherein the complementary clothing items correspond to different categories of clothing items that are different than the category of the seed clothing item. Thus, the cited art teaches these concepts in the claims.
Applicant argues claims 9, 18, and the dependent claims are allowable for the same reasons as claim 1 (Remarks pages 15-16). The examiner disagrees. The rejection of claim 1 has been maintained the rejections of claims 9, 18, and the dependent claims are maintained for the same, and additional, 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 04/06/2026, teaches AI outfit recommendations. A seed item is supplied as input. Items in additionally categories are added until a complete outfit is formed. Items are scored using machine learning to determine how well they go together.
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
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