DETAILED ACTION
Status of Claims
The following is a final office action in response to the communication filed 9/29/2025.
Claims 1-2, 5-9, 12-16 and 19-20 have been amended.
Claims 3-4, 10-11 and 17-18 have been cancelled.
Claims 21-26 have been added.
Claims 1-2, 5-9, 12-16, and 19-26 are currently pending and have been examined.
Priority
The applicant’s claim for benefit of Provisional Patent Application Serial No. 63/479,437 filed 01/11/2023 has been received and acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 1/09/2024 been reviewed and considered.
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 .
Response to Arguments
Applicant’s amendments and associated arguments, filed 9/29/25, with respect to the rejection of the claims under 35 U.S.C. §101 have been considered but they are not persuasive.
Applicant argues that the claimed approach recommends footwear items, to purchase along with an apparel item selected by a user, based on similarities (e.g., color, heel, height, toe shape, fastener, etc.) to complementary footwear depicted with the selected apparel item, which Applicant argues helps retain website visitors by allowing the visitors to conveniently identify complimentary footwear to purchase with a selected apparel item when the specific footwear modeled with the selected apparel item is not carried by the online retailer. For this reason, Applicant argues that claim 1 is allowable for reasons analogous to those discussed in related to claim 19 of Example 2 in the USPTO's 101 Examples guidance, wherein "[t]he claim addresses a business challenge (retaining website visitors) that is particular to the Internet." Examiner respectfully disagrees. Unlike the circumstances in DDR Holdings, Inc. v. Hotels.com (Fed. Cir. 2014), the identified abstract idea in the instant application is merely tied to the technical environment rather than necessarily rooted in technology in order to address a problem specifically arising in the realm of computers or networks. To the contrary, the claims at issue are much more similar to Ultramercial Inc. v. Hulu, LLC (Fed. Cir. 2014) in that they merely recite a general purpose computer that applies the abstract idea by use of conventional computer functions.
Applicant's amendments and associated arguments, filed 9/29/25, with respect to the prior art rejection(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
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 1-2, 5-9, 12-16, and 19-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-2, 5-9, 12-16, and 19-26 are directed to a method (process), a system (machine or manufacture), and a non-transitory medium (manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those identified in bold below:
1. (Currently Amended) A system for complementary item recommendations, the system comprising: at least one processor; and at least one memory comprising programming instructions for execution by the at least one processor, the programming instructions, upon execution by the at least one processor, causing the system to perform the following operations: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item; determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, and a fastener type; calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold: and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
Claim 8 recites abstract limitations, including those identified in bold below:
8. (Currently Amended) A method for recommending complementary apparel items, the method comprising: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, a fastener type, and a style: calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold; and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
Claim 15 recites abstract limitations, including those identified in bold below:
15. (Currently Amended) A computer storage device having programming instructions stored thereon, the programming instructions, upon execution by a processor of a system, causing the system to perform the following operations: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item; determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, a fastener type, and a style; calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold; and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than the recitation of generic computing components, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper (e.g., identifying categories, identifying and ranking candidate items, presenting recommendations, etc.). The mere recitation of a generic computer does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
These limitations, as drafted, also recite processes that, under its broadest reasonable interpretation, represent mathematical relationships (e.g., vectors, models, etc.) and are therefore mathematical concepts. The mere recitation of a generic computer does not take the claim out of the mathematical concepts grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One , the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites additional elements, including those underlined below:
1. (Currently Amended) A system for complementary item recommendations, the system comprising: at least one processor; and at least one memory comprising programming instructions for execution by the at least one processor, the programming instructions, upon execution by the at least one processor, causing the system to perform the following operations: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item; determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, and a fastener type; calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold: and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
Claim 8 recites additional elements, including those underlined below:
8. (Currently Amended) A method for recommending complementary apparel items, the method comprising: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, a fastener type, and a style: calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold; and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
Claim 15 recites additional elements, including those underlined below:
15. (Currently Amended) A computer storage device having programming instructions stored thereon, the programming instructions, upon execution by a processor of a system, causing the system to perform the following operations: identifying an apparel item selected by a user via an online webpage of a retailer, the selected apparel item being displayed in an image of a model wearing the selected apparel item and a complementary footwear apparel item; determining recommended footwear apparel items, carried by the retailer, to purchase along with the selected apparel item via the online webpage, wherein determining the recommended footwear apparel items comprises: identifying candidate footwear apparel items, carried by the retailer, within an identified footwear category of the complementary footwear apparel item; determining a per-category similarity definition assigned to the identified category, the per-category similarity definition defining category-specific features for determining a degree of similarity between footwear apparel items within the identified footwear category, the category-specific features including a color feature, a heel height feature, a toe shape feature, a fastener type, and a style; calculating, by a first pre-trained machine learning model, feature vectors for the candidate footwear apparel items based on the per-category similarity definition, each of the feature vectors indicating a degree of similarity between a corresponding one of the candidate footwear apparel items and the complementary footwear apparel item based on a combination of feature vector values calculated according to the category-specific features; and identifying the recommended footwear apparel items based on a subset of the candidate footwear apparel items having corresponding feature vectors exceeding a threshold; and displaying, to the user via a product display page (PDP) of the online webpage, the recommended footwear apparel items for purchase with the selected apparel item, each of the recommended footwear apparel items being displayed, along with the selected apparel item, via a different view on the PDP.
The functions of the system comprising at least one processor and at least one memory, the pre-trained machine learning model, and the user interface device (receiving, processing and displaying information) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components.
The characterization of the online webpage and associated product display page of said online webpage merely indicates a field of use or technological environment in which to apply the judicial exception.
The display of information via the online webpage merely represents extra-solution activity.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis 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).
As discussed above, the additional elements amount to mere instructions to apply the exception generic computing components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, the characterization of the online webpage and associated product display page of said online webpage merely indicates a field of use or technological environment in which to apply the judicial exception, which does not amount to significantly more than the exception itself. (see MPEP 2106.05(h)).
As discussed above the display of information via an online interface amounts to extra-solution (post-solution activity). MPEP 2106.05(d)(II), and the cases cited therein, including in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
The various metrics/limitations of dependent claims 2, 6-7, 9, 12-14, 16, 20-26 merely narrow the previously recited abstract idea limitations (performing object recognition, calculating ranks for items in categories, training a model, filtering candidate items, characterizing the image, etc.). Claims 2, 11 and 16 further recites the performance of object detection by a second pre-trained machine learning model, which merely amounts to applying the abstract concept of object detection with a generic computing device. with introducing any further additional elements. For the reasons described above with respect to claims 1, 8 and 15, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea
Claims 5 and 19 recite the training of a machine learning model using annotated images including bounding boxes, which merely represents the application of the abstract idea using a generic computing component. At the time of filing, bounding boxes are a generic function used in image training systems. See, for example, Cherian (US 20220309672), which discloses conventional approaches for object recognition utilize bounding boxes to train learning models (see [0003]). For the reasons described above with respect to claims 1, 8 and 15, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea
Potentially Allowable Subject Matter
Claims 1-2, 5-9, 12-16, and 19-26 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, as set forth in this Office action.
The following is an examiner’s statement of reasons for allowance:
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, anticipates, neither reasonably teaches, nor renders obvious the combination of features claimed in applicant's invention.
Previously cited reference, Lorbert et al. (US 12169859 B1), generally discloses techniques for displaying outfit recommendations using attribute feature vectors.
Newly identified reference, Zadeh et al. (US 20140201126 A1), generally discloses algorithms, methods and systems for image recognition which discovers new patterns for a class of items, such as classes of shoes, using images of said items via the internet.
Newly identified reference, Radhunathan et al. (US 20190205905 A1), generally discloses an ensemble engine that utilizes feature vectors to generate complementary items based on a user selection and user preferences, such as a shoe recommendation to complement a piece of clothing.
Newly identified reference, Duan et al. (US 10346893 B1), generally discloses machine-learning approaches to identify ranked complementary sets of items, such as items of clothing, using images displaying said items from electronic interfaces (websites, etc.)
Previously cited reference, Gupta et al. (US 20200226411 A1), generally discloses object detection using a plurality of pre-trained models, trained utilizing bounding boxes for identifying objects of interest, for generating clothing recommendations.
Previously cited reference, Forsyth et al. (US0 20200311798 A1), generally discloses the filtering of candidate items based on various factors, such as brand.
Previously cited reference, Lundgaard (20210110455 A1), generally discloses the filtering of candidate items basedon various factors, including in-stock inventory.
While the aforementioned references disclose each of the elements of the invention, the combination of references does not fully capture the structure and interplay of the elements as recited in the claims. Therefore, upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious all the features of applicant’s invention as the features amount to more than a predictable use of elements in the prior art.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Levy et al. (20100268661 A1) disclosing out of stock items are removed during training.
Shalev et al. (12056911 B1) discloses techniques for performing outfit recommendations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABBY J FLYNN whose telephone number is (571)272-9855. The examiner can normally be reached Monday - Friday 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at (571) 272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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