DETAILED ACTION
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 Amendment
The following Office Action is responsive to the amendments and remarks received on February 12, 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 8, 9, 13, 14, 16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Minvielle Dabdoub (US 2023/0104294, hereinafter Min) in view of Sholl et al. (US 2024/0037869), further in view of Faraji et al. (US 12,333,855), further in view of Fitzpatrick (US 9,697,563).
With reference to claim 1, Min teaches a method for providing a personalized shopping experience using an Al-guided omnichannel shopping application, comprising:
Receiving, via a user app interface presented on a user device associated with a user, user data comprising user styling preferences, shopping habits, and images of the user's personal wardrobe inventory (see paragraph 61);
Processing, by an image processing module executed on a server, images of the personal wardrobe inventory of the user by extracting data points including a type, a brand, a size, a color, and a condition of the wardrobe inventory from the images to provide a virtual closet for the user, wherein the processed images are categorized based on the extracted data points in the virtual closet of the user (see paragraph 62);
Filtering product data obtained from multiple retailers to provide a personalized product feed source, wherein the personalized product feed source comprises selected items to match the user's style preferences, wherein the product data is obtained from both online and offline resources (see paragraph 136, and also see paragraph 55, known personal styling platforms);
enable personalized AI search tools of the selected items comprising clothing and accessories (see paragraph 136, and see also paragraph 171, "artificial intelligence and/or machine learning may be used");
generating personalized product recommendations to the user using the Al model through the user app interface, wherein the personalized product recommendations comprise outfit suggestions that match the user's style preferences and wardrobe needs, complementary products, seasonal and trending items and exclusive offers and deals (see paragraph 81, user interface, paragraph 83, third- party shopping platform, paragraph 93, outfit suggestions);
in response to completion of a purchase transaction through the user app interface, automatically updating the virtual closet by adding a purchased item to the virtual closet without requiring manual image uploaded by the user (see Figure 4, element 412 and paragraph 87, information is added to virtual closet via sale confirmation email).
While Min does not explicitly teach using an Al model for analyzing user preferences, the teaching of a data system that begins to understand user preferences (see paragraph 136) is understood to be equivalent to an Al system which ingests data about the user to learn the same user preferences.
Min fails to teach removing backgrounds, integrating augmented reality features to enable Al, and providing a personal stylist interface that allows stylists to access the virtual closet of the user and the personalized product feed, wherein the stylist is enabled to mark recommended items, that appear in a dedicated "For You" section in the user app interface.
Sholl et al. teaches:
removing backgrounds (see paragraph 7, extracting the selected garment from the photo of the model);
integrating augmented reality (AR) features, wherein the AI search tools utilize camera of the user device to select items in real-time (see paragraph 77 - computer vision, machine learning, and augmented reality combine to create a realistic and interactive virtual try-on experience); and
providing a personal stylist interface that allows stylists to access the virtual closet of the user and the personalized product feed, wherein the stylist is enabled to mark recommended items, that appear in a dedicated "For You" section in the user app interface (see paragraph 183).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the features of Sholl et al. with the virtual closet system of Min, as they are both in the same field of endeavor, and the combination would provide increased functionality and improved user experience through the use of additional technologies that would enhance the virtual closet and styling process.
Min and Sholl et al. fail to teach training, by a training module executed on the server, and continuously updating an AI model using interaction data received across multiple channels comprising e-commerce platforms, video feeds, social media interactions, and stylist interactions; capturing stylist interaction data associated with stylist recommendations provided through the personal stylist interface and using the stylist interaction data as input to continuously refine the AI model; capturing user interaction outcome data associated with user interactions with recommended items, including selection and purchase activity, and using the user interaction outcome data to further refine the AI model; iteratively refining the AI model during operation of the Al-guided omnichannel shopping application to improve relevance and personalization of recommendations over time.
Faraji et al. teaches:
training, by a training module executed on the server, and continuously updating an AI model using interaction data received across multiple channels comprising e-commerce platforms, video feeds, social media interactions, and stylist interactions (see column 39, lines 35-45, using user data including social engagement data to train an AI model for use in a styling app);
capturing stylist interaction data associated with stylist recommendations provided through the personal stylist interface and using the stylist interaction data as input to continuously refine the AI model (see column 40, line 55-column 41, line 17, the stylist model is updated based on stylist recommendation data);
capturing user interaction outcome data associated with user interactions with recommended items, including selection and purchase activity, and using the user interaction outcome data to further refine the AI model (see column 40, lines 35-54, capturing user data including purchases and preferences and using it to update an ML model);
iteratively refining the AI model during operation of the Al-guided omnichannel shopping application to improve relevance and personalization of recommendations over time (see column 37 line 66 to column 38, line 42 – iterating the model).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the styling app as taught by Min and Sholl to include the training of the AI model including all of the data types and frequencies as taught by Faraji, to improve the simplicity, efficiency, and effectiveness of online shopping for fashion items (see Faraji, column 1 lines 19-31) by incorporating AI and available data about the user.
Min, Sholl, and Faraji fail to teach integrating the Al-guided omnichannel shopping application with a plurality of third-party e-commerce platforms to expand a range of products available for the user to shop; maintaining a universal shopping cart to enable a single checkout process for products offered by the plurality of third-party e-commerce platforms; and facilitating completion of a purchase transaction through the user app interface by transmitting purchase and payment information to a transaction system of a selected third-party e-commerce platform, such that the user completes checkout without leaving the user app interface.
Fitzpatrick teaches:
integrating the Al-guided omnichannel shopping application with a plurality of third-party e-commerce platforms to expand a range of products available for the user to shop (see column 10, lines 10-22, collecting merchandise data from a variety of merchants and assembling in a normalized format on a single publisher’s page);
maintaining a universal shopping cart to enable a single checkout process for products offered by the plurality of third-party e-commerce platforms (see column 10 line 65-column 11 line 14, single cart and checkout option); and
facilitating completion of a purchase transaction through the user app interface by transmitting purchase and payment information to a transaction system of a selected third-party e-commerce platform, such that the user completes checkout without leaving the user app interface (see column 11, lines 15-32, a single order is placed and then split to the multiple merchants).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the universal shopping site, cart, and checkout system of Fitzpatrick with the virtual styling and shopping app of Min, Sholl, and Faraji, to permit users to complete checkout of recommended items from a variety of sources in a simple and streamlined process while still accessing merchandise from a variety of sources (see Fitzpatrick, column 1, lines 24-46).
With reference to claim 2, Min, Sholl et al., Faraji et al., and Fitzpatrick teach the method of claim 1, and Min further comprises:
obtaining product information of the selected items from the multiple retailers, wherein the product information comprises product name, description, price, specification, image, availability, vendor information, user reviews and ratings, warranty and return policy, shipping details, SKU or product ID, brand, instructions or manuals (see paragraph 62-63, obtain metadata for a selected garment;
comparing the selected items from the multiple retailers to select a retailer offering the best deal for the user, wherein the comparison is performed by obtaining real-time updates from multiple retailers' databases to provide the most current availabilities (see paragraph 95 - rank shopping alternatives on retailer websites, also see paragraph 54, known search engine shopping platforms);
presenting to the user through the user app interface, the product information of the selected item from the selected retailer (see paragraph 83 - allow a user to shop on a third-party shopping platform);
initiating and receiving a confirmation to purchase the selected item from the user through the user device (see paragraph 83 - allowing a user to shop on a third party platform inherently involves this step);
initiating a transaction in response to receiving the confirmation from the user with the selected retailer to purchase the selected item (see paragraph 83 - allowing a user to shop on a third party platform inherently involves this step); and
transmitting purchase and payment information from the user to a transaction system of the selected retailer for purchase (see paragraph 83 - allowing a user to shop on a third party platform inherently involves this step).
With reference to claim 3, 14, Min, Sholl et al., Faraji et al., and Fitzpatrick teach the method of claim 1, 13 and Min further comprises generating the images of the wardrobe inventory of the user based on products searched by the user using textual inputs via the user app interface, wherein the generated images are added to the virtual closet of the user (see paragraphs 63-64 - obtaining metadata includes the image of every product).
With reference to claim 5, 16, Min, Sholl et al., Faraji et al., and Fitzpatrick teach the method of claim 1, 13, and Min further comprises enabling (ii) access to external databases that assist the user in making informed purchasing decisions (see paragraph 128 - tap into the open web to gain resale value information).
Sholl et al. further teaches enabling (i) social sharing of the selected items added to a virtual cart, finds, and reviews with friends and family (see paragraph 86, social sharing).
The references are combined in the same way and using the same motivation as above.
With reference to claim 8, Min, Sholl et al., Faraji et al., and Fitzpatrick teach the method of claim 1, and Min further teaches enabling the stylist to provide tailored product suggestions to multiple users based on their preferences and past purchases (see paragraph 55 - known stylist platform provides personalized advice).
With reference to claim 9, 19, Min, Sholl et al., Faraji et al., and Fitzpatrick teach the method of claim 1, 13, and Min further teaches aggregating and displaying detailed statistics about the user's wardrobe including inventory count, total value, and usage metrics via a data dashboard (see paragraph 62).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Min, Sholl et al., Faraji et al., and Fitzpatrick as applied to claims 1 and 13 above, and further in view of Morgan et al. (US 2023/0137742).
With reference to claims 4 and 15, Min, Sholl et al., Faraji et al., and Fitzpatrick teach all that is required with reference to claim 1 and 13, but fail to teach providing (i) a store locator feature to guide the user to nearby physical stores carrying desired products, or (ii) in-store navigation in supported stores to direct the user to the location of products.
Morgan et al. teaches providing (i) a store locator feature to guide the user to nearby physical stores carrying desired products (see paragraph 25), or (ii) in-store navigation in supported stores to direct the user to the location of products (see paragraph 29).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the shopping features of Morgan with the virtual closet/shopping system of Min, Sholl, Faraji, and Fitzpatrick, as they are both in the same field of endeavor of using an app to facilitate the shopping process, and the combination would provide increased functionality and improved user experience through the use of additional technologies that would all the user to find items for purchase in-store as well as online.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Min, Sholl et al., Faraji et al., and Fitzpatrick as applied to claims 1 and 13 above, and further in view of Tipograph et al. (US 11,475,487).
With reference to claims 6 and 17, Min, Sholl et al., Faraji et al., and Fitzpatrick teach all that is required with reference to claims 1 and 13, but fail to teach integrating a video shopping component that allows the user to watch shoppable videos featuring products; and displaying a correlated product feed under a video feed to allow the user to shop for products featured in the video in real-time.
Tipograph et al. teaches integrating a video shopping component that allows the user to watch shoppable videos featuring products; and displaying a correlated product feed under a video feed to allow the user to shop for products featured in the video in real-time (see column 18, lines 53-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the video shopping features of Tipograph et al. with the virtual closet/shopping system of Min, Sholl, Faraji, and Fitzpatrick, as they are both in the same field of endeavor of using an app to facilitate the shopping process, and the combination would provide increased functionality and improved user experience through the use of additional technologies that would allow the user to find items for purchase through additional venues.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Min, Sholl et al., Faraji et al., and Fitzpatrick as applied to claims 1 and 13 above, and further in view of Sprangers et al. (US 2019/0019235).
With reference to claims 7 and 18, Min, Sholl et al., Faraji et al., and Fitzpatrick teach all that is required with reference to claims 1 and 13, and Min further teaches enable the stylist to provide tailored product suggestions to multiple users based on their preferences and past purchases (see paragraph 55 – known stylist platform provides personalized advice), but fails to teach allowing the user to interface with the personal stylist virtually via in-app chat and video styling sessions.
Sprangers et al. teaches allowing the user to interface with the personal stylist virtually via in-app chat and video styling sessions (see paragraph 41).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the features of Sprangers with the virtual closet/shopping system of Min, Sholl, Faraji, and Fitzpatrick, as they are both in the same field of endeavor of using an app to facilitate the stylist process, and the combination would provide increased functionality and improved user experience through the use of additional technologies that would allow the user to better interact with the stylist.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Min, Sholl et al., Faraji et al., and Fitzpatrick as applied to claim 1 above, and further in view of Guo et al. (US 2018/0308149).
With reference to claim 11, Min, Sholl et al., Faraji et al., and Fitzpatrick teach all that is required with reference to claim 1, but fail to teach synchronizing with the user’s calendar to provide outfit suggestions for specific events and occasions.
Guo et al. teaches synchronizing with the user’s calendar to provide outfit suggestions for specific events and occasions (see paragraph 22).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the features of Guo with the virtual closet/shopping system of Min, Sholl, Faraji, and Fitzpatrick, as they are in the same field of endeavor of using an app to facilitate the stylist process, and the combination would provide increased functionality and improved user experience through the use of additional data to provide better recommendations.
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Min, Sholl et al., Faraji et al., and Fitzpatrick as applied to claim 1 above, and further in view of Van Gorkom et al. (US 10,402,037).
With reference to claim 12, Min, Sholl et al., Faraji et al., and Fitzpatrick teach all that is required with reference to claim 1, but fail to teach sending notifications to the user about discounts, sales, and special offers on the selected items, wherein the frequency of notifications is automatically tuned based on the user’s interaction patterns with the AI-guided omnichannel shopping application.
Van Gorkom et al. teaches sending notifications to the user about discounts, sales, and special offers on the selected items, wherein the frequency of notifications is automatically tuned based on the user’s interaction patterns with the AI-guided omnichannel shopping application (see column 4, lines 45-55 and column 6, lines 51-66).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the features of Van Gorkom with the virtual closet/shopping app of Min, Sholl, Faraji, and Fitzpatrick, as they are in the same field of endeavor of using an app to facilitate the shopping process, and the combination would provide increased functionality and improved user experience through providing better targeted information to the user, increasing the likelihood of relevance.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-9 and 11-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that claims 4, 5, 15, and 16 have been amended to overcome the 112(b) rejection. However, applicant argues that the claims have been amended to recite “and” when in reality the claims have been amended to recite “or.” Examiner suggests that applicant confirms which claim language is intended, and update the claims or remarks accordingly in the response to this office action.
Applicant argues that the claims are eligible in view of 101. In light of the amendments made, examiner agrees that the claims are eligible at step 2B as they constitute significantly more than the abstract idea, and are necessarily rooted in computer technology. Therefore, the 101 rejection has been withdrawn.
Applicant argues that the claims overcome the 103 rejection in view of Min and Sholl et al. Examiner agrees, and has made a new grounds of rejection in light of the amendments to the claims.
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.
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/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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