DETAILED ACTION
This is a Final Office action is in response to communications filed on January 27th, 2026. Claim 1-2, 4-9, 11-12, 15-16, and 20 is/are amended. Claims 1-20 have been examined in this application. The Information Disclosure Statement (IDS) filed on August 12th, 2026 has been acknowledged. This application claims the benefit of U.S. Provisional Application No.: 63/513,346, filed on July 12, 2023, the entire contents of which are incorporated herein.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-10 is/are drawn to system (i.e., a manufacture), 11-15 is/are drawn to computer readable media (i.e., a manufacture), and claims 16-20 is/are drawn to method (i.e., a process). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1: A computerized system comprising: one or more computer processors;
and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations comprising: accessing a request associated with a user of an item listing system;
based on the request, accessing automotive personalization data stored in an automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system,
wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data associated with personalized automotive upgrade guidance;
the automotive personalization data is associated with a generative artificial intelligence (AI) model and user data of the user, the generative Al model is associated with personalization training operations that generate the automotive personalization data structure to support the personalized automotive upgrade guidance for the item listing system;
and communicating the automotive personalization data to cause display of the automotive personalization data via an item listing system interface of an item listing system client.
Claim 16: A computer-implemented method, the method comprising: accessing a training dataset associated with training a generative artificial intelligence (AI) model for an item listing system;
executing personalization training operations on the training dataset to generate the generative AI model, the generative AI model is associated with personalization training operations and an automotive personalization data structure that support personalized automotive upgrade guidance for the item listing system, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing automotive personalization data associated with the personalized automotive upgrade guidance,
automotive personalization data is stored in the automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system;
and deploying the generative AI model to support generating automotive personalization data in the item listing system.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the independent claims 1 and 11 recite the abstract idea of providing personalized automotive upgrade guidance to a user based on user-specific information in an item-listing environment. In particular, the claim recites accessing a request associated with a user of an item listing system, accessing automotive personalization data associated with personalized automotive upgrade guidance based on the request and user data, and communicating the automotive personalization data for presentation to the user through an item listing system interface. These limitations describe tailoring and providing product-related guidance to a particular user in connection with an item-listing/sales interaction. Further, claim 16 recites the abstract idea of providing personalized automotive upgrade guidance for an item-listing system. In particular, the claim recites an automotive personalization data structure that supports personalized automotive upgrade guidance for an item-listing system and deployment of a model to support generation of automotive personalization data within the item-listing system. The personalized upgrade guidance constitutes tailoring product-related information or recommendations for a particular commercial context involving automotive products. Accordingly, this subject matter falls within the certain methods of organizing human activity grouping, specifically commercial interactions including advertising, marketing, or sales activities or behaviors, as identified in MPEP § 2106.04(a)(2). The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Dependent claims 2, 5-7, 12, 14, 17, and 20 are directed to variations of providing and presenting personalized automotive product or upgrade information to a user, including personalized copy, personalized upgrade guides, personalized upgrade products, and presentation of such information through corresponding or multiple item-listing interfaces. These claims involve tailoring and presenting product-related information to a user in connection with an item-listing or sales interaction, which constitutes a commercial interaction involving advertising, marketing, or sales activity. Claims 3 and 18 are directed to a variation of the abstract idea in which the personalized automotive product or upgrade information is provided in image and text form. These claims recite generating or presenting personalized automotive information through different content formats, but the underlying commercial purpose remains providing tailored automotive product or upgrade guidance to a user. Claims 4, 13, and 19 are directed to a variation of the abstract idea involving tailoring automotive product or upgrade guidance based on information concerning the user, automotive products, images, text, and item-listing interfaces. These claims involve collecting and using user-specific, product-specific, and presentation-related information to customize automotive product recommendations or guidance in an item-listing environment, which constitutes personalization of commercial information in connection with marketing or sales activity. Claims 5, 6, 14, 17, and 20 further recite providing personalized copy, personalized upgrade guides, and personalized upgrade products through one or more corresponding item-listing interfaces. These limitations concern the content and manner in which personalized automotive product information is organized and presented to a user and therefore represent variations of the same commercial interaction of providing tailored product guidance in connection with an item-listing or sales activity. Claims 7 and 19, to the extent they recite generating instances of automotive personalization data for a plurality of item-listing interfaces, are directed to distributing or presenting personalized automotive product information across multiple commercial interfaces. The use of multiple interfaces does not change the underlying commercial activity of tailoring and providing automotive product or upgrade guidance to users. Claims 8 and 15 are directed to a variation of the abstract idea in which the automotive personalization information includes different types of data elements, including non-generative-AI data, generative-AI data, or item-listing-interface data elements. These limitations further characterize the information used or provided in carrying out the personalized automotive product-guidance activity but do not alter the underlying commercial interaction. Claim 9 is directed to a variation of the abstract idea involving receiving a request associated with a user, accessing user-specific automotive personalization information responsive to the request, and presenting the personalized automotive information through an automotive personalization interface. The claim therefore involves obtaining and presenting user-specific product information in response to a commercial inquiry, which constitutes a commercial interaction involving marketing or sales activity. Claim 10 is directed to a variation of the underlying commercial activity in which a generative-AI model is trained and deployed to support generation of personalized automotive information in the item-listing system. Accordingly, claims 1-20 are directed to an abstract idea under 35 U.S.C. §101, using conventional communication and recordkeeping techniques. As such, the claims are directed to an abstract idea involving certain methods of organizing human activity, which falls within a judicial exception under 35 U.S.C. §101.
Independent claim(s) 11 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer processors, memory, a generative artificial intelligence (AI) model, interface, etc. (Claims 1, 11, and 16) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a computer processors, memory, a generative artificial intelligence (AI) model, interface, etc. (Claims 1, 11, and 16, and dependent claims 2-10, 12-15, and 17-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., accessing, generating, communicating, displaying, etc. steps performed by a computer processors, memory, a generative artificial intelligence (AI) model, interface, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) of accessing a request associated with a user, accessing automotive personalization data based on the request, and accessing a training dataset associated with training a generative AI model, to the extent these limitations are treated as additional elements beyond the recited commercial interaction of providing personalized automotive upgrade guidance, constitute pre-solution data-gathering or information-retrieval activity. These limitations obtain the user request, personalization information, and training information used to carry out or support the personalized automotive product-guidance process. Merely obtaining or retrieving information that is subsequently used in carrying out the recited commercial interaction does not impose a meaningful technological limitation on that interaction. MPEP § 2106.05(g) expressly identifies obtaining information for subsequent analysis as an example of insignificant pre-solution activity. Further, the limitations of communicating the automotive personalization data to cause display through an item listing system interface, causing display of an instance of automotive personalization data on an automotive data personalization interface, and presenting personalized copy, personalized upgrade guides, personalized upgrade products, or other automotive personalization information through one or more item listing interfaces constitute, to the extent treated as additional elements, post-solution output activity. These limitations communicate or display the result of the personalized automotive product-guidance process after the relevant personalization information has been determined. Merely presenting the resulting personalized commercial information through a user or item-listing interface does not impose a meaningful technological limit on the underlying commercial interaction. MPEP § 2106.05(g) identifies outputting the result of an otherwise ineligible process as an example of insignificant post-solution activity. Accordingly, independent claims 1 and 11, additionally and/or alternatively, append insignificant extra-solution activity to the judicial exception, including pre-solution information gathering or retrieval and post-solution communication or display of the resulting personalized automotive product information. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-10, 12-15, and 17-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent 1, 11, and 16, and dependent claims 2-10, 12-15, and 17-20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of receiving or accessing a user request, accessing or retrieving automotive personalization data, storing or retrieving data from an automotive personalization data structure, communicating data between computing components, and causing display of personalized automotive information through an item-listing or client interface are, to the extent treated as additional elements beyond the recited commercial interaction, generic computer functions performed at a high level of generality. The claims recite the results of obtaining, storing, retrieving, communicating, and displaying personalization information, but do not require a particular database indexing technique, memory-management architecture, network-communication protocol, data-retrieval algorithm, display-rendering mechanism, or other specific improvement to the operation of the computer itself. Similarly, the recited automotive personalization data structure comprising image-text personalization objects is defined principally by the informational content associated with the structure - i.e., images and text associated with personalized automotive upgrade guidance. The claims do not require a particular physical memory arrangement, indexing technique, pointer architecture, database organization, search technique, compression method, or other specific mechanism by which the computer stores or retrieves the recited image-text objects. Rather, the data structure is used to organize and make available information employed in carrying out the underlying commercial activity of providing personalized automotive upgrade guidance. The additional limitations concerning accessing requests and training information also constitute, to the extent applicable, pre-solution information-gathering activity, while the limitations concerning communicating or displaying the resulting automotive personalization data, personalized copy, upgrade guides, or upgrade products through one or more item-listing interfaces constitute post-solution output activity. These limitations merely obtain information used in carrying out the personalized commercial interaction and subsequently communicate or display the resulting commercial information, additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). The claims use conventional computing components to obtain personalization information, organize that information, and present personalized automotive product or upgrade information to the user which is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0046 and 0096] acknowledges that “the automotive personalization data 120 can be associated with enhanced personalization, visual simulation, adaptive recommendations, trend identification, and improved user engagement. Enhanced personalized is provided because the generative AI model 142 is capable of understanding complex patterns in user behavior and vehicle data, leading to highly personalized recommendations that align with individual preferences. The generative AI model 142 can simulate visual representations of recommended upgrades on the user’s specific vehicle, providing a realistic preview and aiding in the decision making process. The dynamic nature of the generative AI model 142 allows for adaptive recommendations that consider evolving user preferences, ensuring that suggestions remain relevant over time” and “The invention may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc. refer to code that perform particular tasks or implement particular abstract data types. The invention may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc.”. This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-10, 12-15, and 17-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Specifically, claims 2, 12, and 17 recite using a generative AI personalization engine to generate personalized automotive information. Claims 3 and 18 recite training a generative AI model to support image and text generation. Claims 4, 13, and 19 recite training using user, automotive, image, text, and item-listing-interface data. Claims 5, 14, and 20 recite providing personalized copy, upgrade guides, and upgrade products through corresponding item-listing interfaces. Claims 6 and 7 recite presenting personalized automotive information through multiple item-listing interfaces and generating personalization data for those interfaces. Claims 8 and 15 further characterize the personalization information as including generative-AI, or interface-related data elements. Claim 9 recites receiving a user request, accessing responsive automotive personalization information, and displaying that information through an interface, while claim 10 recites accessing training data, training a generative AI model, and deploying the model to support generation of personalized automotive information, and therefore do not add an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
When viewed as an ordered combination, the additional elements of claims 2-7, 9-14, and 16-20 merely instruct to implement the abstract idea using generic computer components to collect, store, represent, and display information. The claims do not recite any unconventional arrangement of elements, nor do they effect an improvement to computer functionality or another technical field and therefore fail to integrate the abstract concept into a practical application and it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
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:
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 of this title, 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.
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.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20210233144 (“Gaur”) in view of U.S. Pub. 20200111134 (“Zheng”) in further view of U.S. Pub. 20230009814 (“Hao”).
As per claims 1 and 11, Gaur discloses, and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations comprising: accessing a request associated with a user of an item listing system (Examiner interprets transmission of the user identifier through Gaur's client application/website for obtaining product recommendations as the claimed request associated with a user of an item listing system i.e. Gaur ¶46 - users access application interface 230 or a website and use the interface to provide a user identifier and receive product/service recommendations; and Gaur ¶49 - computing devices 204 include processors 220 and memory 222 storing applications/data, including auction application 224 and client application interface 230) (0046-0049);
based on the request, accessing automotive personalization data (Examiner interprets the user-specific vehicle-product recommendation information derived from user and vehicle information as automotive personalization data; Gaur ¶63 - recommendation module 248 receives a user identifier, retrieves credit data associated with that user and vehicle data associated with a vehicle, and uses a trained ML model to recommend products/services) (0063-0064) stored in an automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system (Examiner interprets personalized recommendations of vehicle accessories and aftermarket products as personalized automotive upgrade guidance, because the recommendation identifies products associated with supplementing, modifying, protecting, or enhancing a particular vehicle i.e. Gaur ¶19 expressly identifies vehicle protection products, vehicle accessories, paint protection, and aftermarket products. Gaur ¶68 further recommends multiple vehicle products/services, expressly including vehicle accessories, based on user/credit and vehicle data) (0063-0064, 0068, 0019);
wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data associated with personalized automotive upgrade guidance (Examiner interprets the recommendation, content-generation and UI modules as executable instructions for providing the personalization data. In the combination, the provided content concerns Gaur's vehicle accessories/aftermarket products; Gaur at ¶63–65 causes application interface 230 to present recommended vehicle products/services) (0063-0065),
the automotive personalization data is associated with a generative artificial intelligence (AI) model and user data of the user (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur's user-associated data together with Hao's historical user behavior, interests, click behavior and user-behavior vectors as the claimed user data of the user i.e. Gaur ¶63 retrieves data associated with the particular user) (0063-0068, 0019),
the generative Al model is associated with personalization training operations that generate the automotive personalization data structure to support the personalized automotive upgrade guidance for the item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets the combined system as using the generated personalization structure to support presentation/recommendation of Gaur's vehicle accessories and aftermarket products through Zheng's marketplace/listing presentation mechanism i.e. Gaur ¶¶19, 68 - personalized recommendations include vehicle accessories and aftermarket products) (0063-0068, 0019).
Gaur specifically doesn’t disclose, stored in an automotive personalization data structure comprising image-text personalization objects, wherein the automotive personalization data structure is associated with automotive personalization logic and communicating the automotive personalization data to cause display of the automotive personalization data via an item listing system interface of an item listing system client, however Zheng discloses, stored in an automotive personalization data structure comprising image-text personalization objects (Examiner interprets Zheng's stored data representation as a personalization data structure i.e. Zheng ¶57 - data-representation generating module 404 generates a data representation based on user behavior relating to a product image; the data representation includes personalized presentation features and may be stored in a database record. Further, Examiner interprets the associated product-image and textual structural elements represented within Zheng's personalization data representation as the claimed image-text personalization objects i.e. Zheng ¶66 - the data representation includes a product-image identifier and text to be included in the personalized content. Zheng ¶83 further expressly identifies an alphanumeric string and a product image as represented structural elements associated with a product available for purchase) (0057, 0065-0066, 0083),
wherein the automotive personalization data structure is associated with automotive personalization logic (Examiner interprets Zheng's ML/data-representation/content-generation modules and associated executable functionality as personalization logic. When used for Gaur's automotive recommendations, the logic constitutes the claimed automotive personalization logic i.e. Zheng ¶¶55-58 - ML system 400 includes access module 402, data-representation generating module 404, banner-image generating module 406 and UI module 408; the ML algorithm generates the data representation and the banner module generates personalized content from the representation) (0055-0058),
and communicating the automotive personalization data to cause display of the automotive personalization data via an item listing system interface of an item listing system client (Examiner interprets Zheng's online-marketplace client UI presenting personalized product content and associated product listings as the claimed item listing system interface of an item listing system client. i.e. Zheng ¶¶67, 70 - personalized content is presented through a user interface of a client device; selection of a product image causes display of the corresponding product listing through that interface) (0059, 0067-0070).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, stored in an automotive personalization data structure comprising image-text personalization objects, wherein the automotive personalization data structure is associated with automotive personalization logic and communicating the automotive personalization data to cause display of the automotive personalization data via an item listing system interface of an item listing system client, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
Gaur specifically doesn’t disclose, is associated with a generative artificial intelligence (AI) model and user data of the user, the generative Al model is associated with personalization training operations that generate the automotive personalization data structure to support the personalized automotive upgrade guidance for the item listing system, however Hao discloses, is associated with a generative artificial intelligence (AI) model (Examiner interprets Hao's trained generative adversarial network, including its generative model used for personalized product recommendation, as a generative artificial intelligence model. When Hao's generative recommendation technique is applied to Gaur's automotive accessory/product recommendation system, Gaur's automotive personalization data becomes associated with a generative AI model i.e. Hao expressly describes an “artificial intelligence-based” information-recommendation method and applies a generative adversarial network to product-domain recommendation. The generative model generates candidate sample data according to historical user behavior data, and the generative and discriminative models are adversarially trained to obtain a trained GAN used to determine an information-recommendation model. Hao ¶40 expressly states that a GAN is applied to cross-product-domain recommendation and that the generative model generates sample data to improve recommendation results; Hao further encodes historical behavior into a user behavior feature vector and derives a target-user behavior vector for the particular target product domain i.e. Hao’s historical user behavior, click information, interests, and generated user-behavior vectors as the claimed user data of the user) (0005, 0008-0011, 0040, 0051, 0068-0069, Fig. 2), the generative Al model is associated with personalization training operations (Examiner interprets Hao's training/retraining of the GAN using behavior of the particular user to adapt the recommendation model to that user's changing interests as personalization training operations i.e. Hao ¶¶8-11, 40, 102 - Hao performs adversarial training of the generative and discriminative models based on historical user behavior. Hao further obtains click behavior from the target user, updates historical user behavior data, and retrains the GAN so that it adapts to changes in the user's interests. More particularly, Hao teaches that, when a target user browses content, a recommendation request may be triggered, the server determines candidate sample data corresponding to the target user, and that candidate sample data may be generated based on the trained generative model. The candidate data is then used to determine content to be recommended to that user (Hao ¶97). Examiner therefore interprets Hao's target-user-specific candidate/recommendation data generated using the trained generative model as personalization data associated with both a generative AI model and user data of the user and Hao further expressly teaches personalization training operations. After target recommendation information is displayed, the user's selections generate click-behavior data; the system uses the click behavior to update historical user-behavior data and retrains the generative adversarial network using the updated user data so that the GAN adapts to changes in the user's interests (Hao ¶102)) (0008-0011, 0040, 0097, 0102) that generate the automotive personalization data structure (Examiner interprets that Hao supplies the trained, user-adaptive generative-AI mechanism i.e. Hao ¶¶68-69 teaches trained generative processing of user behavior into target-user vectors and candidate sample data) (0068-0069).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, associated with a generative artificial intelligence (AI) model and user data of the user, the generative Al model is associated with personalization training operations that generate the automotive personalization data structure to support the personalized automotive upgrade guidance for the item listing system, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
As per claims 16, Gaur discloses, computer-implemented method, the method comprising (Examiner notes that the underlined limitation is disclosed by another prior art. Gaur ¶¶56–60 teaches software/model/recommendation modules executable by processors) (0056-0060):
executing personalization training operations on the training dataset to generate the generative AI model, the generative AI model is associated with personalization training operations and an automotive personalization data structure that support personalized automotive upgrade guidance for the item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur's user/vehicle-specific accessory and aftermarket-product recommendations as personalized automotive upgrade guidance; Gaur ¶¶19, 63, 68 expressly uses user plus vehicle information to recommend vehicle accessories, vehicle-protection products and aftermarket products) (0063-0068, 0019).
Gaur specifically doesn’t disclose, for an item listing system and an automotive personalization data structure, automotive personalization data is stored in the automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system, however Zheng discloses, for an item listing system (Examiner interprets Zheng's online marketplace publishing product listings as the claimed item listing system. In the combination, Hao's generative model is applied to that known marketplace personalization environment; Zheng ¶¶25-26 - Zheng's networked system is an online marketplace that responds to requests for product listings and publishes item listings for products/services) (0025-0026),
and an automotive personalization data structure (Examiner interprets Zheng's stored personalization data representation as the claimed personalization data structure. When the represented product/recommendation information is Gaur's vehicle accessory and automotive product information, it becomes an automotive personalization data structure; Zheng ¶¶57, 65-66 - ML system generates a stored data representation/vector based on user/product behavior; it contains user/presentation features, including product-image and text information and Gaur ¶¶19, 63 supplies vehicle/automotive data and personalized vehicle-product recommendation content.) (0057-0059, 0065-0066),
automotive personalization data is stored in the automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system (Examiner interprets Zheng's stored data representation as a personalization data structure i.e. Zheng ¶57 - data-representation generating module 404 generates a data representation based on user behavior relating to a product image; the data representation includes personalized presentation features and may be stored in a database record. Further, Examiner interprets the associated product-image and textual structural elements represented within Zheng's personalization data representation as the claimed image-text personalization objects i.e. Zheng ¶66 - the data representation includes a product-image identifier and text to be included in the personalized content. Zheng ¶83 further expressly identifies an alphanumeric string and a product image as represented structural elements associated with a product available for purchase; Examiner interprets Zheng's online-marketplace client UI presenting personalized product content and associated product listings as the claimed item listing system interface of an item listing system client. i.e. Zheng ¶¶67, 70 - personalized content is presented through a user interface of a client device; selection of a product image causes display of the corresponding product listing through that interface) (0057-0059, 0065-0070, 0083),
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, automotive personalization data is stored in the automotive personalization data structure comprising image-text personalization objects associated with item personalized automotive upgrade guidance for the item listing system, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
Gaur specifically doesn’t disclose, accessing a training dataset associated with training a generative artificial intelligence (AI) model, executing personalization training operations on the training dataset to generate the generative AI model, the generative AI model is associated with personalization training operations, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing automotive personalization data associated with the personalized automotive upgrade guidance and deploying the generative AI model to support generating automotive personalization data in the item listing system, however Hao discloses, accessing a training dataset associated with training a generative artificial intelligence (AI) model for an item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Hao's historical user-behavior data collected for GAN training as the claimed training dataset. Gaur independently establishes use/access of stored training sets; Hao ¶¶8, 13, 51; ¶104/S701 - obtains historical user-behavior data across a plurality of product domains for training the recommendation model. Fig. 7/S701 summarizes user behavior to obtain historical user-behavior data. Gaur ¶59 independently trains ML models using training sets stored in database 250 or received from devices/feedback. Further, Hao ¶¶8-11, 52-53, 77 expressly uses a generative model in a generative adversarial network and adversarial training the generative and discriminative models. ¶77 specifically trains generative-model parameters to obtain a trained generative model) (0008-0013, 0051-0053, 0077);
executing personalization training operations on the training dataset to generate the generative AI model, the generative AI model is associated with personalization training operations (Examiner interprets Hao's model training/retraining using individual user behavior and feedback to improve user-specific recommendations as personalization training operations; Hao ¶¶77, 102, 105 - performs adversarial training using historical user behavior and later uses target-user click behavior to update the history and retrain the GAN so it adapts to changes in the user's interests and Hao ¶77 - alternately trains the models; when the generative model is trained, its parameters are trained using a target loss function to obtain a trained generative model; alternate training yields trained generative and discriminative models) (0077, 0102-0105),
wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing automotive personalization data associated with the personalized automotive upgrade guidance (Examiner interprets Hao's training/retraining of the GAN using behavior of the particular user to adapt the recommendation model to that user's changing interests as personalization training operations i.e. Hao ¶¶8-11, 40, 102 - Hao performs adversarial training of the generative and discriminative models based on historical user behavior. Hao further obtains click behavior from the target user, updates historical user behavior data, and retrains the GAN so that it adapts to changes in the user's interests. More particularly, Hao teaches that, when a target user browses content, a recommendation request may be triggered, the server determines candidate sample data corresponding to the target user, and that candidate sample data may be generated based on the trained generative model. The candidate data is then used to determine content to be recommended to that user (Hao ¶97). Examiner therefore interprets Hao's target-user-specific candidate/recommendation data generated using the trained generative model as personalization data associated with both a generative AI model and user data of the user and Hao further expressly teaches personalization training operations. After target recommendation information is displayed, the user's selections generate click-behavior data; the system uses the click behavior to update historical user-behavior data and retrains the generative adversarial network using the updated user data so that the GAN adapts to changes in the user's interests (Hao ¶102)) (0008-0011, 0040, 0097, 0102),
and deploying the generative AI model to support generating automotive personalization data in the item listing system (Examiner interprets storing and providing the trained model for subsequent online recommendation operation as deploying the trained generative AI model. Gaur independently reinforces actual implementation/deployment of trained ML models and Hao's deployed generative model produces user-specific recommendation data for Gaur's automotive products, yielding the claimed automotive personalization data; Hao ¶94; ¶¶104-112/Fig. 7 -trained GAN is stored for subsequent use in an online cross-product-domain recommendation system; Fig. 7 distinctly shows offline training followed by the online service process. Gaur ¶61 independently teaches storing/implementing a trained ML model at the service-provider computer or sending it to a client device for implementation; Hao ¶97 - after training, candidate sample data corresponding to a target user may be generated based on the trained generative model, and recommendation content is determined therefrom) (0094-0097, 0104-0112).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, accessing a training dataset associated with training a generative artificial intelligence (AI) model, executing personalization training operations on the training dataset to generate the generative AI model, the generative AI model is associated with personalization training operations, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing automotive personalization data associated with the personalized automotive upgrade guidance and deploying the generative AI model to support generating automotive personalization data in the item listing system, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
As per claims 2, 12 and 17, Gaur specifically doesn’t disclose, further comprising a generative AI personalization engine that uses the generative AI model to generate the automotive personalization data, however Hao discloses, further comprising a generative AI personalization engine that uses the generative AI model to generate the automotive personalization data (Examiner interprets Hao's generation unit operating with the generative model of the GAN, together with the associated recommendation/training functionality, as a generative AI personalization engine because it performs generative-AI processing to produce user-specific recommendation data i.e. Hao ¶¶13-16 - Hao expressly provides an apparatus having an obtaining unit, generation unit, discriminative unit, and training unit. The generation unit generates candidate sample data according to historical user behavior using a generative model in a GAN, while the adversarial training is performed to the generative and discriminative models. Hao expressly uses the generative model of a GAN to produce data for personalized recommendation. The GAN/generative model is the claimed generative AI model i.e. Hao ¶¶14, 52-53, 97 - candidate sample data is generated from historical user behavior using a generative model in a GAN. Hao further explains that the server generates candidate sample data using the generative model and adversarial trains the GAN) (0013-0016, 0052-0053, 0097) to generate the automotive personalization data (Hao teaches generative-AI generation of user-specific recommendation data; i.e. Hao ¶97 teaches that candidate sample data corresponding to a target user may be generated based on the trained generative model, and that this data is used to determine content/recommendation information for the user) (0097).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, further comprising a generative AI personalization engine that uses the generative AI model to generate the automotive personalization data, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
As per claims 3 and 18, Gaur discloses, further comprising a generative AI personalization engine that is associated with a machine learning engine, the machine learning engine is associated with training the generative AI model (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur's model module 246 as a machine learning engine because it generates, trains, stores, and implements ML/neural-network models. In the combination, this type of ML engine is used with Hao's generative-AI personalization functionality i.e. Gaur ¶57 - model module 246 generates one or more machine-learned models, including neural networks/deep neural networks. Gaur further at ¶¶57-59 teaches model module 246 generating and training ML/neural-network models using training sets) (0057-0059).
that supports image generation and text generation for instances of automotive personalization data ((Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Gaur ¶¶19, 63, 68 - automotive recommendations include vehicle accessories, vehicle protection products and aftermarket products) (0063-0068, 0019).
Gaur specifically doesn’t disclose, further comprising a generative AI personalization engine, however Hao discloses, further comprising a generative AI personalization engine (Examiner interprets Hao's generation unit operating with the generative model of the GAN, together with the associated recommendation/training functionality, as a generative AI personalization engine because it performs generative-AI processing to produce user-specific recommendation data i.e. Hao ¶¶13-16 - Hao expressly provides an apparatus having an obtaining unit, generation unit, discriminative unit, and training unit. The generation unit generates candidate sample data according to historical user behavior using a generative model in a GAN, while the adversarial training is performed to the generative and discriminative models. Hao expressly uses the generative model of a GAN to produce data for personalized recommendation. The GAN/generative model is the claimed generative AI model i.e. Hao ¶¶14, 52-53, 97 - candidate sample data is generated from historical user behavior using a generative model in a GAN. Hao further explains that the server generates candidate sample data using the generative model and adversarial trains the GAN) (0013-0016, 0052-0053, 0097).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, further comprising a generative AI personalization engine, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
Gaur specifically doesn’t disclose, that supports image generation and text generation, however Zheng discloses, that supports image generation and text generation (Examiner interprets Zheng's ML-based generation of personalized banner/image content using trained product-image/presentation features as supporting image generation. Applied to Gaur's vehicle products, the images represent instances of automotive personalization data i.e. Zheng ¶¶46, 58, 67 - the ML system uses training data including product-image/layout features and executes to generate online banner images; personalized banner content is generated from the learned data representation. Zheng ¶58 expressly states that banner image generating module 406 generates the online banner image based on the data representation.) (0046, 0058-0067).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, that supports image generation and text generation, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
As per claims 4, 13, and 19, Gaur discloses, wherein the personalization training operations support training generative AI models based on training data comprising user data, automotive data, (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur's vehicle datasets and vehicle-product information as the claimed automotive data i.e. Gaur ¶19 - vehicle data includes make/model/year, vehicle features, vehicle accessories, protection products, aftermarket products, dealer/inventory information and related automotive information. Gaur ¶70 further teaches the ML model using credit data and vehicle data as inputs) (0019, 0070), image data, text data and item listing interfaces data (Examiner notes that the underlined limitation is disclosed by another prior art).
Gaur specifically doesn’t disclose, wherein the personalization training operations support training generative AI models based on training data comprising user data, however Hao discloses, wherein the personalization training operations support training generative AI models based on training data comprising user data (Examiner interprets Hao's adversarial training as personalization training operations for a generative AI model, while Gaur establishes the known architecture for training one or more recommendation models i.e. Hao ¶¶8-16, 40, 52-53 - historical user behavior is supplied to a generative model in a GAN, and the generative and discriminative models undergo adversarial training to produce a trained GAN for recommendation; Hao ¶¶51, 68-69 uses historical user behavior and target-user behavior vectors for generative recommendation training) (0008-0016, 0051-0053, 0068-0069, 0040).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, wherein the personalization training operations support training generative AI models based on training data comprising user data, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
Gaur specifically doesn’t disclose, image data, text data and item listing interfaces data, however Zheng discloses, image data, text data and item listing interfaces data (Examiner interprets the text and text-related features expressly supplied to the ML training process as text data; Product images and image-related presentation features used in ML training are the claimed image data i.e. Zheng ¶¶38, 42, 46 - item features include an image of the item, ML algorithms evaluate product-image features, and product-image arrangements are expressly used in training. Examiner interprets Zheng's template/layout/presentation features and user-interaction results associated with an online marketplace interface that presents and accesses product listings as item listing interfaces data. This is substantially stronger than the old Jackson mapping because Zheng is training on actual interface/presentation characteristics and interaction results) (0042-0046, 0038, 0070).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, image data, text data and item listing interfaces data, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
As per claims 5, 14, and 20, Gaur discloses, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data including personalized copy, personalized upgrade guides, and personalized upgrade products to corresponding item listing interfaces (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur's user-specific recommendations identifying vehicle accessories/products appropriate for the particular customer/vehicle as personalized upgrade guidance/guides, because the recommendation guides the user toward automotive additions or enhancements and the recommended accessories and aftermarket products tailored to user/vehicle information as personalized upgrade products i.e. Gaur ¶¶19, 63, 68 - user and vehicle data are used to recommend vehicle products/services, expressly including vehicle accessories, vehicle protection and aftermarket products and expressly recommends vehicle accessories, vehicle protection products and aftermarket products based on user/vehicle data) (0063-0068, 0019).
Gaur specifically doesn’t disclose, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data including personalized copy to corresponding item listing interfaces, however Zheng discloses, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data including personalized copy (Examiner interprets Zheng's ML/data-representation/content-generation/UI modules as the claimed personalization logic including instructions for providing personalization data; Examiner further interprets the user-specific textual content selected/represented for inclusion in Zheng's personalized marketplace presentation as personalized copy. The content is personalized because Zheng selects presentation content based on user behavior/preferences. i.e. Zheng ¶¶57-59 - data representation generating module 404 creates and stores the personalization representation; banner-image generating module 406 uses the representation to generate personalized content; UI module 408 causes display to the user and personalized online-banner sections include text 318; the personalization data representation identifies text to be included; structural elements expressly include an alphanumeric string together with a product image) (0057-0059, 0038, 0070),
to corresponding item listing interfaces (Examiner interprets Zheng's user-interface presentation associated with the corresponding marketplace product listing as the claimed corresponding item listing interface. In the combination, those listings correspond to Gaur's automotive accessories/products. i.e. Zheng ¶70 - selection of a product image causes display of the corresponding product listing, and listing data is accessed from a database and displayed through the client UI) (0070)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, wherein the automotive personalization data structure is associated with automotive personalization logic that includes instructions for providing the automotive personalization data including personalized copy to corresponding item listing interfaces, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
As per claims 6, Gaur discloses, comprising personalized copy, personalized upgrade guides, and personalized upgrade products for a plurality of item listing system interfaces for presenting instances of automotive personalization data for requests processed using a generative AI presentation engine (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets these user/vehicle-specific recommendation outputs as personalized automotive upgrade guidance/guides i.e. Gaur ¶¶63, 68 - personalized ML-based recommendations identify one or more vehicle products/services, including accessories, based on user and vehicle data and these recommended automotive accessories/aftermarket products are the claimed personalized upgrade products. Examiner further interprets the multiple client devices/interfaces through which marketplace product listings and recommendation information are presented as a plurality of item listing system interfaces. Gaur supplies plural automotive client interfaces; Gaur ¶46 teaches computing devices 204(1) … 204(N), each capable of accessing/presenting application interface 230 and receiving product/service recommendations) (0063-0068, 0046) for presenting instances of automotive personalization data for requests processed using a generative AI presentation engine (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that each presented personalized recommendation/presentation concerning a vehicle accessory/product is interpreted as an instance of automotive personalization data; Gaur ¶68 sends indications of multiple personalized vehicle products/services to a user device for presentation. Zheng ¶67generates personalized presentation content and presents it through a client-device interface) (0067-0068).
Gaur specifically doesn’t disclose, comprising personalized copy, however Zheng discloses, comprising personalized copy (Examiner interprets User-specific textual presentation content is the claimed personalized copy; Zheng ¶¶49, 66, 83 - personalized presentation contains text; the data representation expressly identifies text/alphanumeric content to be included) (0049, 0066, 0083)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, comprising personalized copy, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
Gaur specifically doesn’t disclose, for requests processed using a generative AI presentation engine, however Hao discloses, for requests processed using a generative AI presentation engine (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Hao's trained generative-model processing of user recommendation requests, when integrated with Zheng's personalized content-generation/presentation modules, as the claimed generative AI presentation engine processing requests to present personalized content; Hao ¶97 - when a target user browses content, a recommendation request is triggered; candidate sample data corresponding to the target user may be generated based on the trained generative model, and personalized recommendation information is determined therefrom. Zheng ¶¶58–59, 67 supplies the presentation-generation/UI functionality) (0058-0059, 0067-0069, 0097).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, for requests processed using a generative AI presentation engine, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
As per claims 7, Gaur specifically doesn’t disclose, wherein the personalization training operations support generating instances of automotive personalization data for a plurality of item listing interfaces, however Hao discloses, wherein the personalization training operations support generating instances of automotive personalization data for a plurality of item listing interfaces of the item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets training/retraining the GAN based on behavior of individual users for improving recommendation relevance as personalization training operations; Hao supplies trained user-specific recommendation generation; Gaurs supplies automotive product content. Accordingly, in the combination, the trained generative system produces instances of automotive personalization data; Examiner interprets Hao's trained recommendation system supporting presentation across multiple recommendation interfaces, when applied to Zheng's online marketplace/item-listing architecture, as supporting generation of personalization content for a plurality of item listing interfaces. Hao ¶101 expressly teaches recommendation interfaces, respectively illustrated in FIGS. 6A and 6B, for presenting information generated using a model trained from multiple product domains. Zheng ¶¶25-26, 70 supplies the online-marketplace/product-listing nature: the system publishes item listings and displays corresponding product listings through client interfaces) (0008-0016, 0069, 0097, 0101).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, wherein the personalization training operations support generating instances of automotive personalization data for a plurality of item listing interfaces, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
Gaur specifically doesn’t disclose, of the item listing system, however Hao discloses of the item listing system (Examiner interprets Zheng's marketplace is the claimed item listing system, and its client interfaces/product-listing displays provide the corresponding item-listing interfaces; Zheng ¶¶25-26 - networked system 102 is an online marketplace that responds to requests for product listings and publishes item listings for products/services) (0025-0026).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, the item listing system, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
As per claims 8 and 15, Gaur discloses, wherein the automotive personalization data comprises two or more: a non-generative AI data element, a generative AI data element, and a generative AI item listing interface element (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Gaur supplies conventional automotive/user data; Examiner interprets Gaur's preexisting user/vehicle/product datasets as non-generative AI data elements, because the data exists independently of generation by a generative AI model and is supplied as input to the recommendation system i.e. Gaur ¶¶19, 63-64 - Gaur uses conventional stored vehicle data and user/credit data, including make, model, year, accessories, protection products, aftermarket products, etc., as inputs to the recommendation model) (0019, 0063-0064).
Gaur specifically doesn’t disclose, a generative AI data element, and a generative AI item listing interface element, however Hao discloses, a generative AI data element, and a generative AI item listing interface element (Examiner interprets Hao's candidate sample/recommendation data generated by the trained generative model as a generative AI data element. Applied to Gaur's automotive product domain, the generated element forms part of the automotive personalization data; Hao ¶¶69, 97 - Hao generates candidate sample data based on a target user's behavior using the trained generative model; the candidate data is used to determine personalized recommendation information) (0068-0069, 0097).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, a generative AI data element, and a generative AI item listing interface element, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
As per claims 9, Gaur discloses, communicating a request associated a first user (Examiner interprets communicating the particular user's identifier through application interface 230 for requesting/retrieving personalized recommendations as communicating a request associated with a first user; Gaur ¶46 - a user operating computing device 204 accesses application interface 230 and provides a user identifier through the interface to receive product/service recommendations) (0046);
based on communicating the request associated with the first user, accessing an instance of automative personalization data (Examiner interprets the recommendation produced/retrieved for that particular user based on user and vehicle data as an instance of automotive personalization data; Gaur ¶63 - recommendation module 248 receives the user identifier through interface 230, retrieves user/credit data and vehicle data, and uses the trained ML model to recommend a product/service) (0063), the instance of personalization data is associated with an automotive data personalization interface (Examiner interprets application interface 230, when used to request and present user-specific vehicle product/service information, as the claimed automotive data personalization interface; Gaur ¶¶46, 63-65 - application interface 230 is used for receiving user information and automotive product/service recommendations; recommendation module 248 sends the recommended product/service to computing device 204 for presentation) (0046, 0063-0065);
and causing display of the instance of automotive personalization data on the automotive data personalization interface (Examiner interprets presentation of the user-specific vehicle recommendation through Gaur's application interface as causing display of the instance of automotive personalization data on the automotive data personalization interface; Gaur ¶65 - recommendation module 248 sends an indication of the product/service to computing device 204 for presentation and sends instructions to the computing device for displaying the recommendation) (0065).
As per claims 10, Gaur specifically doesn’t disclose, accessing a training dataset associated with training an instance of a generative AI model, executing personalization training operations on the training dataset to generate the instance of the generative AI model; and deploying the instance of the generative AI model to support generating instances of automotive personalized data, however Hao discloses, accessing a training dataset associated with training an instance of a generative AI model for an item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Hao's collected historical user-behavior data used for GAN training as the claimed training dataset; Hao ¶¶8, 13, 51, 104 - Hao obtains historical user behavior data in a plurality of product domains for training the recommendation system. Fig. 7/S701 summarizes online behavior data to obtain the historical user behavior dataset. Further, Hao directly teaches training a particular instance of a generative model/GAN, which Examiner interprets as the claimed generative AI model; Hao ¶77 - alternately trains the generative and discriminative models, including specifically training the generative-model parameters using a target loss function to obtain a trained generative model) (0008-0013, 0051, 0077, 0104);
executing personalization training operations on the training dataset to generate the instance of the generative AI model (Examiner interprets adversarial training and user-feedback retraining of the GAN using historical/user click behavior as personalization training operations performed on the training dataset; Hao ¶¶77, 102, 105 - Hao trains the generative/discriminative models using historical user behavior; it later retrains the GAN based on updated click behavior to adapt to changes in the user's interests and that training the generative-model parameters produces a trained generative model, and alternate training yields trained generative and discriminative models.) (0077, 0102-0105);
and deploying the instance of the generative AI model to support generating instances of automotive personalized data in the item listing system (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets storing/providing the trained recommendation model for subsequent online service, together with Gaur's express implementation of trained models at server/client, as deploying the trained model; Hao ¶94; ¶¶104-112/Fig. 7 - the trained GAN is stored for use in an online recommendation system; Fig. 7 distinguishes offline training from the subsequent online service process. Further, Hao's online generation of target-user recommendation data, when applied to Gaur's automotive accessory/product domain, as generating instances of automotive personalized data; Hao ¶97 — during online operation, candidate sample data corresponding to the target user may be generated based on the trained generative model, and personalized recommendation content is determined therefrom) (0094-0097, 0104-0112).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, accessing a training dataset associated with training an instance of a generative AI model, executing personalization training operations on the training dataset to generate the instance of the generative AI model; and deploying the instance of the generative AI model to support generating instances of automotive personalized data, as taught by Hao for the purpose to employ trained generative recommendation model in the personalization system to improve the generation and selection of user-specific automotive product content based on the user's historical and ongoing behavior.
Gaur specifically doesn’t disclose, for an item listing system and in the item listing system, however Zheng discloses, for an item listing system (Examiner interprets Zheng's online marketplace publishing product listings as the claimed item listing system. In the combination, Hao's generative model is applied to that known marketplace personalization environment; Zheng ¶¶25-26 - Zheng's networked system is an online marketplace that responds to requests for product listings and publishes item listings for products/services) (0025-0026),
in the item listing system (Zheng supplies the item-listing environment in which the generated personalized automotive recommendation content would be presented; Zheng ¶70 - personalized marketplace content is linked to a corresponding product listing displayed through the online marketplace client interface) (0070).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for accessing a request associated with a user of an item listing system, based on the request, accessing automotive personalization data, associated with item personalized automotive upgrade guidance for the item listing system, as taught by Gaur, for an item listing system and in the item listing system, as taught by Zheng for the purpose using user-specific image/text data representation and product-listing interface, thereby providing richer personalized presentation of the recommended automotive products.
Response to Arguments
With regards to § 101 rejections:
The arguments filed on January 27th, 2026, with respect to the rejection(s) of claims 1-20 under 35 U.S.C 101 have been fully considered but are unpersuasive/moot. The rejection is maintained and updated to address the amended claims.
Applicant states that the amended claims are patent eligible because the claimed automotive personalization data structure, image-text personalization objects, and personalization logic allegedly provide a “specific technical architecture” that improves item-listing system technology, computer functionality, and user-interface operation. Applicant further relies on Enfish and McRO, asserting that the claimed architecture enables multimodal upgrade guidance, structured reuse of personalization assets, and interface-conditioned delivery of upgrade content. These arguments are not persuasive. First, the claims do not recite the particular technological mechanisms that allegedly produce the asserted improvements. Claim 1 broadly recites an “automotive personalization data structure comprising image-text personalization objects” and “automotive personalization logic” including instructions for providing automotive personalization data. However, the claim does not require a particular object schema, linking relationship between the image and text objects, indexing arrangement, memory organization, retrieval mechanism, database architecture, data-reuse mechanism, or interface-selection algorithm. Likewise, although Applicant characterizes the architecture as providing “structured reuse” and “interface-conditioned delivery,” those particular technical mechanisms are not affirmatively required by the claim. The claim instead defines the data structure and logic principally according to the information they contain and the commercial result they provide, personalized automotive upgrade guidance. Under MPEP §§ 2106.04(d)(1) and 2106.05(a), an asserted technological improvement must be supported by a technical explanation in the specification and the claim itself must reflect the components or steps that provide that improvement. A bare characterization of a claimed arrangement as a “technical architecture” is insufficient when the claim does not recite the mechanism responsible for the asserted technological advance. The MPEP specifically explains that an improvement in the abstract idea itself, such as improving how commercial information is personalized or presented, is not an improvement to computer technology. Applicant’s reliance on Enfish is distinguishable. In Enfish, the claims recited a particular self-referential database structure having specific structural characteristics that changed how the computer stored and retrieved data and yielded improvements such as greater flexibility, faster searching, and reduced memory requirements. Here, by contrast, the claims do not recite a particular database structure that changes the operation of the underlying computer or database. Merely labeling stored information an “automotive personalization data structure” and specifying that it contains image-text personalization objects does not itself establish an improvement to data-storage or database technology. Applicant’s reliance on McRO is likewise unpersuasive. The claims in McRO recited specific rules that constrained the automated process and constituted the mechanism by which the improvement in computer animation was achieved. The present claims do not recite comparable technical rules governing how the generative AI model creates the image-text objects, how personalization is technically determined, or how the objects are selected, linked, retrieved, or delivered. Rather, the claim states the desired result, providing personalized automotive upgrade guidance, without claiming the particular algorithmic or architectural technique that produces that result. MPEP § 2106.05(a) expressly distinguishes claims containing a technical explanation of how an asserted improvement is achieved from claims merely invoking a computer to obtain the desired result.
Applicant also asserts improvements in “item listing system technology,” “computer functionality,” and “user interface operation.” Those asserted benefits, however, are largely improvements to the content and commercial usefulness of the recommendation: providing text and images, reusing personalization information, and presenting upgrade-specific rather than generic recommendations. The claims do not require improved processor performance, reduced memory usage, improved network operation, improved database retrieval, improved AI-model efficiency, reduced computational burden, or a new interface-rendering technique. The computer and AI components are instead used as tools to perform the personalized commercial recommendation activity. Accordingly, when the claim is considered as a whole, the automotive personalization data structure, image-text personalization objects, personalization logic, generative AI model, and item-listing interface do not transform the commercial activity into a technological improvement. They specify computer-based tools and information formats used to implement the abstract idea of providing personalized automotive upgrade guidance in an item-listing environment. Therefore, the additional elements do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
With respect to Applicant’s Step 2B argument that the recited elements are “not conventional or generic,” Applicant’s conclusory assertion alone does not establish an inventive concept. Novelty and eligibility are separate inquiries, and the mere presence of more specific claim terminology does not necessarily amount to significantly more. The claim must be considered as an ordered combination, but here the ordered combination still performs the same functions of obtaining personalization information, organizing that information, using a model to support personalized recommendations, and communicating the resulting automotive product guidance. No claimed non-generic technological arrangement changes the functioning of the computer, database, interface, or generative AI model itself. Accordingly, the claims do not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Therefore, the rejection of claims 1-20 under 35 U.S.C. §101 is maintained.
With regards to § 103 rejections:
Applicant's arguments, see pages 14-17, filed January 27th, 2026, with respect to the rejection(s) of claims 1-20 under 35 U.S.C 102/103 have been fully considered but are unpersuasive/moots on new ground of rejection.
Thus, the dependent claims that depend from independent claims 1, 11, and 16 respectively are also moots.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pat. 20230152115 (“Baughman”).
Baughman outlines a system route from a first physical location to a second physical location is divided into a set of segments using a route optimization engine. Using a user response analysis model, a response to a physical environment associated with a segment in the set of segments is scored, the scoring resulting in a score. Using a content generation model and the score, the physical environment is augmented, the augmenting combining the physical environment and a generated environment.
THIS ACTION IS MADE FINAL. 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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/GAUTAM UBALE/
Primary Examiner, Art Unit 3689
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