DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Final Office Action in response to communications received on 3/9/2026. Claims 1-12 are currently pending and have been examined. No claims have been amended.
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.
Step 1: The claims 1-7 are a system and claims 8-12 are a method. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1 and 8, taking claim 1 as a representative claim) recite:
A computer implemented system to personalize a shopping experience in a conversational commerce platform, wherein the system comprising: a hardware processor; and a memory coupled to the hardware processor, wherein the memory comprises a set of program instructions in the form of a processing subsystem, configured to be executed by the hardware processor, wherein the processing subsystem is hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules comprising :
a user interface module configured to receive input from consumers through an e-commerce platform;
an input conversion module configured to receive a payload from the user interface module , process the input, and translate processed input into text using a large language model (LLM) engine;
an engine module comprising:
a catalog facet creation module configured to inject product catalog structure, characteristics, and descriptions into a large language model (LLM) to create multiple facets of the catalog;
a facet enrichment module configured to enrich each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model;
a buyer profiling module configured to inject facet characteristics, and descriptions into a large language model (LLM) to create a typical buyer profile for each facet created;
a customer profiling module configured to profile customers based on natural language input or guided conversation and to understand customer needs using on of natural language processing , voice processing, or visual recognition techniques;
an artificial intelligence (AI) merchandizing module configured to determine and present best facets of the catalog and best featured products to the customer based on their profile , historical data , and expressed needs;
a conversational commerce module configured to enable customers to interact with a virtual assistant to find, choose, and purchase products through guided conversation;
a personalization module configured to request and present the best facets, categories , and products to customers based on their profile and needs ;
a data collection and analytics module configured to track conversations and navigation , analyze performance, and generate data for optimization; and
a training and optimization module configured to enable administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM).
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for providing a user products and promotions via a virtual assistant based on at least user inputs received from the user creating a personalized shopping experience for the customer (see paragraph 0017 of the instant application). The steps under its broadest reasonable interpretation specifically fall under sales activities. 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.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A computer implemented system to personalize a shopping experience in a conversational commerce platform, wherein the system comprising: a hardware processor; and a memory coupled to the hardware processor, wherein the memory comprises a set of program instructions in the form of a processing subsystem, configured to be executed by the hardware processor, wherein the processing subsystem is hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules comprising :
a user interface module configured to receive input from consumers through an e-commerce platform;
an input conversion module configured to receive a payload from the user interface module , process the input, and translate processed input into text using a large language model (LLM) engine;
an engine module comprising:
a catalog facet creation module configured to inject product catalog structure, characteristics, and descriptions into a large language model (LLM) to create multiple facets of the catalog;
a facet enrichment module configured to enrich each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model;
a buyer profiling module configured to inject facet characteristics, and descriptions into a large language model (LLM) to create a typical buyer profile for each facet created;
a customer profiling module configured to profile customers based on natural language input or guided conversation and to understand customer needs using on of natural language processing , voice processing, or visual recognition techniques;
an artificial intelligence (AI) merchandizing module configured to determine and present best facets of the catalog and best featured products to the customer based on their profile , historical data , and expressed needs;
a conversational commerce module configured to enable customers to interact with a virtual assistant to find, choose, and purchase products through guided conversation;
a personalization module configured to request and present the best facets, categories , and products to customers based on their profile and needs ;
a data collection and analytics module configured to track conversations and navigation , analyze performance, and generate data for optimization; and
a training and optimization module configured to enable administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM).
The additional elements of emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application – MPEP 2106.05(f).
The recitation of the large language model and the neural network merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves processing input text from a user, cataloging product information, determining buyer profile information, determining customer profile information, determining best products to present to a user based on profile, historical data and expressed needs, personalizing information, and tracking data to optimize the output, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning and/or neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claims 1 and 8 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1 and 8 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1 and 12 are ineligible.
Dependent claims 2-7 and 9-12 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1 and 12 without significantly more.
Claim 2 recites wherein the user interface module is configured to receive input from at least one of multiple platforms, wherein the multiple platforms comprise web, mobile, voice-activated devices, or a combination thereof. The additional element of the multiple platforms is recited at a high level of generality and does not integrate the judicial exception into a practical application.
Claim 3 recites wherein the catalog facet creation module uses clustering techniques to group similar products into facets. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 4 recites wherein the facet enrichment module utilizes pre-trained image generation models for generating images from textual descriptions. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 5 recites wherein the customer profiling module integrates real-time customer feedback to refine profiles. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 6 recites wherein the artificial intelligence (AI) merchandizing module incorporates seasonal trends and events into facet presentation. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 7 recites wherein the data collection and analytics module provide a dashboard for visualizing key performance indicators. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 9 recites comprising anonymizing customer data to ensure compliance with privacy regulations. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 10 recites comprising injecting, by a facet enrichment module, facet characteristics, and descriptions into a large language model (LLM) to create typical buyers’ profile for each facet. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application. The data fed into the LLM is merely reciting the LLM at the apply it level and does not integrate the judicial exception into a practical application.
Claim 11 recites comprising providing real-time inventory updates to customers during their interaction with the virtual assistant. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 12 recites comprising offering personalized promotions based on customer behaviour and preferences. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
For at least these reasons claims 1-12 are rejected 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-8, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Poliak (US 20240420208) in view of Cobb (US 20250238819).
Regarding claim 1, Poliak discloses:
A computer implemented system to personalize a shopping experience in a conversational commerce platform, wherein the system comprising: a hardware processor; and (computer system 700)
a memory coupled to the hardware processor, wherein the memory comprises a set of program instructions in the form of a processing subsystem, configured to be executed by the hardware processor, (program instructions 722 in memory 720 of Figure 7) wherein the processing subsystem is hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules comprising (modules represented in Figure 2A and Figure 5 and direction of communication indicated):
a user interface module [0055] The memory 116 may also include an end user text prompt service which allows an end user to input prompts, such as text for interaction with the system 100. In some embodiments, the web browser 120 may be used as an interface for the text prompt service. configured to receive input from consumers through an e-commerce platform; (end user text prompt 122)
an input conversion module configured to receive a payload from the user interface module ("New Prompt API Endpoint 140), process the input, and translate processed input into text using a large language model (LLM) engine; ([0010] translating the input from the end user using LLM into a intent based text search query;
an engine module comprising: (Chat Service 142)
a catalog facet creation module (an extract, transform, load (ETL) module 146) configured to inject product catalog structure, characteristics, and descriptions ([0061] The customer product catalog 160 includes product descriptions of products included in customer product catalog 160. Examiner interprets descriptions of the product to encompass characteristics of the products) into a large language model (LLM) to create multiple facets of the catalog; [0064] FIGS. 2A and 2B show a flow of information through the system 100. As shown in FIG. 2B, a product catalog is received and products are vector embedded using LLM 184 and stored in PCD 144. More specifically, as shown in FIG. 2A, the ingest API endpoint 148 receives the product catalog 160, the ETL 146 performs a transform on the product catalog 160 and feeds the transforms to the embedding API 182 and the LLM 184. Also, as shown in FIG. 2A, the LLM 184 generates embeddings and returns them to the ETL 146 via the embedding API 182. The ETL 146 creates product vectors by vectorizing the embeddings of the information associated with the products in the product catalog 160 and the product vectors are stored in PCD 144.
a customer profiling module configured to profile customers ([0010] translating the input from the end user using LLM into a intent based text search query Examiner interprets intent as profiling the customer) based on natural language input or guided conversation and to understand customer needs using on of natural language processing [0002] processing of natural language end user input related to commerce and generation of natural language and context-relevant responses and see [0060], voice processing, or visual recognition techniques; [0096]receiving user input from an end user. At block 2104, it is determined whether the end user is seeking to ask certain questions about certain products from their context. At block 2106, if it is determined that the end user is seeking to ask certain questions about certain products from their context, the method 2100 outputs the user input and products in context to a Large Language Model, and at block 2108, receives a completion that determines which product(s) the end user is intending to ask their questions about. And see example query process in [0097] Examiner interprets needs of the customer as context interpretation from asked questions
an artificial intelligence (AI) merchandizing module (the LLM (e.g., an OpenAI input) configured to determine and present best facets of the catalog and best featured products [0068] a threshold limit of relevance may be set and used to exclude products from the recall set. For example, the threshold limit may be a measure of relevance to the context of the end user text input. and promotions (click here to purchase from REI for $145 element 406) to the customer based on their profile [0096] "context from questions", historical data (history of end user inputs and responses 906), and expressed needs; [0065] As shown in FIG. 2B, an end user input (e.g., a query) is received. For example, as shown in FIG. 2A, an end user input is received, such as by new prompt API endpoint 140. Also, as shown in FIG. 2B, conversation text from the end user input is embedded using LLM 184. For example, as shown in FIG. 2A, end user input is passed to chat service 142 that uses LLM 184 and embedding API 182 to obtain embeddings from end user input. Also, as shown in FIG. 2B, products that are relevant to the conversation text are requested from the PCD 144. For example, as shown in FIG. 2A, chat service 142 may create input vectors by vectorizing the embeddings and look up the vectors in the PCD 144. As shown in FIG. 2B, products are retrieved from the PCD 144 and returned to build a new input for the LLM (e.g., an OpenAI input). The new input is passed to LLM 184 to generate a response to the end user that includes product information. For example, as shown in FIG. 2A, chat service 142 may send a list of products (e.g., as a selectable link) retrieved from PCD144 to the completion API 180 and LLM 184 to generate a completion that is returned to the chat service 142 and output to the end user via the new prompt API endpoint 140.
a conversational commerce module (chat service 142) configured to enable customers to interact with a virtual assistant to find, choose, and purchase products through guided conversation; (Shown in Figure 15 conversation between user and Brchat for a user needing a birthday gift, the chat responding with a listing of products and[0052] allowing customers to find and purchase a real in-stock product.
a personalization module (Context 904 passed to the action sequence module in 806 of Figure 8) configured to request and present the best facets (parse attributes values from queries in Figure 12), categories (relevant categories in Figure 12), and products ("here are some green T-shirts product list in Figure 15) to customers based on their profile and needs [0087] context 904 may include at least one of a chronologically ordered list of any preceding end user inputs or responses to preceding end user inputs 906, one or more lists of products that were previously presented to the end user 908, one or more lists of products selected by the end user 910, one or more lists of products viewed by the end user 912, or one or more lists of products added to a shopping cart by the end user 914. ;
a data collection and analytics module (action sequence module 914 ingesting context 904) configured to track conversations (history of end user inputs and responses 906) and navigation [0093] receiving context related to the end user input that includes a chronologically ordered list of any preceding end user inputs or responses to preceding end user inputs, and one or more lists of products that were previously presented to the end user, viewed by the end user, added to a shopping cart by the end user, or selected by the end user, or receiving a search query for products)
While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
a facet enrichment module configured to enrich each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model;
a buyer profiling module configured to inject facet characteristics, and descriptions into a large language model (LLM) to create a typical buyer profile for each facet created;
analyze performance, and generate data for optimization; and
a training and optimization module configured to enable administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM).
However Cobb teaches:
a facet enrichment module configured (computing device performing image mapping) to enrich each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions [0193] The computing device 322 may perform image mapping (at a.3.a.19), to associate images with persona profiles for visual representation. The computing device 322 may perform a standard semantic similarity comparison between persona descriptions and text descriptions of pre-generated or existing image collections (at a.3.a.20). For example: for a persona emphasizing “outdoor activity,” images featuring outdoor product use are prioritized. If no direct matches are found, fallback or generic images are assigned by the computing device 322. And see [0229-0231] using a neural network-based model (neural network in [0117];
a buyer profiling module configured (LLM generates personas [0046]) to inject facet characteristics, and descriptions into a large language model (LLM) to create a typical buyer profile for each facet created; [0036] For example, the systems and methods described herein may be configured to address the shortcomings of current solutions, such as neglecting customer demographics and behavioral patterns, leading to suboptimal content targeting, by generating personas (e.g., as is generally illustrated in FIG. 17) and/or by integrating the personas into strategy and content recommendations. For example, the systems and methods described herein may be configured to determine that “busy professionals” and “outdoor enthusiasts” are the top buyers for a product, and promote targeted imagery and messaging in appropriate sections of the listing to best capture each type of shopper. And see [0187-0190]
[…] analyze performance, and generate data for optimization; and a training and optimization module configured to enable administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM). [0229] In some embodiments, the computing device 322 may synthesize data, such as any data described herein, to generate a comprehensive content optimization strategy tailored to the target product or group of products. The computing device 322 may incorporate competitive insights, market trends, and content performance metrics into actionable recommendations for copy, imagery, and overall listing strategy. The actionable recommendations and/or any output described herein, may be displayed on a suitable display such as those described herein.
[0230] The computing device 322 may perform data aggregation. Insights are derived, by the computing device 322, from the consolidated data sources, including: attribute importance metrics (from a.2); persona analysis and demographic overlaps (from a.3); search term prioritization (from a.4); image archetypes and artistic attributes with market impact (from a.5); and/or diagram fig a.6.a demonstrates how these data sources are funneled into a unified repository for insight generation. And [0231] The computing device 322 may perform LLM-driven analysis. An LLM processes aggregated data to generate key insights within defined categories
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include a facet enrichment module configured to enrich each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model; a buyer profiling module configured to inject facet characteristics, and descriptions into a large language model (LLM) to create a typical buyer profile for each facet created; analyze performance, and generate data for optimization; and a training and optimization module configured to enable administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM), as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 2, Poliak in view of Cobb teaches the limitations set forth above. Poliak further discloses:
wherein the user interface module is configured to receive input from at least one of multiple platforms, wherein the multiple platforms comprise web, mobile, voice-activated devices, or a combination thereof. [0060] The new prompt API endpoint 140 is configured to receive end user input, such as text input via the end user text prompt 122 or web browser 120.
Regarding claim 3, Poliak in view of Cobb teaches the limitations set forth above. While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
wherein the catalog facet creation module uses clustering techniques to group similar products into facets.
However Cobb teaches:
wherein the catalog facet creation module uses clustering techniques to group similar products into facets. [0037] In some embodiments, the systems and methods described herein may be configured to identify topically coherent groups (e.g., clusters, families, and/or the like) of search terms. For example, the systems and methods described herein may be configured to address the shortcomings of current solutions, such as failing to effectively group or prioritize search terms, resulting in scattered keyword strategies, by clustering search terms into families and augmenting these clusters with market share and conversion data. For example, the systems and methods described herein may be configured to group terms, such as, “Bluetooth speaker” and “portable speaker” into a single family, with insights showing they contribute to 40% of competitor sales and 30% of target product sales. And see [0117], [0197]
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include wherein the catalog facet creation module uses clustering techniques to group similar products into facets, as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 4, Poliak in view of Cobb teaches the limitations set forth above. While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
wherein the facet enrichment module utilizes pre-trained image generation models for generating images from textual descriptions
However Cobb teaches:
wherein the facet enrichment module utilizes pre-trained image generation models for generating images from textual descriptions [0217] The computing device 322 may perform prompt construction (at a.5.d.3). A structured prompt is dynamically generated by the computing device 322 and sent to send to the LLM, including: list of archetypes with descriptions; and/or instructions for analyzing images and assigning archetypes. For example response format: JSON with archetypes as keys and Boolean indicators as values. [0218] In some embodiments, the computing device 322 may perform LLM-based classification (at a.5.d.4). The LLM receives, from the computing device 322, the prompt and analyzes each image to classify relevant archetypes. For example: a lifestyle image showing a product in use might be classified under “lifestyle image,” “outdoor setting,” and “customer interaction.” [0219] Additionally, or alternatively, the computing device 322 may use computer vision models trained on archetype datasets for faster inference and/or hybrid approaches combining LLM and computer vision to improve accuracy.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include wherein the facet enrichment module utilizes pre-trained image generation models for generating images from textual descriptions, as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 5, Poliak in view of Cobb teaches the limitations set forth above. Poliak further discloses:
wherein the customer profiling module integrates real-time customer feedback to refine profiles. [0096]receiving user input from an end user. At block 2104, it is determined whether the end user is seeking to ask certain questions about certain products from their context. At block 2106, if it is determined that the end user is seeking to ask certain questions about certain products from their context, the method 2100 outputs the user input and products in context to a Large Language Model, and at block 2108, receives a completion that determines which product(s) the end user is intending to ask their questions about. And see example query process in [0097] Examiner interprets needs of the customer as context interpretation from asked questions as shown in the chat, live chat stream is used to determine the context of the user based on inputs see chats in Figure 4, 15, and 18)
Regarding claim 7, Poliak in view of Cobb teaches the limitations set forth above. While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
wherein the data collection and analytics module provide a dashboard for visualizing key performance indicators
However Cobb teaches
wherein the data collection and analytics module provide a dashboard for visualizing key performance indicators. [0073] display an actionable report that describes a projected performance of the target product in a computer-networked marketplace relative to the at least one organic competing product also presented on the computer-networked marketplace.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include wherein the data collection and analytics module provide a dashboard for visualizing key performance indicators, as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 8, Poliak discloses:
receiving, by a user interface module, input from consumers through an e-commerce platform; [0055] The memory 116 may also include an end user text prompt service which allows an end user to input prompts, such as text for interaction with the system 100. In some embodiments, the web browser 120 may be used as an interface for the text prompt service; end user text prompt 122)
receiving, by an input conversion module ("New Prompt API Endpoint 140), a payload from the user interface module, process the input, and translate processed input into text using a large language model (LLM) engine; ([0010] translating the input from the end user using LLM into a intent based text search query;
injecting, by a catalog facet creation module, (an extract, transform, load (ETL) module 146) product catalog structure, characteristics, and descriptions ([0061] The customer product catalog 160 includes product descriptions of products included in customer product catalog 160. Examiner interprets descriptions of the product to encompass characteristics of the products) into a large language model (LLM) to create multiple facets of the catalog; [0064] FIGS. 2A and 2B show a flow of information through the system 100. As shown in FIG. 2B, a product catalog is received and products are vector embedded using LLM 184 and stored in PCD 144. More specifically, as shown in FIG. 2A, the ingest API endpoint 148 receives the product catalog 160, the ETL 146 performs a transform on the product catalog 160 and feeds the transforms to the embedding API 182 and the LLM 184. Also, as shown in FIG. 2A, the LLM 184 generates embeddings and returns them to the ETL 146 via the embedding API 182. The ETL 146 creates product vectors by vectorizing the embeddings of the information associated with the products in the product catalog 160 and the product vectors are stored in PCD 144.
profiling, by a customer profiling module, customers based on natural language input or guided conversation and to understand customer needs ([0010] translating the input from the end user using LLM into a intent based text search query Examiner interprets intent as profiling the customer) [0002] processing of natural language end user input related to commerce and generation of natural language and context-relevant responses and see [0060], voice processing, or visual recognition techniques; [0096]receiving user input from an end user. At block 2104, it is determined whether the end user is seeking to ask certain questions about certain products from their context. At block 2106, if it is determined that the end user is seeking to ask certain questions about certain products from their context, the method 2100 outputs the user input and products in context to a Large Language Model, and at block 2108, receives a completion that determines which product(s) the end user is intending to ask their questions about. And see example query process in [0097] Examiner interprets needs of the customer as context interpretation from asked questions
determining and presenting, by an artificial intelligence (AI) merchandizing module, (the LLM (e.g., an OpenAI input) best facets of the catalog and featured products and promotions (click here to purchase from REI for $145 element 406)
to the customer based on their profile [0096] "context from questions", historical data, (history of end user inputs and responses 906) and expressed needs; [0068] a threshold limit of relevance may be set and used to exclude products from the recall set. For example, the threshold limit may be a measure of relevance to the context of the end user text input. [0065] As shown in FIG. 2B, an end user input (e.g., a query) is received. For example, as shown in FIG. 2A, an end user input is received, such as by new prompt API endpoint 140. Also, as shown in FIG. 2B, conversation text from the end user input is embedded using LLM 184. For example, as shown in FIG. 2A, end user input is passed to chat service 142 that uses LLM 184 and embedding API 182 to obtain embeddings from end user input. Also, as shown in FIG. 2B, products that are relevant to the conversation text are requested from the PCD 144. For example, as shown in FIG. 2A, chat service 142 may create input vectors by vectorizing the embeddings and look up the vectors in the PCD 144. As shown in FIG. 2B, products are retrieved from the PCD 144 and returned to build a new input for the LLM (e.g., an OpenAI input). The new input is passed to LLM 184 to generate a response to the end user that includes product information. For example, as shown in FIG. 2A, chat service 142 may send a list of products (e.g., as a selectable link) retrieved from PCD144 to the completion API 180 and LLM 184 to generate a completion that is returned to the chat service 142 and output to the end user via the new prompt API endpoint 140.
determining, by the artificial intelligence (AI) merchandizing module (the LLM (e.g., an OpenAI input), best featured products or promotions to the customer based on profile, historical data, expressed needs, or a combination thereof; [0068] a threshold limit of relevance may be set and used to exclude products from the recall set. For example, the threshold limit may be a measure of relevance to the context of the end user text input. [0065] As shown in FIG. 2B, an end user input (e.g., a query) is received. For example, as shown in FIG. 2A, an end user input is received, such as by new prompt API endpoint 140. Also, as shown in FIG. 2B, conversation text from the end user input is embedded using LLM 184. For example, as shown in FIG. 2A, end user input is passed to chat service 142 that uses LLM 184 and embedding API 182 to obtain embeddings from end user input. Also, as shown in FIG. 2B, products that are relevant to the conversation text are requested from the PCD 144. For example, as shown in FIG. 2A, chat service 142 may create input vectors by vectorizing the embeddings and look up the vectors in the PCD 144. As shown in FIG. 2B, products are retrieved from the PCD 144 and returned to build a new input for the LLM (e.g., an OpenAI input). The new input is passed to LLM 184 to generate a response to the end user that includes product information. For example, as shown in FIG. 2A, chat service 142 may send a list of products (e.g., as a selectable link) retrieved from PCD144 to the completion API 180 and LLM 184 to generate a completion that is returned to the chat service 142 and output to the end user via the new prompt API endpoint 140.
enabling, by a conversational commerce module (chat service 142), customers to interact with a virtual assistant to find, choose, and purchase products through guided conversation; (Shown in Figure 15 conversation between user and Brchat for a user needing a birthday gift, the chat responding with a listing of products and[0052] allowing customers to find and purchase a real in-stock product.
requesting and presenting, by a personalization module, (Context 904 passed to the action sequence module in 806 of Figure 8) the best facets, categories, (parse attributes values from queries in Figure 12), categories (relevant categories in Figure 12) and products to customers based on their profile and needs; ("here are some green T-shirts product list in Figure 15); [0087] context 904 may include at least one of a chronologically ordered list of any preceding end user inputs or responses to preceding end user inputs 906, one or more lists of products that were previously presented to the end user 908, one or more lists of products selected by the end user 910, one or more lists of products viewed by the end user 912, or one or more lists of products added to a shopping cart by the end user 914. ;
tracking, by a data collection and analytics module, (action sequence module 914 ingesting context 904) conversations (history of end user inputs and responses 906) and navigation, [0093] receiving context related to the end user input that includes a chronologically ordered list of any preceding end user inputs or responses to preceding end user inputs, and one or more lists of products that were previously presented to the end user, viewed by the end user, added to a shopping cart by the end user, or selected by the end user, or receiving a search query for products)
While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
enriching, by a facet enrichment module, each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model;
analyse performance, and generate data for optimization; and
enabling, by a training and optimization module, administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM).
However Cobb teaches:
enriching, by a facet enrichment module, (computing device performing image mapping) each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model; [0193] The computing device 322 may perform image mapping (at a.3.a.19), to associate images with persona profiles for visual representation. The computing device 322 may perform a standard semantic similarity comparison between persona descriptions and text descriptions of pre-generated or existing image collections (at a.3.a.20). For example: for a persona emphasizing “outdoor activity,” images featuring outdoor product use are prioritized. If no direct matches are found, fallback or generic images are assigned by the computing device 322. And see [0229-0231] (neural network in [0117];
analyse performance, and generate data for optimization; and enabling, by a training and optimization module, administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM). [0229] In some embodiments, the computing device 322 may synthesize data, such as any data described herein, to generate a comprehensive content optimization strategy tailored to the target product or group of products. The computing device 322 may incorporate competitive insights, market trends, and content performance metrics into actionable recommendations for copy, imagery, and overall listing strategy. The actionable recommendations and/or any output described herein, may be displayed on a suitable display such as those described herein.
[0230] The computing device 322 may perform data aggregation. Insights are derived, by the computing device 322, from the consolidated data sources, including: attribute importance metrics (from a.2); persona analysis and demographic overlaps (from a.3); search term prioritization (from a.4); image archetypes and artistic attributes with market impact (from a.5); and/or diagram fig a.6.a demonstrates how these data sources are funneled into a unified repository for insight generation. And [0231] The computing device 322 may perform LLM-driven analysis. An LLM processes aggregated data to generate key insights within defined categories
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include enriching, by a facet enrichment module, each facet with detailed descriptions, keywords, metadata, and images generated from textual descriptions using a neural network-based model; enabling, by a training and optimization module, administrators to fine-tune the large language model (LLM) based on insights generated from data analytics and to reevaluate facets and merchandizing using the updated large language model (LLM), as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 10, Poliak in view of Cobb teaches the limitations set forth above.
While Poliak discloses the use of a large language model to determine a catalog of items best to present to a user based on user inputs, such as conversational inputs, the reference does not expressly disclose:
comprising injecting, by a facet enrichment module, facet characteristics, and descriptions into a large language model (LLM) to create typical buyers’ profile for each facet
However Cobb teaches:
comprising injecting, by a facet enrichment module, facet characteristics, and descriptions into a large language model (LLM) to create typical buyers’ profile for each facet [0036] For example, the systems and methods described herein may be configured to address the shortcomings of current solutions, such as neglecting customer demographics and behavioral patterns, leading to suboptimal content targeting, by generating personas (e.g., as is generally illustrated in FIG. 17) and/or by integrating the personas into strategy and content recommendations. For example, the systems and methods described herein may be configured to determine that “busy professionals” and “outdoor enthusiasts” are the top buyers for a product, and promote targeted imagery and messaging in appropriate sections of the listing to best capture each type of shopper. And see [0187-0190]
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak to include comprising injecting, by a facet enrichment module, facet characteristics, and descriptions into a large language model (LLM) to create typical buyers’ profile for each facet, as taught in Cobb, in order to provide efficient attribute identification and mapping functionality. For example, to address the shortcomings of current solutions, such as the time-consuming and error-prone process of determining relevant product attributes and mapping the product attributes to listings and/or search terms (paragraph 0034).
Regarding claim 12, Poliak in view of Cobb teaches the limitations set forth above. Poliak further discloses:
comprising offering personalized promotions based on customer behaviour and preferences. (see chat in Figure 4 with conversation about user's personal information regarding a trip and the output of the "purchase it from REI for $145)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Poliak (US 20240420208) in view of Cobb (US 20250238819) in further view of Rose (US 20170287044).
Regarding claim 6, Poliak in view of Cobb teaches the limitations set forth above, however does not disclose:
wherein the artificial intelligence (AI) merchandizing module incorporates seasonal trends and events into facet presentation.
However Rose teaches:
wherein the artificial intelligence (AI) merchandizing module incorporates seasonal trends and events into facet presentation. [0042] The recommendation engine 104 is further configured to “learn” from at least one feedback mechanism. [0084] product matching is performed is illustrated. As shown, the item of FIG. 2B was determined to have an overall score of 37%, which is comprised of several score modules including in this example, an 87% popularity score, a 47% weather matching score, a 12% sports seasonality score, and a 2% profile match. The popularity, weather, sports seasonality and profile match scores are derived by the recommendation engine 104 as discussed elsewhere herein. And see [0105]
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak in view of Cobb to include wherein the artificial intelligence (AI) merchandizing module incorporates seasonal trends and events into facet presentation, as taught in Rose, in order to specifically tailor its recommendation algorithms based on feedback information received from the curator and/or the consumer (Abstract).
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Poliak (US 20240420208) in view of Cobb (US 20250238819) in further view of Ahmad-Taylor (US20250061491).
Regarding claim 9, Poliak in view of Cobb teaches the limitations set forth above, however does not disclose:
comprising anonymizing customer data to ensure compliance with privacy regulations.
However Ahmad-Taylor teaches:
comprising anonymizing customer data to ensure compliance with privacy regulations [0208] FIG. 6 is a block diagram illustrating an automated compliance management system 602, according to some examples, which may be deployed as part of an interactive platform 352 hosting a chatbot system 300 (both of FIG. 3) to facilitate compliance with various data privacy and other legislative requirements, such as those of the General Data Protection Regulation (GDPR), Digital Services Act (DSA), California Consumer Privacy Act (CCPA), and other global privacy requirements. The compliance management system 602 operates with other systems 612 of a platform to implement user privacy and data protections and provide an environment in which an online platform can safely and responsibly operate.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak in view of Cobb to include comprising anonymizing customer data to ensure compliance with privacy regulations, as taught in Ahmad-Taylor, in order to provide an environment in which an online platform can safely and responsibly operate (paragraph 0208).
Regarding claim 11, Poliak in view of Cobb teaches the limitations set forth above, however does not disclose:
comprising providing real-time inventory updates to customers during their interaction with the virtual assistant.
However Ahmad-Taylor teaches:
comprising providing real-time inventory updates to customers during their interaction with the virtual assistant. [0111] This allows the chatbot system 300 to provide the user 320 with the most relevant, accurate and up-to-date information on their product query, such as whether the product is in stock, available options and attributes, delivery timelines, and the like. Having access to live inventory data across catalogs is key to responding to natural language product queries in a conversational manner.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the personalized product result from a conversational input of Poliak in view of Cobb to include comprising providing real-time inventory updates to customers during their interaction with the virtual assistant, as taught in Ahmad-Taylor, in order to provide an environment in which an online platform can safely and responsibly operate (paragraph 0208).
Response to Arguments
Applicant's arguments filed 3/9/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to the rejection under 35 USC 101, the examiner maintains the claims are rejected under 35 USC 101.
While the claims recite additional elements of the subsystem executed by a hardware processor on a server, the claim still recite limitations directed to an abstract idea such as the product catalog structure, characteristics, and descriptions used to create facets of the catalog, the detailed descriptions, keywords, metadata, and images generated from textual descriptions, creation of a typical buyer profile for each of the facet, profiling customers based on natural language input or guided conversation and to understand customer needs, determining and presenting best facets of the catalog and best featured products to the customer based on their profile , historical data , and expressed needs, enabling customers to interact with a virtual assistant to find, choose, and purchase products through guided conversation, requesting and presenting the best facets, categories , and products to customers based on their profile and needs, tracking conversations and navigation , analyze performance, and generate data for optimization, and tuning the output based on generated insights. These are methods of organizing human activity related to sales and marketing techniques. The asserted operations to overcome prior technical limitations are merely the user of high level additional elements applied to the abstract idea and thereby improving the inputs and outputs of the models which are improvements to the abstract idea and not the technology itself.
The examiner notes that the rejection does not assert the limitations can be performed by a human mind (mental process), as this is a separate consideration under the judicial exceptions. Thereby, remarks directed to the judicial exception of mental process are considered moot.
With respect to the remarks directed to the alleged improvement to the technological systems, like that of the Enfish decision and Data Engine v. Google, the examiner respectfully disagrees. Enfish reflected an improvement to computer functionality and its specification described the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility (see MPEP 2106.05(a)(I)). Unlike the claims in Enfish, the additional elements of Applicant’s claims do not pertain to an “improvement” to the functioning of a computer or to another technology (see MPEP 2106.04(a) and 2106.05(a)). The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond linking the use of the judicial exception to a particular technological environment. Here in the instant application, the claim merely lists the modules of the system and their individual processes. There is no nexus of how an improvement to the computer functionality in the current system, but rather merely recites the uses of these additional elements at the apply it level. These remarks also apply to the response under Step 2A prong two.
Additionally, the recitation of a specific model does not preclude the model from still being recited as applying the idea using a computer. Again, the claim merely lists the models of the system with high level functional language and not that of how the models and modules work in an interconnected manner to integrate the judicial exception into a practical application. For these reasons the claims of the instant application do not recite limitations like those found in the McRO and DDR Holdings decisions. The fact patterns of these case differ.
With respect to the remarks directed to Step 2B, when the claim limitations are viewed in an ordered combination, the examiner asserts the claims still remain rejected under 35 USC 101 for the reasons set forth above. The claim merely recites individual modules that are used to process data sets directed to abstract ideas with a high level of generality.
For these reasons all claims remain rejected under 35 USC 101.
With respect to the remarks directed to 35 USC 103, the examiner maintains the claims remain rejected.
With respect to the remarks directed to the reference Poliak, the examiner first asserts that the instant specification discusses facets as a logical grouping of products based on their shared attributes and the creation module implements this organization of the products ([0024]). At the level of detail recited in the claim language, the examiner maintains Poliak discloses this limitation. Based on the vectorized embeddings products from product catalogs items similar or matching that which is requested can be retrieved. The claim is not requiring the retrieval of the facet be limited to something more specific like the example of “beginner running gear”. Terms such as characteristics and description data under broadest reasonable interpretation do not require this narrow interpretation of the claim language. In this same vein, the claim simply requires an AI module to determine and present the “best” facets of the catalog and “best” featured products. Under the broadest reasonable interpretation, a threshold limit of relevance does teach “best” as the term is given its plain meaning in the claim interpretation. For example, “most relevant” can be “best”, as shown in the prior art rejection in Poliak [0068].
As to the discussion of customer profiling, under the broadest reasonable interpretation of profiling a customer, determining the intent of a user’s search/query is a form of customer profiling. This is especially true when using natural language processing to understand the needs of the customer, as specifically stated in the claim language. Additionally, the collected user inputs of the end user recited in [0087] of Poliak are a form of profiling/profile as it is data representative of the user.
With respect to the remarks directed to the reference Cobb, the examiner first notes the claim interpretation above under the broadest reasonable interpretation is still applied here, in particular to the term facet. As Poliak discloses the determining of the intent of the customer based on user’s inputs and profiling customers through the use of natural language processing and Cobb goes further to teach creating personas that can be used for recommending products, the examiner maintains the rejection. As a facet is a logical grouping of products based on their shared attributes and the creation module implements this organization of the products ([0024]), top buyers within a persona are correlated to top products for such persona in Cobb ([0036, at least). With that interpretation, the Cobb reference also does teach the enriching of the facet as it associates images and other metadata with the identified personas for a visual representation.
With respect to the remarks directed to Cobb’s use of the LLM driven insight generation, the examiner asserts the LLM of Cobb functions as an LLM would and finely tunes the data throughout the process of the machine learning and as such generates updated data analytics that are reevaluated through the learning process. This is the manner in which the LLM recited in Cobb [0229] is able to generate a comprehensive optimization strategy which is required in the claim.
With respect to the remarks directed to the combination of Poliak and Cobb, the examiner maintains the combination of references is proper. The examiner first notes the claims are constructed in such a manner that the modules are listed in as individual modules of a system and not that of an interconnected module system working together, as described in the remarks. Second, the examiner has set forth reasoning as to why one of ordinary skill in the art would be motivated to combine the references. As state in MPEP 2145 However, "[a]ny judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant’s disclosure, such a reconstruction is proper." In re McLaughlin, 443 F.2d 1392, 1395, 170 USPQ 209, 212 (CCPA 1971). "A factfinder should be aware, of course, of the distortion caused by hindsight bias and must be cautious of arguments reliant upon ex post reasoning. . . . Rigid preventative rules that deny factfinders recourse to common sense, however, are neither necessary under our case law nor consistent with it." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 421, 82 USPQ2d 1385, 1397 (2007) (internal quotations omitted). Applicants may also argue that the combination of two or more references is "hindsight" because "express" motivation to combine the references is lacking. However, there is no requirement that an "express, written motivation to combine must appear in prior art references before a finding of obviousness." Ruiz v. A.B. Chance Co., 357 F.3d 1270, 1276, 69 USPQ2d 1686, 1690 (Fed. Cir. 2004). See KSR, 550 U.S. at 402, 82 USPQ2d at 1389 ("The diversity of inventive pursuits and of modern technology counsels against confining the obviousness analysis by a formalistic conception of the words teaching, suggestion, and motivation, or by overemphasizing the importance of published articles and the explicit content of issued patents.") See also Uber Techs., Inc. v. X One, Inc., 957 F.3d 1334, 1339-40, 2020 USPQ2d 10476 (Fed. Cir. 2020) ("[W]e hold that the Board erred when it determined that a person of ordinary skill in the art would not have been motivated to combine the teachings of Okubo with Konishi's server-side plotting to render obvious the limitation ‘software ... to transmit the map with plotted locations to the first individual.’ This combination does not represent ‘impermissible hindsight’…. Rather, because Okubo's terminal-side plotting and Konishi's server-side plotting were both well known in the art, and were the only two identified, predictable solutions for transmitting a map and plotting locations, it would have been obvious to substitute server-side plotting for terminal-side plotting in a combination of Okubo and Konishi."). In the instant application combination of prior art, Cobb is working to address the shortcomings of the time consuming and error prone process of determining relevant products relevant to search queries. Poliak is also seeking to increase accuracy of product retrieval from conversational inputs from end users ([002]). As such, the combination is not improper hindsight.
For the reasons above the dependent claims and corresponding claim rejections are maintained.
Relevant Art Not Cited
Beauchamp (US 20240289365) using a LLM to process a search query and enhance the search query to target the result to the user.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/7/2026