DETAILED ACTION
Response to Amendment
This action is in response to the response to the amendment filed on 08/13/2026. Claims 1, 14, and 20 have been amended. Claims 1-9, 11-16, and 18-20 are pending and currently under consideration for patentability.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
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 1-9, 11-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1-9, 11-16, and 18-20 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1-9, 11-13 recite a method, claims 14-16, 18, 19 recite a non-transitory computer-readable medium, and claim 20 recites a system. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1-9, 11-16, and 18-20 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Independent Claims 1, 14, and 20 recite limitations directed to the abstract idea including “receiving a natural language request to generate a digital flyer, wherein the natural language request includes one or more design conditions for the digital flyer, wherein at least one design condition specifies one item to focus the digital flyer on; accessing an item catalog storing item data; generate the digital flyer by identifying the one item specified by the one design condition from the item data and one or more other items similar to the one item, wherein the query further includes the natural language request comprising the one or more design conditions and the item data accessed from the item catalog; receiving a batch of one or more digital flyers generated; parse the first digital flyer into a plurality of image segments, wherein each image segment is classified into one of a plurality of segment categories including one segment category relating to images of items; match the image segment to an item in the item catalog; augmenting the first digital flyer to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment; and causing presentation of the augmented first digital flyer.” These further limitations are not seen as any more than the judicial exception. Claims 1, 14, and 20 recite additional limitations including “from a client device; generating a query for a machine-learned generative model including a prompt; providing the query to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model on the query; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded; and on an electronic display of a client device of a user.” Causing presentation of augmented first digital flyer based on a batch of digital flyers determined according to a query including design conditions and accessed item data is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) because the claims are directed to managing a relationship between parties in order to cause presentation of an augmented first digital flyer to a user. The Specification confirms that the claimed subject matter is a longstanding commercial practice. “[R]etailers create weekly flyers to drive traffic,” built around “cornerstone sales” and complementary sales. “These flyers are generally built by retailer employees or operators, who make decisions about what items to promote,” and the task “requires time and effort from human retailer users.” (See ¶¶ [0002] [0003] of Applicant’s originally filed specification). Likewise, flyer augmentation was performed by “human operators [who] identify the different elements of the flyer in the image, associate the promoted items with the items in the catalog.” (See ¶ [0055] of Applicant’s originally filed specification). Furthermore, causing presentation of augmented first digital flyer determined according to a query including design conditions and accessed item data is also considered to be fall under another grouping of abstract idea, specifically, Mental Processes; such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to receiving information (i.e. request to generate a digital flyer), accessing information (i.e. item catalog data), providing information (i.e. query to model), receiving information (i.e. a batch of digital flyers) and causing presentation of information (i.e. digital flyers in the batch for presentation) which can all be performed in the human mind. Therefore, under Step 2A, Prong I, Claims 1, 14, and 20 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1, 14, and 20 recite additional limitations including “from a client device; generating a query for a machine-learned generative model including a prompt; providing the query to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model on the query; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded; and on an electronic display of a client device of a user.” The additional limitations reciting – “from a client device; for a machine-learned generative model; to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model; applying an image segmentation model to a first digital flyer to parse the first digital flyer into a plurality of image segments; applying an item matching model to each image segment classified into the segment category relating to images of items to match the image segment to an item in the item catalog; and on an electronic display of a client device” are not found to integrate the judicial exception into a practical application because they are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. client device/model serving system/machine-learned generative model/image segmentation model/item matching model, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1, 14, and 20 recite additional limitations including “from a client device; generating a query for a machine-learned generative model including a prompt; providing the query to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model on the query; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded; and on an electronic display of a client device of a user.” The additional limitations reciting – “from a client device; for a machine-learned generative model; to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model; applying an image segmentation model to a first digital flyer to parse the first digital flyer into a plurality of image segments; applying an item matching model to each image segment classified into the segment category relating to images of items to match the image segment to an item in the item catalog; and on an electronic display of a client device” do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 14, and 20 also recite additional limitations including – “generating a query for a machine-learned generative model including a prompt; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; and augmenting the first digital flyer by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment.”
The claims recite additional limitations including – “generating a query for a machine-learned generative model including a prompt.” However, merely generating a query/prompt for a machine learning model to generate content is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to ¶ [0057] of U.S. Publication 2019/0080207 to Chang teaches generating a query/prompt for a neural network model.
The claims reciting additional limitations including – “applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items.” However, merely applying a model to images in order to segment the images into different categories is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to ¶¶ [0037] [0038] of U.S. Publication 2019/0258895 to Sacheti teaches object detection with categorical classification of detected objects and selectable “hot spot” indicators on detected objects in the image.
The claims reciting additional limitations including – “augmenting the first digital flyer by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment.” However, merely adding UI elements to the content in order to receive input from users is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to Col. 7 Lines 6-9 of U.S. Patent 10,863,230 to Pham; “In conventional overlay UI applications or user interface applications that utilize overlay elements, users may provide input or place the overlay UI elements 204, 206, and 208 in any area or portion of user interface 200. For example, the user (player 212) may provide input to move overlay UI element 204 to position 220 which would obscure the primary portion or area of content 218 from viewers who are consuming the content provided by user interface 200.”
Independent claims 1, 14, and 20 do not include additional elements or a combination of elements that result in the claims amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe generic computer-based elements, ¶ [0132], for implementing “computer processor”, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 2-9, 11-13, and 15, 16, 18, and 19 further recite independent claims 1 and 14, respectively. Dependent claims 2-9, 11-13, 15, 16, 18, and 19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in Claims 1, 14, and 20. For example, claims 2-9, 11-13, 15, 16, 18, and 19 describe the limitations for providing a digital flyer for presentation to a user based on a batch of digital flyers determined according to a query including design conditions and accessed item data – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-9, 11-13, 15, 16, 18, and 19, there are no additional elements introduced. For example, dependent claims 9, 11, 12, 18, and 19 recite applying a multimodal model / object recognition algorithm / natural language processing algorithm. However, merely applying a model/algorithm to data in order to determine data does not integrate the judicial exception into a practical application because they are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims 2-9, 11-13, 15, 16, 18, and 19 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Allowable Subject Matter
Claims 1-9, 11-16, and 18-20 are allowable over the prior art.
Independent claims 1, 14, and 20 recite – “receiving, from a client device, a natural language request to generate a digital flyer, wherein the natural language request includes one or more design conditions for the digital flyer, wherein at least one design condition specifies one item to focus the digital flyer on; accessing an item catalog storing item data; generating a query for a machine-learned generative model including a prompt to generate the digital flyer by identifying the one item specified by the one design condition from the item data and one or more other items similar to the one item, wherein the query further includes the natural language request comprising the one or more design conditions and the item data accessed from the item catalog.”
The Gupta, Deng, Jain, and Sheth references do not explicitly disclose the limitations of the invention; specifically utilizing a prompt to generate a digital flyer by identifying, from item data accessed from an item catalog, the one item specified by a design condition of a natural language request, together with one or more other items similar to that item; and the natural language request itself and the accessed item data. This uniquely distinct feature alongside the combination of limitations in independent claims 1, 14, and 20 render the claims allowable over the prior art.
Response to Arguments
Applicant’s arguments see pages 1-6 of the Remarks disclosed, filed on 08/13/2026, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 1-9, 11-16, and 18-20 have been considered but are not persuasive:
The Applicant asserts “First, these limitations are not excluded methods of organizing human activity. The MPEP provides that the types of excluded methods include: "fundamental economic principles or practices commercial or legal interactions and managing personal behavior or relationships or interactions between people MPEP § 2106.04(a)(2) II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY. Generating a query for a machine- learned generative model that identifies items from an item catalog, applying an image segmentation model to parse a digital flyer into image segments, and applying an item matching model to match those image segments to catalog items are not fundamental economic principles or practices, commercial or legal interactions, or the management of personal behavior, relationships, or interactions between people. These limitations do not recite any agreement, transaction, or legal obligation between people, nor do they manage any relationship or interaction between people. Rather, they recite a specific technical process for generating a query for, and processing the output of, a machine-learned generative model using computer- implemented image segmentation and item-matching models. As such, these limitations cannot be characterized as reciting any excluded method of organizing human activity. Second, these limitations are not mental processes. The MPEP defines "a mental process (thinking) that 'can be performed in the human mind, or by a human using a pen and paper' to be an abstract idea." MPEP § 2106.04(a)(2) III. MENTAL PROCESSES (citing CyberSource Corp. V. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011). Generating a query for a machine-learned generative model requires computationally identifying, from item data stored in an item catalog, the one item specified by a design condition and one or more other items similar to that item, and then formulating a query incorporating that identification together with the natural language request and the item data. This identification of similar items from a structured item catalog, and the corresponding formulation of a query for a machine-learned generative model, is not a process that a human can practically perform in his or her mind, or with pen and paper, at the speed and scale required to generate a digital flyer in response to a request. Likewise, applying an image segmentation model to parse a digital flyer into a plurality of image segments, and applying an item matching model to match an image segment to an item in the item catalog, requires computational image analysis that has no practical analog in human mental observation of an image. These limitations are therefore necessarily rooted in computer technology, rather than merely reciting the use of a generic computer to perform an otherwise mental task. As such, these limitations cannot be characterized as a mental processes abstract idea.” The Examiner respectfully disagrees. Independent Claims 1, 14, and 20 recite limitations directed to the abstract idea including “receiving a natural language request to generate a digital flyer, wherein the natural language request includes one or more design conditions for the digital flyer, wherein at least one design condition specifies one item to focus the digital flyer on; accessing an item catalog storing item data; generate the digital flyer by identifying the one item specified by the one design condition from the item data and one or more other items similar to the one item, wherein the query further includes the natural language request comprising the one or more design conditions and the item data accessed from the item catalog; receiving a batch of one or more digital flyers generated; parse the first digital flyer into a plurality of image segments, wherein each image segment is classified into one of a plurality of segment categories including one segment category relating to images of items; match the image segment to an item in the item catalog; augmenting the first digital flyer to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment; and causing presentation of the augmented first digital flyer.” These further limitations are not seen as any more than the judicial exception. Claims 1, 14, and 20 recite additional limitations including “from a client device; generating a query for a machine-learned generative model including a prompt; providing the query to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model on the query; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded; and on an electronic display of a client device of a user.” Causing presentation of augmented first digital flyer based on a batch of digital flyers determined according to a query including design conditions and accessed item data is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) because the claims are directed to managing a relationship between parties in order to cause presentation of an augmented first digital flyer to a user. The Specification confirms that the claimed subject matter is a longstanding commercial practice. “[R]etailers create weekly flyers to drive traffic,” built around “cornerstone sales” and complementary sales. “These flyers are generally built by retailer employees or operators, who make decisions about what items to promote,” and the task “requires time and effort from human retailer users.” Spec. ¶¶ [0002]–[0003]. Likewise, flyer augmentation was performed by “human operators [who] identify the different elements of the flyer in the image, associate the promoted items with the items in the catalog.” Spec. ¶ [0055]. The claims recite performing these same commercial activities. Furthermore, causing presentation of augmented first digital flyer determined according to a query including design conditions and accessed item data is also considered to be fall under another grouping of abstract idea, specifically, Mental Processes; such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to receiving information (i.e. request to generate a digital flyer), accessing information (i.e. item catalog data), providing information (i.e. query to model), receiving information (i.e. a batch of digital flyers) and causing presentation of information (i.e. digital flyers in the batch for presentation) which can all be performed in the human mind.
The Applicant also asserts “Such is the case with the present claims. The additional elements embody an improvement to the technical field of automated digital content generation. Rather than presenting a digital flyer generated in the abstract, the additional elements integrate a machine- learned generative model with an item catalog by generating a query that incorporates item data identifying a specific item and similar items, so that the model serving system generates a digital flyer tethered to actual catalog items rather than an untethered natural-language description. The additional elements further apply an image segmentation model to parse the resulting digital flyer into discrete image segments, and apply an item matching model to computationally match each such image segment to a specific item in the item catalog, so that a user-interactable element can be overlaid on the image segment to perform an action related to the matched item. This combination of a generative model constrained by catalog-derived query data, an image segmentation model, and an item matching model addresses the specific technical problem of generating digital flyer content that is both automatically produced by a generative model and actionable with respect to a retailer's actual item catalog, which conventional generative content tools do not achieve.” The Examiner respectfully disagrees. Claims 1, 14, and 20 recite additional limitations including “from a client device; generating a query for a machine-learned generative model including a prompt; providing the query to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model on the query; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded; and on an electronic display of a client device of a user.” The additional limitations reciting – “from a client device; for a machine-learned generative model; to a model serving system for execution by the machine-learned generative model; from the model serving system; by executing the machine-learned generative model; applying an image segmentation model to a first digital flyer to parse the first digital flyer into a plurality of image segments; applying an item matching model to each image segment classified into the segment category relating to images of items to match the image segment to an item in the item catalog; and on an electronic display of a client device” are not found to integrate the judicial exception into a practical application because they are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. client device/model serving system/machine-learned generative model/image segmentation model/item matching model, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,).
The Applicant finally asserts “The additional elements are not so widely prevalent. Wide prevalence requires a high degree of understanding among artisans in the field, such that it need not be described in detail. See id., further referencing 35 U.S.C. § 112(a). The additional elements are not so widely prevalent as to render them conventional, routine, or well understood. Critically, the prior art does not teach nor suggest the additional elements, specifically the claimed combination of a machine-learned generative model constrained by item-catalog data, an image segmentation model, and an item matching model that computationally links AI-generated image segments to specific catalog items, as further discussed below under the arguments against the rejections under 35 U.S.C. § 103.” The Examiner respectfully disagrees. Claims 1, 14, and 20 also recite additional limitations including – “generating a query for a machine-learned generative model including a prompt; applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items; and augmenting the first digital flyer by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment.” With respect to the additional limitations including – “generating a query for a machine-learned generative model including a prompt.” However, merely generating a query/prompt for a machine learning model to generate content is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to ¶ [0057] of U.S. Publication 2019/0080207 to Chang teaches generating a query/prompt for a neural network model. With respect to the additional limitations including – “applying an image segmentation model to a first digital flyer; applying an item matching model to each image segment classified into the segment category relating to images of items.” However, merely applying a model to images in order to segment the images into different categories is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to ¶¶ [0037] [0038] of U.S. Publication 2019/0258895 to Sacheti teaches object detection with categorical classification of detected objects and selectable “hot spot” indicators on detected objects in the image. With respect to the additional limitations including – “augmenting the first digital flyer by overlaying one or more user-interactable elements on one or more image segments, wherein each user-interactable element is encoded to, responsive to a user interaction with the image segment, perform one or more actions related to the item matched to the image segment.” However, merely adding UI elements to the content in order to receive input from users is seen as simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. For example, according to Col. 7 Lines 6-9 of U.S. Patent 10,863,230 to Pham; “In conventional overlay UI applications or user interface applications that utilize overlay elements, users may provide input or place the overlay UI elements 204, 206, and 208 in any area or portion of user interface 200. For example, the user (player 212) may provide input to move overlay UI element 204 to position 220 which would obscure the primary portion or area of content 218 from viewers who are consuming the content provided by user interface 200.” Independent claims 1, 14, and 20 do not include additional elements or a combination of elements that result in the claims amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe generic computer-based elements, ¶ [0132], for implementing “computer processor”, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Therefore, the rejection(s) of claim(s) 1-9, 11-16, and 18-20 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Applicant’s arguments see pages 6-8 of the Remarks disclosed, filed on 08/13/2026, with respect to the 35 U.S.C. § 103 rejection(s) of claim(s) 1-9, 11, 12, 14-16, and 18-20 over Gupta in view of Deng and Jain with claim 13 being rejected in further view of Sheth have been considered and are persuasive. Applicant asserts “The references, alone or in combination, do not teach the amended step of "generating a query for a machine-learned generative model including a prompt to generate the digital flyer by identifying the one item specified by the one design condition from the item data and one or more other items similar to the one item, wherein the query further includes the natural language request comprising the one or more design conditions and the item data accessed from the item catalog." The Office Action concedes that Gupta does not disclose accessing an item catalog storing item data, and instead relies on Deng for this limitation. See Office Action, p. 12. And, in fact, Gupta's query for its generative model is formed from a natural language "characteristic" or objective (e.g., an invitation to a hot air balloon festival), and is not generated by identifying an item, and one or more other items similar to that item, from item data accessed from an item catalog, as recited in claim 1. See Gupta, ¶¶ [0031]-[0035]. The Action consequently relies on Deng and Jain to provide the teaching of the claimed step. However, Deng's disclosure of aggregating store trend data into product catalogs, and Jain's disclosure of matching a candidate product to a reference product for price-comparison purposes, do not remedy this deficiency. To start, Deng does not teach the amended step of generating the machine-learning query with the natural language request and the item data. The Office Action relies on Deng solely for accessing an item catalog storing item data, Deng, ¶ [0063], and for augmenting the first digital flyer with a user-interactable element, Deng, ¶¶ [0114]-[0115]. Office Action, pp. 12-14. At the first cited portion, Deng describes logging store trend data and aggregating it into product catalogs after the fact. Deng, ¶ [0063]. This first portion does not describe generating any query for a machine-learned generative model, let alone a query that incorporates a natural language request together with item data identifying a specified item and similar items. Further Deng at the second portion describes an advertisement-feedback overlay, such as a "Why Am I Seeing This Ad" link that lets a user manage advertisement preferences. Deng, ¶¶ [0114]-[0115]. This also has nothing to do with query formation for a generative model. Therefore Deng does not disclose, any teaching of generating a query from the natural language request and the item data accessed from the item catalog. Jain, too, doesn't teach the amended step of generating the machine-learning query with the natural language request and the item data. Jain is directed to comparing a candidate product listing against a reference product listing to identify lower-priced matching offers. Jain: col. 2 11. 12-14, col. 17 11. 58-67. Identification of lower-priced matching offers is not related to generating a digital flyer leveraging a generative model executed on a query comprising the natural language request and item data. As such, Jain also does not remedy the deficiency of Gupta.” The Examiner agrees and would also like to note that the claims recite “receiving, from a client device, a natural language request to generate a digital flyer, wherein the natural language request includes one or more design conditions for the digital flyer, wherein at least one design condition specifies one item to focus the digital flyer on; accessing an item catalog storing item data; generating a query for a machine-learned generative model including a prompt to generate the digital flyer by identifying the one item specified by the one design condition from the item data and one or more other items similar to the one item, wherein the query further includes the natural language request comprising the one or more design conditions and the item data accessed from the item catalog.” The Gupta, Deng, Jain, and Sheth references do not explicitly disclose the limitations of the invention; specifically utilizing a prompt to generate a digital flyer by identifying, from item data accessed from an item catalog, the one item specified by a design condition of a natural language request, together with one or more other items similar to that item; and the natural language request itself and the accessed item data. This uniquely distinct feature alongside the combination of limitations in independent claims 1, 14, and 20 render the claims allowable over the prior art. Therefore, the rejection(s) of claim(s) 1-9, 11-16, and 18-20 under 35 U.S.C. § 103 has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are cited to further show the state of the art:
U.S. Publication 2019/0080207 to Chang for disclosing The classification apparatus includes a reader apparatus configured to receive visual information and textual information associated with the visual information and detect query relevant categorization information regarding products or service of interest to a user from the visual information and textual information associated with the visual information, a localize and identify apparatus configured to receive the visual information and the query relevant categorization information and selectively reduce the visual information to a relevant visual representation and detect further categorization information based on the relevant visual representation, and a deep learning processor apparatus comprising a unit classifier and a type classifier, wherein the unit classifier correlates the relevant visual representation with a specific product identification code, and the type classifier correlates the relevant visual representation with a type considered represented in the relevant visual representation.
U.S. Publication 2019/0258895 to Sacheti for disclosing object detection processing of image content that categorically classifies specific objects within image content. Exemplary object detection processing may be utilized to enhance visual search processing including content retrieval and curation, among other technical advantages. An exemplary object detection model is implemented to categorically classify an object. In doing, so an exemplary object detection model may classify objects based on: analysis of specific objects within image content, positioning of the objects within the image content and intent associated with the image content, among other examples. The object detection model generates exemplary categorical classification(s) for specific data objects, which may be propagated to enhance processing efficiency and accuracy during visual search processing. Exemplary categorical classifications may comprise hierarchical classifications of a detected object that can be used to retrieve, curate and surface content that is most contextually relevant to a detected object.
U.S. Publication 2024/0362427 to Gupta for disclosing generating digital content, a computing device implements a generation system to receive a user input specifying a characteristic for digital content. The generation system generates input text based on the characteristic for processing by a first machine learning model. Output text generated by the first machine learning model based on processing the input text is received. The output text describes a digital content component. The generation system generates the digital content component by processing the output text using a second machine learning model. The generation system generates the digital content including the digital content component for display in a user interface based on the characteristic.
U.S. Publication 2022/0377424 to Deng for disclosing a system for providing dynamic digital content may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a plurality of data feeds. The processor may further analyze the data feeds to identify values for parameterized variables. A plurality of deep learning (DL) models can be trained to obtain product attribute data from the data feeds. The processor may then identify rules or triggers based on the values of the parameterized variables. The rules and/or triggers cause the processor to dynamically generate or select digital content and transmit the digital content to user communication devices of selected audience.
U.S. Patent 10,949,907 to Jain for disclosing generating a list of products each matching a reference product are disclosed. A user query is first received, and multi-modal attribute data for the reference product are determined, with each data mode being a type of product characterization having a modality selected from a text data class, categorical data, a pre-compared engineered feature, audio, image, and video. Next, a first list of candidate products is determined based on a product match signature, and a second list of candidate products is generated from the first, wherein for at least one given candidate product, a deep learning multi-modal matching model is selected to determine whether a match is found. Lastly, the second list is filtered to remove outliers and to generate the list of matching products. Also disclosed are benefits of the new methods and systems, and alternative embodiments of the implementation.
U.S. Publication 2018/0189620 to Sheth for disclosing a system generates a first layer of information that includes in combination information from the product label and attributes determined from the information from the product label but not listed on the product label. The system also generates a second layer of information that populates the second layer of information with information from the first layer of information, receives changes to the second layer of information from one of the retailer and the brand owner associated with the populated information from the first layer of information, and publishes the second layer of information with the changes from one of the retailer and the brand owner to the portion of the electronic label. The system accepts changes from one of the retailer and the brand owner to the second layer of information and prevents changes to the first layer of information from one of the retailer and the brand owner.
U.S. Publication 2023/0214899 to Martinez for disclosing processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.
U.S. Publication 2024/0126997 to Bent for disclosing obtaining via a conversational campaign assistant interface, by a custom language model, natural language input. The method includes generating, by the custom language model, an output comprising a predicted user intent. The method includes determining actions to perform and determining a natural language response. The method includes transmitting, to an action component, the action data structure comprising executable instructions that cause the action component to automatically perform operations associated with completing the action. The method includes transmitting to the conversation campaign assistant interface, the response data structure comprising the natural language response to be provided for display to a user via the conversational campaign assistant interface. The method includes obtaining user input indicative of a validation of the action data structure or the response data structure and updating the custom language model based on the user input.
U.S. Patent 11,589,128 to Greiner for disclosing providing an interactive, program based product introduction experience are described. In one example, video content may be captured, enriched with product data, and provided on-demand to interactive user devices. Users viewing the content may be presented with supplemental information or resources allowing those users to obtain more information about objects appearing in the video, or purchase products that may be a sociopathic with those objects. The objects appearing in the video may be passively up hearing, rather than explicitly introduced or offered for sale as part of the original video content. However, the supplemental information allows such users to discover and/or purchase new products through interactivity with the video content.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 extension fee 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 Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pairdirect.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
September 23, 2026
