DETAILED ACTION
Status of Claims
This action is in reply to the response received on 25 February 2026.
Claims 11-20 have been withdrawn.
Claims 1-10 are pending and have been examined.
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 .
Election/Restrictions
In the response received on 25 February 2026, the claims in Group I (claims 1-10) elected by the Applicant without traverse, are acknowledged.
Information Disclosure Statement
The Information Disclosure Statements filed on 20 March 2024, 04 April 2025, and 21 November 2025, have been considered. The initialed copy of the Forms 1449 is enclosed herewith.
Claim Objections
Claims 9 and 10 are objected to because of the following informalities: the claims both recite causing display of the product search query results on a graphical user interface, which appears to be a typographical error as the claims depend from claim 1 and which already recites a graphical user interface (line 15, claim 1). The claims should both recite causing display of the product search query results on [[a]] the graphical user interface Appropriate correction is required.
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 1-10 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-10 are directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of identifying a plurality of recommended products. Specifically, representative claim 1 recites the abstract idea of:
providing a product listing system having a seed product;
accessing the seed product;
identifying a plurality of candidate products associated with the seed product using a product knowledge graph,
wherein the product knowledge graph is associated with a knowledge graphic service;
identifying a plurality of recommended products, wherein the plurality of recommended products are a subset of the plurality of candidate products; and
communicating the plurality of recommended products to cause generation of the plurality of recommended products to a user.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of identifying a plurality of recommended products, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recite the steps of providing a product listing system having a seed product, accessing the seed product, identifying candidate products associated with the seed product using the product knowledge graph, identifying a plurality of recommended products to provide to a user, where the recommended products are a subset of the candidate products, thereby making this a sales activity or behavior.
The Examiner additionally notes that that the steps of identifying a plurality of candidate products associated with the seed product using a product knowledge graph and identifying a plurality of recommended products that are a subset of the plurality of candidate products, would fall into the enumerated grouping of mental processes. A mental process is defined as and includes “concepts performed in the human mind (including an observation, evaluation, judgement, and opinion)” (see MPEP 2106.04(a)(2)(III)). In this case, the steps of identifying, would be considered a concept performed in the human mind, such as an observation or an evaluation. Thus, representative claim 1 recites an abstract idea that also falls into the grouping of mental processes.
Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: one or more computer processors, computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, a generative AI knowledge graph service, a graphical user interface.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., identifying a plurality of recommended products) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Additionally, the Examiner notes that the claim recites the step communicating the plurality of recommended products to cause generation of the plurality of recommended products on a graphical user interface, which is insignificant extra-solution activity. Extra-solution activity can be understood as activities that are incidental to the primary process or product that are merely a nominal or tangential addition the claim (see MPEP 2106.05(g)). In this case, the activity of communicating the plurality of recommended products to cause generation of the plurality of recommended products on a graphical user interface, is merely nominal or tangential additions to the primary process of recommending products to a user.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements of one or more computer processors, computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, a generative AI knowledge graph service, a graphical user interface. recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Further, the step communicating the plurality of recommended products to cause generation of the plurality of recommended products on a graphical user interface, does not provide significantly more than the judicial exception because they are merely well-understood, routine, and conventional activities previously known to the industry of data management and processing. The courts have recognized the computer functions as well-understood, routine, and conventional functions when they are claimed in a generic manner or as insignificantly extra-solution activity. Receiving or transmitting data (i.e., communicating the plurality of recommended products) over a network (e.g., using the Internet to gather data) and storing and retrieving information in memory are recognized computer functions that are considered insignificant extra-solution activity (see MPEP 2106.05(d)(II)). This is similar to the steps and additional elements that are recited in the claims. For example, the step of communicating the recommended products to the graphical user interface, would be the same as the activity of transmitting and receiving data over a network and is the same as the function of transmitting and receiving the data over a network, such as the internet. For examples of court cases, see Versata Dev. Group, Inc. v. SAP Am, Inc., 793 F.3d 1306, 1344 (Fed. Cir. 2015) and Intellectual Ventures I v. Symantec Corp., 838 F. 3d 1307, 1315 (Fed. Cir. 2016).
As such, representative claim 1 is ineligible.
Dependent claims 2-10 do not aid in the eligibility of representative independent claim 1. The claims of 2-10 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of node and a node (claims 2, 4, & 6), a database (claims 2 & 9), edges and edge (claim 4), an edge-node prediction and a generative AI model (claim 5), and one or more application (claim 8). Applicant’s specification does not provide any discussion or description of the claimed additional elements, as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claim 2, 4-6, and 8-9 are directed towards an abstract idea. Additionally, the additional elements of claims 2, 4-6, and 8-9, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 3, 7, and 10 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, the dependent claims 2-10 are ineligible.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Feller, D., et al. (Patent No. US 9,824,152 B2), in view of Skarphedinsson, N., et al. (Patent No. US 12,563,071 B1).
Claim 1-
Feller discloses a computerized system comprising:
one or more computer processors (Feller, Col. 4, ln. 46-47 “at least one processor 202”); and
computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations (Feller, see: Col. 4, ln. 59-60 “memory 206 holds instructions and data used by the processor”) comprising:
providing a product listing system having a seed product (Feller, see: Col. 5, ln. 23 “recommendation module 125”; and Col. 7, ln. 23-26 disclosing “candidate generation module 330 identifies multiple seed recipes from events related to the user” and ln. 30-31 disclosing “if a user endorsed a recipe for ‘lemon shrimp’” and “identified as a seed recipe [i.e., seed product]”; Also see FIG. 3 rendering the system for providing recommendations.);
accessing the seed product (Feller, see: Col. 7, ln. 30-31 disclosing “if a user endorsed a recipe for ‘lemon shrimp’” and “identified as a seed recipe [i.e., seed product]”; and ln. 34-37 disclosing “candidate generation module 330 may use all recipes in the events related to the user”);
identifying a plurality of candidate products associated with the seed product using a product knowledge graph, wherein the product knowledge graph is associated with a graph (Feller, see: Col. 7, ln. 42-43 disclosing “The candidate generation module 330 determines a certain number of candidate recipes [i.e., candidate products] based on the seed recipe”; and Col. 8, ln. 47-48 disclosing “candidate generation module 330 ranks the candidates” and Col. 9, ln. 6-7 disclosing “knowledge graph 345 establishes relationships that are explicitly and not explicitly in the recipes”; Also see FIG. 3 rendering the recommendation system that includes the candidate generation module and the knowledge graph.);
identifying a plurality of recommended products (Feller, see: Col. 8, 54-59 disclosing “candidate generation module 330 may apply logistic regression techniques to rank the candidate recipes” and “may select a certain number of candidate recipes with the highest ranking score as recommended recipes”),
wherein the plurality of recommended products are a subset of the plurality of candidate products (Feller, Col. 8, ln. 57-59 “may select a certain number of candidate recipes with the highest ranking score as recommended recipes”); and
communicating the plurality of recommended products to cause generation of the plurality of recommended products on a graphical user interface (Feller, Col. 8, ln. 57-59 “may select a certain number of candidate recipes with the highest ranking score as recommended recipes for the user” and see: Col. 14, ln. 30-32 disclosing “recommended recipes are transmitted to the presentation module 135 of the client device 130 for display to the user”; Also see FIG. 4).
Although Feller describes that a knowledge graph is used to identify candidate products, Feller does not specifically state that the knowledge graph is associated with a generative AI knowledge graph service. Feller does not disclose:
a generative Al knowledge graph service
Skarphedinsson, however, does teach:
a generative Al knowledge graph service (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Claim 2-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses wherein the plurality of candidate products are identified based on:
accessing a first product identifier associated with the seed product item (Feller, see: Col. 11, ln. 18-23 disclosing “feature alignment module 360 receives a feature set for a recipe and aligns features” and “identifies a recipe that a user has endorsed, as a seed recipe, and retrieves the feature set for the seed recipe”);
using the first product identifier, querying the product knowledge graph (Feller, see: Col. Col. 11, ln. 6-8 disclosing “content model for aligning features between recipes based on the knowledge graph 345” and see: Col. 12, ln. 14-15 disclosing “ranking function includes a set of features aligned from the features of the seed recipe” and ln. 20-21 “searches for recommended recipes using the ranking function”);
identifying a plurality of connected product identifiers in the product knowledge graph associated with the first product identifier, wherein the plurality of connected product identifiers are nodes connected to a node of the first product identifier in the product knowledge graph (Feller, see: Col. 9, ln. 6-7 “knowledge graph 345 establishes relationships…in the recipes” and ln. 20-23 disclosing “knowledge graph 345 may contain attributes of a particular object on the graph” and “nodes representing ingredients contain…information” and ln. 32-33 disclosing “Nodes of the…knowledge graph 345 are connected by edges that indicate relationships”);
for the first product identifier and each of the plurality of connected product identifiers, identifying corresponding candidate products from a product listing database (Feller, see: Col. 6-7, ln. 67 and 1-3 disclosing “relationships between recipes can be formed, stored in a recipe-to-recipe table”; and see: Col. 8, 54-59 disclosing “candidate generation module 330 may apply logistic regression techniques to rank the candidate recipes”; and see: Col. 9, ln. 61-66 disclosing “nodes…and edges between nodes serve to organize food knowledge in to the knowledge graph” and “generate features of recipes” and Col. 10, ln. 48-50 “generate a content model for aligning from a first set of features to a second set of feature”); and
identifying the plurality of candidate products based on the corresponding candidate products from the product listing database (Feller, see: Col. 6-7, ln. 67 and 1-3 disclosing “relationships between recipes can be formed, stored in a recipe-to-recipe table”; and see: Col. 8, 54-59 disclosing “candidate generation module 330 may apply logistic regression techniques to rank the candidate recipes” and “may select a certain number of candidate recipes with the highest ranking score as recommended recipes”).
Claim 3-
Feller in view of Skarphedinsson teach the system of claim 2, as described above.
Feller discloses:
wherein the plurality of candidate products are individual instances of the products associated with corresponding product identifiers, an instance of a product having a plurality of product features of the instance of the product (Feller, see: Col. 7, ln. 19-24 disclosing “ candidate generation module 330 applies the user model to generate candidate recipes” and Col. 9, ln. 20-23 disclosing “knowledge graph 345 may contain attributes of a particular object” and “representing ingredients contain nutritional information and associated allergens and dietary restrictions”).
Claim 4-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses:
wherein the product knowledge graph comprises a plurality of products as nodes and a plurality of relationships as edges, wherein a node in the product knowledge graph comprises a plurality of node attributes and an edge in the product knowledge graph comprises a plurality of edge attributes, wherein the plurality of node attributes comprise a graph context that includes in insights on a first node connected to a second node, the plurality of node attributes including an audience attribute, the audience attribute identifies one or more targeted demographics for a corresponding product associated with the node (Feller, see: Col. 9, ln. 20-21 disclosing “food knowledge graph 345 may contain attributes”; and ln. 22-23 disclosing “representing ingredients contain nutritional density information and associated allergens and dietary restrictions [i.e., audience attribute]”; and ln. 32-44 disclosing “Nodes of the food knowledge graph 345 are connected by edges that indicate relationships between the nodes. The food knowledge graph has different types of edges representing different relationships, and two nodes may be connected by more than one edge. For example, one type of edge explicitly indicates the parent-child relationship between an ingredient and an abstraction (e.g., “black beans are a kind of beans”). Another type of edge between two nodes representing two ingredients indicates that the ingredients have equivalent nutritional content (e.g., “a Fuji apple is the nutritional equivalent of a gala apple”). Edges may connect similar nodes, such as an edge representing substitutability between ingredients represented by two nodes”).
Feller does not disclose:
a generative AI graph;
Skarphedinsson, however, does teach:
a generative AI graph (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Claim 5-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
wherein the generative graph service is an edge-node prediction service associated with a generative model, and wherein identifying the plurality of candidate products associated with the seed product is performed with a ranker of the product listing system (Feller, see: Col. 6, ln. 61-67 disclosing “user model can predict the likelihood that the user will also like a recipe for ‘blueberry muffin,’ or a recipe for ‘lemon shrimp.’ Furthermore, the user model can predict the likelihood that if the user likes the recipe for ‘blueberry muffin,’ the user will also like a recipe for ‘mocha smoothies.’ Accordingly, the user model can predict likelihood from one recipe to another recipe, and therefore relationships between recipes can be formed, and stored in a recipe-to-recipe table. The recipe-to-recipe table may have entries, e.g., (a first recipe, a second recipe, likelihood), and be stored in the database 310, accessible to other components of the recommendation module 125, such as the candidate generation module 330’ and Col. 7, ln. 31-33 disclosing “if a user endorsed a recipe for ‘lemon shrimp’ at 9:46 PM on Aug. 15, 2013 and the recipe is identified as a seed recipe” and ln. 39-41 “candidate generation module 330 may leave some positively acknowledge recipes to train a ranking algorithm”).
Feller does not disclose:
generative AI model;
Skarphedinsson, however, does teach:
generative AI model (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Claim 6-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses wherein the product knowledge graph is a mapped product knowledge graph, the product knowledge graph is mapped to a plurality of product instances in a product listing database based on product identifiers associated with nodes in the product knowledge graph and product identifiers associated with the plurality of product instances in a product listing database (Feller, see: Col. 6, ln. 65-67 disclosing “relationships between recipes can be formed, and stored in a recipe-to-recipe table. The recipe-to-recipe table may have entries, e.g., (a first recipe, a second recipe, likelihood), and be stored in the database 310”; and see: Col. 9, ln. 20-23 disclosing “knowledge graph 345 may contain attributes of a particular object” and “representing ingredients contain nutritional information and associated allergens and dietary restrictions” and ln. 42-44 disclosing “Edges may connect similar nodes, such as an edge representing substitutability between ingredients represented by two nodes”).
Claim 7-
Feller in view of Skarphedinsson teach the system of claim 6, as described above.
Feller discloses: wherein a first product and a second product in the product knowledge graph are mapped to a first product instance in the product listing database, wherein the first product is identical to the first product instance, and wherein the second product is not identical to the first product instance (Feller, see: Col. 9, ln. 33-42 disclosing “The food knowledge graph has different types of edges representing different relationships, and two nodes may be connected by more than one edge. For example, one type of edge explicitly indicates the parent-child relationship between an ingredient and an abstraction (e.g., “black beans are a kind of beans”). Another type of edge between two nodes representing two ingredients indicates that the ingredients have equivalent nutritional content (e.g., “a Fuji apple is the nutritional equivalent of a gala apple”)”).
Claim 8-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses the operations further comprising:
accessing product knowledge graph generation data associated with generating the product knowledge graph for the product listing system, wherein the product knowledge generation data comprises a plurality of seed prompt inputs that support generating the product knowledge graph (Feller, see: Col. 7, ln. 42-43 disclosing “The candidate generation module 330 determines a certain number of candidate recipes [i.e., candidate products] based on the seed recipe”; and Col. 8, ln. 47-48 disclosing “candidate generation module 330 ranks the candidates” and Col. 9, ln. 6-7 disclosing “knowledge graph 345 establishes relationships that are explicitly and not explicitly in the recipes”);
using the generative graph, generating the product knowledge graph comprising a plurality of products as nodes (Feller, see: Col. 9, ln. 6-13 disclosing “The knowledge graph 345 establishes relationships that are explicit and not explicit in the recipes. In one embodiment, the knowledge graph 345 contains a graph representing food knowledge. The nodes of the graph represent ingredients, ingredient abstractions…Example nodes of the graph include “apple,” “gala apple,” “fruit,” “slicing,” “peeling,” “knife,” and “peeler.’”); and
deploying the product knowledge graph to support one or more application in the product listing system (Feller, see: Col. 9, ln. 33-42 disclosing The food knowledge graph has different types of edges representing different relationships, and two nodes may be connected by more than one edge. For example, one type of edge explicitly indicates the parent-child relationship between an ingredient and an abstraction (e.g., “black beans are a kind of beans”). Another type of edge between two nodes representing two ingredients indicates that the ingredients have equivalent nutritional content (e.g., “a Fuji apple is the nutritional equivalent of a gala apple”)”).
Feller does not disclose:
artificial intelligence (AI) knowledge graph service;
Skarphedinsson, however, does teach:
artificial intelligence (AI) knowledge graph service (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Claim 9-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses the operations further comprising:
accessing a product search query (Feller, see: Col. 12, ln. 14-15 disclosing “ranking function includes a set of features aligned from the features of the seed recipe” and ln. 20-21 “searches for recommended recipes using the ranking function”);
using the generative graph, processing the product search query using the product knowledge graph, wherein the product knowledge graph is a mapped product knowledge graph, the product knowledge graph is mapped to a plurality of product instances in a product listing database (Feller, see: Col. 6, ln. 65-67 disclosing “relationships between recipes can be formed, and stored in a recipe-to-recipe table. The recipe-to-recipe table may have entries, e.g., (a first recipe, a second recipe, likelihood), and be stored in the database 310”; and see: Col. 9, ln. 20-23 disclosing “knowledge graph 345 may contain attributes of a particular object” and “representing ingredients contain nutritional information and associated allergens and dietary restrictions” and ln. 42-44 disclosing “Edges may connect similar nodes, such as an edge representing substitutability between ingredients represented by two nodes”)”; and Col. 11, ln. 6-8 disclosing “content model for aligning features between recipes based on the knowledge graph 345” and see: Col. 12, ln. 14-15 disclosing “ranking function includes a set of features aligned from the features of the seed recipe” and ln. 20-21 “searches for recommended recipes using the ranking function”);
based on processing product search query, identifying product search query results from the plurality of product instances (Feller, see: Col. 12, ln. 43-47 disclosing “searching module 370 may exclude or limit trivial alignments such as one-to-one alignments (e.g., from “broccoli” to “broccoli”). In this way, the resulting recommended recipes will be less obvious and more surprising to the user”); and
causing display of the product search query results on a graphical user interface (Feller, see: Col. 13, ln. 22-25 disclosing “recommended recipes determined by the searching module 370 using content-based recommendation, and provides the resulting recommended recipes to the client device 130”).
Feller does not disclose:
generative AI knowledge graph service,
Skarphedinsson, however, does teach:
generative AI knowledge graph service (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Claim 10-
Feller in view of Skarphedinsson teach the system of claim 1, as described above.
Feller discloses the operations further comprising:
communicating a product search query (Feller, see: Col. 12, ln. 14-15 disclosing “ranking function includes a set of features aligned from the features of the seed recipe” and ln. 20-21 “searches for recommended recipes using the ranking function”);
based on communicating the product search query, receiving product search query results, wherein the product search query results are generated using the generative graph that processed the product search query using the product knowledge graph, wherein the product knowledge graph is a mapped product knowledge graph, the product knowledge graph is mapped to a plurality of product instances in a product listing database (Feller, see: Col. 6, ln. 65-67 disclosing “relationships between recipes can be formed, and stored in a recipe-to-recipe table. The recipe-to-recipe table may have entries, e.g., (a first recipe, a second recipe, likelihood), and be stored in the database 310”; and see: Col. 9, ln. 20-23 disclosing “knowledge graph 345 may contain attributes of a particular object” and “representing ingredients contain nutritional information and associated allergens and dietary restrictions” and ln. 42-44 disclosing “Edges may connect similar nodes, such as an edge representing substitutability between ingredients represented by two nodes”)”; and Col. 11, ln. 6-8 disclosing “content model for aligning features between recipes based on the knowledge graph 345” and see: Col. 12, ln. 14-15 disclosing “ranking function includes a set of features aligned from the features of the seed recipe” and ln. 20-21 “searches for recommended recipes using the ranking function”); and
causing display of the product search query results on a graphical user interface (Feller, see: Col. 13, ln. 22-25 disclosing “recommended recipes determined by the searching module 370 using content-based recommendation, and provides the resulting recommended recipes to the client device 130”).
Feller does not disclose:
generative AI knowledge graph service,
Skarphedinsson, however, does teach:
generative AI knowledge graph service (Skarphedinsson, see: Col. 103, ln. 46-51 teaching “providing 1702, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment. As referred to herein, generative AI uses models such as neural networks, large language models (LLMs), and the like to generate content”).
This step of Skarphedinsson is applicable to the system of Feller, as they both share characteristics and capabilities, namely, they are directed to providing recommendation content to a user. 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 system of Feller to include the feature of a generative AI knowledge graph service, as taught by Skarphedinsson. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Feller to improve the efficiency of a user request for content (Skarphedinsson, Col. 100, at least lines 13-24).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Martineau, J., et al. (PGP No. US 2019/0392330 A1), describes a recommendation method includes determining one or more aspects of a first item based on at least one descriptive text of the first item. The recommendation method also includes updating a knowledge graph containing nodes that represent multiple items, multiple users, and multiple aspects. Updating the knowledge graph includes linking one or more nodes representing the one or more aspects of the first item to a node representing the first item with one or more first edges.
Non-patent literature (NPL) document, titled Using graph neural networks to recommend related products, published on amazon science (2022), describes techniques using GNNs to recommend products based on relationships between products, and representing these relationships via a graph.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688