DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 18 December 2024.
Claims 1-15 are pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on 17 June 2025, has been considered. An initialed copy of the Form 1449 is enclosed herewith.
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 1-15 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 1-6 are directed to a system, and claims 7-15 are directed to a method.
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 bundled product recommendations. Specifically, representative claim 7 recites the abstract idea of:
receiving a selection of a first product at a retailer;
identifying the first product in a product graph generated from historical add to cart data, the product graph including a plurality of nodes and a plurality of edges, each node corresponding to a different product, and each edge being a directed weighted edge connecting between a pair of nodes and a corresponding to an order frequency in which two products corresponding to the pair of nodes were added to a shopping cart in the historical add to cart data;
identifying, in the product graph:
one or more edges from the first product to one or more secondary products;
based on similar product to the first product existing within the product graph, one or more edges from the similar product to one or more secondary-similar products; and
one or more inferred edges from the first product to one or more inferred secondary products;
forming a set of recommended products from at least some of the one or more secondary products, the one or more secondary-similar products, and the one or more inferred secondary products; and
presenting at least one bundled product recommendation to a customer as a recommendation for purchase with the initial product, the at least one bundled product recommendation being selected from among the set of recommended products.
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 7 recites the abstract idea of bundled product recommendations, 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 7 is a certain method of organizing human activity because it relates to sale activities since the claims specifically steps for providing the bundled product recommendation to a user that involves receiving the first selection of a product from a retailer, identifying a first product in a product graph generated from historical add to cart data, where the product graph includes nodes and edges corresponding to the first product and a different product and the frequency of which two products are added to the cart from the historical add to cart data, further identifying from the product graph edges from the first product to secondary products, determining similarities of products, forming the recommendations of products based on secondary products and similar products, and presenting the bundled product recommendation to the customer for purchase of the initial product and selecting the bundled product recommendation from a set of the recommended products, thereby making this a sales activity or behavior.
The Examiner additionally notes that that the step identifying the first product in a product graph generated from historical add to cart data, would fall into the enumerated grouping of a mental process. 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 step of identifying the first product in a product graph, where the graph is generated for the add to cart history data, would be considered a concept performed in the human mind, such as an observation and judgement. Thus, representative claim 7 recites an abstract idea that also falls into the grouping of mental processes.
Thus, representative claim 7 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 7 includes additional elements: a website, and a user interface on the customer device.
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 7 merely recites a commonplace business method (i.e., bundled product recommendations) 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 presenting at least one bundled product recommendation on a user interface on the customer device, is considered to be 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 to claim (see MPEP 2106.05(g)). In this case, the activity of presenting to a user interface on the customer device is merely nominal or tangential additions to the primary process of [abstract idea].
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 recited in independent claim 7 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 7 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 7 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 7 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 presenting at least one bundled product recommendation on a user interface on the customer device, does not provide significantly more than the judicial exception because it is 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 over a network (e.g., using the Internet to gather data), 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 presenting would be the same as the activity of transmitting data over a network in this case. 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 7 is ineligible.
Independent claim 1 is similar in nature to representative claim 7 and Step 2A, Prong 1 analysis is the same as above for representative claim 7. It is noted that in independent claim 1 includes the additional element of a computing system including a data store, a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor. The Applicant’s specification does not provide any discussion or description of additional elements in claim 1, as being anything other than generic elements. Thus, the claimed additional elements of claim 1 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. As such, the additional elements of claim 1 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claim 1, 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.
As such, claim 1 is ineligible.
Dependent claims 2-6 and 8-15, depending from claims 1 and 7 respectively, do not aid in the eligibility of the independent representative claim 7 and independent claim 1. The claims of 2-6 and 8-15 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims 5 and 9 recite the additional element of a graph neural network (GNN). Applicant’s specification does not provide any discussion or description of the graph neural network as being anything other than a generic element. The claimed additional element, individually and in combination with other features in the claims, does not integrate into a practical application and does not provide an inventive concept because it is merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claims 5 and 9 are directed towards an abstract idea. Additionally, the additional elements of claims 5 and 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 2-4, 6, 8, and 10-15 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 2-6 and 8-15 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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Frazer, D., et al. (PGP No. US 2014/0222506 A1), in view of Silverstein, et al. (PGP No. US 2022/0292566 A1).
Claim 1-
Frazer discloses a system for providing a product recommendation on a retailer website, the system (Frazer, see: paragraph [0048] disclosing “method 100” and “a recommendation”) comprising:
a computing system including a data store, a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to (Frazer, see: paragraph [0504] disclosing “computer system 6000 that executes programming”):
receive data, product price information, and product data for products offered at a retail website (Frazer, see: paragraph [0051] disclosing “data warehouse 410 that includes…purchase data 414. The purchase data includes data related to the actual purchase of goods…over the internet” and “also include content attribute data”’ and paragraph [0058] disclosing “previous transaction information” and “pricing information”; and pargraph [0097] disclosing “retailer has thousands to hundreds of thousands of products for sale”);
form a product graph from the add to cart data, the product graph including a plurality of nodes and a plurality of edges, each node corresponding to a different product, and each edge being a directed weighted edge connecting between a pair of nodes and corresponding to an order and frequency in which two products corresponding to the pair of nodes were added (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer” and “nodes represent products and edges represent consistency relationships between pairs of nodes” and “a weighted edge between each pair of nodes. The weight represents the consistency with which the products in those categories are purchased together” and “such that edges with high weights are shorter or, in other words, two nodes that have higher consistency strength between them are closer to each other than two nodes that have lower consistency strength between them”);
identify similar products within the product graph based, at least in part, on the products represented in the product graph (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity” and “logical associations between products”; and paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer”; and see: paragraph [0123] disclosing Insight Discovery and Decisioning from the Insight/relationship determination module Graphs--The insight/relationship determination module 320 graphs serve as the model or internal representation of the knowledge extracted”; and paragraph [0487] disclosing “independent training dataset 2814, 2815, 2816 for each target product which will be modeled”);
determine inferred edges between pairs of nodes in the product graph based on a model trained using at least some of the plurality of edges as positive examples and node pairs lacking an edge therebetween as negative examples (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer” and “edges with high weights are shorter or, in other words, two nodes that have higher consistency strength [i.e., positive examples] between them are closer to each other than two nodes that have lower consistency strength [i.e., negative examples] between them”; and paragraph [0123] disclosing “The insight/relationship determination module 320 graphs serve as the model…representation of the knowledge extracted”; paragraph [0488] disclosing “training dataset” and “put through a series of binning…model training, scoring and analyzing steps”);
identify, in the product graph (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph”):
one or more edges from the initial product to one or more secondary products (Frazer, see: paragraph [0049] disclosing “The first entity can be a first product and the second entity can be a second product”; and paragraph [0108] disclosing “a set of Edges representing strength of relationships between pairs of nodes (entities)”);
based on a similar product to the initial product existing within the product graph, one or more edges from the similar product to one or more secondary-similar products (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity” and “logical associations between products”; and paragraph [0111] disclosing “nodes represent products and edges represent consistency relationships between pairs of nodes”); and
one or more inferred edges from the initial product to one or more inferred secondary products (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity”; and paragraph [0111] disclosing “nodes represent products and edges represent consistency relationships between pairs of nodes”; and paragraph [0123] disclosing “the insight/relationship determination module graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products a customer may buy”);
form a set of recommended products from at least some of the one or more secondary products, the one or more secondary-similar products, and the one or more inferred secondary products (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”, and see FIG. 22 rendering the recommendation example, of recommending products that are ranked from lower to highest that are connected to a lot of other products that will likely be cross-sold to a customer, determining what products to recommend.).
Although Frazer does disclose a product graph that displays data corresponding to purchased products and different products that are similar in nature, Frazer does not disclose that the graph includes add to cart data of an initially selected product, nor does Frazer describe that the similarities of the different products were related to specific attributes of the products. Frazer does not disclose:
add to cart data;
products added to a shopping cart;
attributes of the products;
receive a selection of an initial product;
Silverstein, however, does teach:
add to cart data (Silverstein, see: paragraph [0020] teaching “user places one or more products in the shopping cart” and paragraph [0021] teaching “retrieves a search history….for the primary product”);
products added to a shopping cart (Silverstein, see: paragraph [0020] teaching “user places one or more products in the shopping cart” and paragraph [0021] teaching “retrieves a search history….for the primary product”);
attributes of the products (Silverstein, see: paragraph [0025] teaching “if the received query was for a ‘stapler kit,’ then bundle component analysis module 106 determines a stapler kit includes a stapler and staples. Then bundle component analysis module 106 performs a further search on “staplers and staples” that reveals one or more bundles under a category of ‘office essentials kit’ [i.e., attributes] that include a stapler and staples”);
receive a selection of an initial product (Silverstein, see: paragraph [0020] teaching “user places one or more products in the shopping cart” and paragraph [0021] teaching “search for the primary product” and “retrieves a search history….for the primary product”);
This step of Silverstein is applicable to the system of Frazer, as they both share characteristics and capabilities, namely, they are directed to determining relationships between products. 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 Frazer, to include the features of add to cart data; products added to a shopping cart; attributes of the products; and receive a selection of an initial product as taught by Silverstein. 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 Frazer to improve identification of products that can be bundled in a transaction (Silverstein, see: paragraph [0005]).
Claim 2-
Frazer in view of Silverstein teach the system of claim 1, as described above.
Frazer discloses:
wherein the set of recommended products forms bundled products, and wherein the system is further configured to present the at least one bundled product on a user interface on the customer device as a recommendation for purchase with the initial product (Frazer, see: paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; and paragraph [0419] disclosing “Once the recommendations are created for each customer, the retailer has a choice to deliver those recommendations using various channels. For example…through their web-site”).
Frazer does not disclose:
a list of bundled products;
Silverstein does teach:
a list of bundled products (Silverstein, see: paragraph [0029] teaching “Bundle component analysis module 106 generates an optimized bundle list (step 218). In an embodiment, based on the received user input, bundle component analysis module 106 generates a list of bundles optimized to the requirements of the user”).
This step of Silverstein is applicable to the system of Frazer, as they both share characteristics and capabilities, namely, they are directed to determining relationships between products. 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 Frazer, to include the features of a list of bundled products, as taught by Silverstein. 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 Frazer to improve identification of products that can be bundled in a transaction (Silverstein, see: paragraph [0005]).
Claim 3-
Frazer in view of Silverstein teach the system of claim 1, as described above.
Frazer discloses:
wherein the set of recommended products is a subset of a collection formed by the one or more secondary products, the one or more secondary-similar products, and the one or more inferred secondary products, the subset formed based on application of a price filter (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; paragraph [0415] disclosing “Recommendation engine is to offer the right product…at the right price through the right channel”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”).
Claim 4-
Frazer in view of Silverstein teach the system of claim 1, as described above.
Frazer discloses:
wherein the set of recommended products is a subset of a collection formed by the one or more secondary products, the one or more secondary-similar products, and the one or more inferred secondary products, the subset formed based on application of a similarity filter to exclude from the set of recommended products those products having a degree of similarity to the initial product that is above a threshold level of similarity (Frazer, see: paragraph [0111] disclosing “weights below a certain threshold are ignored”; and see: paragraph [0286] disclosing “defined as the variability in the product density along different categories” and paragraph [0287] “products are in the same category as the product itself”).
Claim 5-
Frazer in view of Silverstein teach the system of claim 1, as described above.
Frazer discloses wherein the model comprises a graph neural network (GNN) (Frazer, see: paragraph [0075] disclosing “a graphical network structure that reveals the product associations and provides insight”).
Claim 6-
Frazer in view of Silverstein teach the system of claim 1, as described above.
Frazer discloses wherein the products offered at a retail website include a plurality of products within a product category, and wherein the product graph is formed from items within the product category (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer” and “nodes represent products and edges represent consistency relationships between pairs of nodes. This graph has one node for each product at a category level of the product hierarchy”).
Claim 7-
Frazer discloses a method comprising:
receiving a selection of a first product at a retail website (Frazer, see: paragraph [0051] disclosing “data warehouse 410 that includes…purchase data 414. The purchase data includes data related to the actual purchase of goods…over the internet” and “also include content attribute data”’ and paragraph [0058] disclosing “previous transaction information” and “pricing information”; and pargraph [0097] disclosing “retailer has thousands to hundreds of thousands of products for sale”);
identifying the first product in a product graph generated from historical data, the product graph including a plurality of nodes and a plurality of edges, each node corresponding to a different product, and each edge being a directed weighted edge connecting between a pair of nodes and corresponding to an order and frequency in which two products corresponding to the pair of nodes were added to a shopping cart in the historical data (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer” and “nodes represent products and edges represent consistency relationships between pairs of nodes” and “a weighted edge between each pair of nodes. The weight represents the consistency with which the products in those categories are purchased together” and “such that edges with high weights are shorter or, in other words, two nodes that have higher consistency strength between them are closer to each other than two nodes that have lower consistency strength between them”);
identifying, in the product graph (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph”):
one or more edges from the first product to one or more secondary products (Frazer, see: paragraph [0049] disclosing “The first entity can be a first product and the second entity can be a second product”; and paragraph [0108] disclosing “a set of Edges representing strength of relationships between pairs of nodes (entities)”);
based on a similar product to the first product existing within the product graph, one or more edges from the similar product to one or more secondary-similar products (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity” and “logical associations between products”; and paragraph [0111] disclosing “nodes represent products and edges represent consistency relationships between pairs of nodes”); and
one or more inferred edges from the first product to one or more inferred secondary products (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity”; and paragraph [0111] disclosing “nodes represent products and edges represent consistency relationships between pairs of nodes”; and paragraph [0123] disclosing “the insight/relationship determination module graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products a customer may buy”);
forming a set of recommended products from at least some of the one or more secondary products, the one or more secondary-similar products, and the one or more inferred secondary products (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”, and see FIG. 22 rendering the recommendation example, of recommending products that are ranked from lower to highest that are connected to a lot of other products that will likely be cross-sold to a customer, determining what products to recommend.); and
at least one bundled product recommendation as a recommendation for purchase with the initial product, the at least one bundled product recommendation being selected from among the set of recommended products (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”, and see FIG. 22).
Although Frazer does disclose a product graph that displays data corresponding to purchased products and different products that are similar in nature, Frazer does not disclose that the graph includes add to cart data of an initially selected product, nor does Frazer describe that the similarities of the different products were related to specific attributes of the products, where the bundled products are also presented to the user. Frazer does not disclose:
add to cart data;
products added to a shopping cart;
presenting at least one bundled product recommendation on a user interface on the customer device
Silverstein, however, does teach:
add to cart data (Silverstein, see: paragraph [0020] teaching “user places one or more products in the shopping cart” and paragraph [0021] teaching “retrieves a search history….for the primary product”);
products added to a shopping cart (Silverstein, see: paragraph [0020] teaching “user places one or more products in the shopping cart” and paragraph [0021] teaching “retrieves a search history….for the primary product”);
presenting at least one bundled product recommendation on a user interface on the customer device (Silverstein, see: paragraph [0025] teaching “if the received query was for a ‘stapler kit,’ then bundle component analysis module 106 determines a stapler kit includes a stapler and staples. Then bundle component analysis module 106 performs a further search on “staplers and staples” that reveals one or more bundles under a category of ‘office essentials kit’ [i.e., attributes] that include a stapler and staples”).
This step of Silverstein is applicable to the method of Frazer, as they both share characteristics and capabilities, namely, they are directed to determining relationships between products. 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 method of Frazer, to include the features of add to cart data, products added to a shopping cart, and presenting at least one bundled product recommendation on a user interface on the customer device, as taught by Silverstein. 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 Frazer to improve identification of products that can be bundled in a transaction (Silverstein, see: paragraph [0005]).
Claim 8-
Frazer in view of Silverstein teach the method of claim 7, as described above.
Frazer discloses further comprising:
identifying the similar products within the product graph based, at least in part, on the products represented in the product graph (Frazer, see: paragraph [0074] disclosing “The insight/relationship determination module 320 employs information-theoretic notions of consistency and similarity” and “logical associations between products”; and paragraph [0111] disclosing “nodes represent products and edges represent consistency relationships between pairs of nodes”); and
determining the inferred edges between pairs of nodes in the product graph based on a model trained using at least some of the plurality of edges as positive examples and node pairs lacking an edge therebetween as negative examples (Frazer, see: paragraph [0111] disclosing “insight/relationship determination module Consistency Graph created using the transaction data from a Grocery retailer” and “edges with high weights are shorter or, in other words, two nodes that have higher consistency strength [i.e., positive examples] between them are closer to each other than two nodes that have lower consistency strength [i.e., negative examples] between them”; and paragraph [0123] disclosing “The insight/relationship determination module 320 graphs serve as the model…representation of the knowledge extracted”; paragraph [0488] disclosing “training dataset” and “put through a series of binning…model training, scoring and analyzing steps”).
Frazer does not disclose:
attributes of the products
Silverstein does teach:
attributes of the products Silverstein, see: paragraph [0025] teaching “if the received query was for a ‘stapler kit,’ then bundle component analysis module 106 determines a stapler kit includes a stapler and staples. Then bundle component analysis module 106 performs a further search on “staplers and staples” that reveals one or more bundles under a category of ‘office essentials kit’ [i.e., attributes] that include a stapler and staples”).
This step of Silverstein is applicable to the system of Frazer, as they both share characteristics and capabilities, namely, they are directed to determining relationships between products. 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 Frazer, to include the features of attributes of the products, as taught by Silverstein. 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 Frazer to improve identification of products that can be bundled in a transaction (Silverstein, see: paragraph [0005]).
Claim 9-
Frazer in view of Silverstein teach the method of claim 8, as described above.
Frazer discloses wherein the model comprises a graph neural network (GNN) (Frazer, see: paragraph [0075] disclosing “a graphical network structure that reveals the product associations and provides insight”).
Claim 10-
Frazer in view of Silverstein teach the method of claim 7, as described above.
Frazer discloses further comprising applying one or more filters to the set of recommended products to generate the at least one bundled product recommendation, the one or more filters being based, at least in part, on a price or a product diversity metric (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; paragraph [0415] disclosing “Recommendation engine is to offer the right product…at the right price through the right channel”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”).
Claim 11-
Frazer in view of Silverstein teach the method of claim 10, as described above.
Frazer discloses wherein the product diversity metric determines a level of similarity between the first product and a selected one of the set of recommended products, the one or more filters excluding products from among the set of recommended products based on the product diversity metric falling within a threshold indicative of high product similarity (Frazer, see: paragraph [0111] disclosing “weights below a certain threshold are ignored”; and see: paragraph [0286] disclosing “defined as the variability in the product density along different categories” and paragraph [0287] “products are in the same category as the product itself”).
Claim 12-
Frazer in view of Silverstein teach the method of claim 7, as described above.
Frazer discloses wherein the products represented by nodes within the product graph are within a common product category. (Frazer, see: paragraph [0111] disclosing “weight represents the consistency with which the products in those categories are purchased together”).
Claim 13-
Frazer in view of Silverstein teach the method of claim 7, as described above.
Frazer discloses wherein the plurality of edges each have a weight that is normalized (Frazer, see: paragraph [0111] disclosing “There is a weighted edge between each pair of nodes”; and see: paragraph [0283] disclosing “the weights are normalized”).
Claim 14-
Frazer in view of Silverstein teach the method of claim 13, as described above.
Frazer discloses further comprising removing an edge from the product graph based on a weight of the edge falling below a predetermined threshold (Frazer, see: paragraph [0111] disclosing “weights below a certain threshold are ignored”.
Claim 15-
Frazer in view of Silverstein teach the method of claim 7, as described above.
Frazer discloses further comprising, identifying in the product graph one or more inferred edges between the similar product and one or more inferred secondary-similar products and including the one or more inferred secondary-similar products in the set of recommended products (Frazer, see: paragraph [0123] disclosing “graph is used as a model for decisions, such as a recommendation engine that predicts the most likely products”; and paragraph [0291] disclosing “the bundle shown in FIG. 10, the Notebook is the principal product and the mouse is the peripheral product of the bundle”; and paragraph [0473] disclosing “products 1, 2, 3, etc. represents the top products recommended by a recommendation engine”, and see FIG. 22 rendering the recommendation example, of recommending products that are ranked from lower to highest that are connected to a lot of other products that will likely be cross-sold to a customer, determining what products to recommend.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kumar, S., et al. (Patent No. US 8,015,140 B2), describes technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior.
Non-patent literature document, Real-time Retrieval for Recommendations, published on applyingml.com (2021), describes real-time recommendations for customers using user behavior data and item metadata.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY PRESTON whose telephone number is (571)272-4399. The examiner can normally be reached M-F 9-5.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688
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