DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/29/2026 has been entered.
Status of Claims
• The following is an office action in response to the communication filed 04/29/2026.
• Claims 1-9, 11, 16, and 21 have been amended.
• Claims 10 and 20 have been canceled.
• Claims 1-9, 11-19, and 21-22 are currently 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 .
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, 11-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-9 are directed to a machine, claims 11-19 are directed to a process, and claims 21-22 are directed to a manufacture. Therefore, claims 1-9, 11-19, and 21-22 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
storing a classification hierarchy that classifies information, identified using elements, into a plurality of groupings for which [are] provide[d] [selections] corresponding to the plurality of browse, the classification hierarchy comprising a plurality of hierarchy levels and each of the plurality of groupings is associated with at least one of the plurality hierarchy levels;
monitoring engagement metrics relating to how [users] interact with the elements;
generating, without generating importance signals for each of the plurality of groupings, importance signals, for the plurality of hierarchy levels, based on relationships between the plurality of hierarchy levels and two or more of the plurality of groupings and based on weighted data associated with the engagement metrics, wherein a quantity of the plurality of hierarchy levels is less than a quantity of the plurality of groupings;
receiving a request to view a grouping of the plurality of groupings;
retrieving, based on receiving the request, and after generating the importance signals for the plurality of hierarchy levels without generating importance signals for each of the plurality of groupings, particular importance signals, of the importance signals for the plurality of hierarchy levels, for a hierarchy level, of the plurality of hierarchy levels, associated with the grouping;
executing a ranking to generate a ranked listing for the grouping by, to generate ranking values for the grouping, combining the particular importance signals for the hierarchy level with one or more popularity signals, derived from the engagement metrics, by applying weights, to the particular importance signals for the hierarchy level and the one or more popularity signals, that are selected using a model to optimize being used for tow or more different types of engagement; and
causing display[] the ranked listing for the, to be generated.
Additionally, taking claim 11 as representative, claim 11 recites at least the following limitations that are believed to recite an abstract idea:
storing a category classification hierarchy that classifies items, offered, into a plurality of browse categories, the category classification hierarchy comprising a plurality of hierarchy levels and each of the plurality of browse categories is associated with at least one of the plurality hierarchy levels;
monitoring user engagement metrics relating to interacting with the items;
generating, based on the user engagement metrics and based on relationships between one or more of the plurality of hierarchy levels, one or more of the items, and one or more of the plurality of browse categories, shelf importance signals for the plurality of hierarchy levels, wherein a quantity of the plurality of hierarchy levels is less than a quantity of the plurality of browse categories;
receiving a request to view a browse category of the plurality of browse categories;
retrieving, based on receiving the request, and after generating the shelf importance signals for the plurality of hierarchy levels, particular shelf importance signals, of the shelf importance signals, for a hierarchy level, of the plurality of hierarchy levels, associated with the browse category;
generating a ranked item listing for the browse category based, on retrieving, the particular shelf importance signals for the hierarchy level and based on one or more weights that are selected using a model to optimize one or more types of engagement; and
sending the ranked item listing for the browse category.
The above limitations recite the concept of ranking items according to groupings to recommend item listings. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the item recommendations are for the purpose of purchasing (Spec: [0002]). Furthermore, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, such as observations, evaluations, judgments, and opinions. Specifically, determining the claims recite concepts similar to collecting information, analyzing it, and displaying certain results of the collection and analysis. Claim 21 recites similar concepts as claim 11 and accordingly falls within the same groupings of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 11, and 21 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 11, and 21 recite additional elements, such as a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute functions; an electronic platform; graphical user interfaces (GUIs); interface elements; graphical user interface (GUI) groupings; network traffic-based importance signals; network traffic-weighted data; a machine learning model that is trained to optimize; hyperlinks; computing devices; a computing device of the one or more computing devices; retrieving during runtime; a ranking engine; transmitting data; execution of computing instructions by one or more processors and stored on one or more non-transitory computer-readable storage devices; and a non-transitory, computer-readable medium comprising instructions that, when executed by a processing resource, cause the processing resource. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 11, and 21 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 11, and 21 merely recite a commonplace business method (i.e., ranking items according to groupings to recommend item listings) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 11, and 21 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 11, and 21 specifying that the abstract idea of ranking items according to category to recommend item listings is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 11, and 21 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 11, and 21 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 11, and 21 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 11, and 21, these claims recite additional elements, such as a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute functions; an electronic platform; graphical user interfaces (GUIs); interface elements; graphical user interface (GUI) groupings; network traffic-based importance signals; network traffic-weighted data; a machine learning model that is trained to optimize; hyperlinks; computing devices; a computing device of the one or more computing devices; retrieving during runtime; a ranking engine; transmitting data; execution of computing instructions by one or more processors and stored on one or more non-transitory computer-readable storage devices; and a non-transitory, computer-readable medium comprising instructions that, when executed by a processing resource, cause the processing resource. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 11, and 21 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 11, and 21 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 11, and 21 specifying that the abstract idea of ranking items according to category to recommend item listings is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 11, and 21 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 11, and 21 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-9, 12-19, and 22, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-9, 12-19, and 22 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Furthermore, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, such as observations, evaluations, judgments, and opinions. Dependent claims 2-5, 7-8, 12-15, 17-18, and 22 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 6, 9, 16, and 19 further identify additional elements, such as a linear learning model that is trained, and computing offline. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-9, 12-19, and 22 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 11, and 21, dependent claims 2-9, 12-19, and 22 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-9, 11-19, and 21-22 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-8 and 11-18 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Bar (US 8001141 B1), hereinafter Bar, in view of previously cited Kamotsky (US 20200233873 A1), hereinafter Kamotsky.
In regards to claim 1, Bar discloses a system comprising (Bar: [abstract]):
one or more processors (Bar: Col. 6, Ln. 10-16 and Fig. 3; Col. 4, Ln. 30-35 and Fig. 1); and
one or more computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute functions comprising (Bar: Col. 2, Ln. 5-10; Col. 6, Ln. 17-24):
storing a classification hierarchy that classifies information, identified using interface elements via an electronic platform, into a plurality of graphical user interface (GUI) groupings for which graphical user interfaces (GUIs) generated by the electronic platform provide hyperlinks corresponding to the plurality of GUI groupings, the classification hierarchy comprising a plurality of hierarchy levels and each of the plurality of GUI groupings is associated with at least one of the plurality of hierarchy levels (Bar: Col. 4, Ln. 31-36 and Fig. 1 – “a catalog server 104 for retrieving catalog data 102 identifying items organized within one or more browse trees…browse tree is stored”; Col. 3, Ln. 10-42 – “A catalog of items (including each item's description) may be organized into a browse tree structure in order to facilitate searching. As the name suggests, a ‘browse’ tree permits users to ‘browse’ through various items, which are logically arranged in the form of a hierarchical tree. Items are located within the browse tree based on their category. Each node of the browse tree (a.k.a., a ‘browse node’) may be associated with a category of items in a hierarchical manner. Moreover, the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree.…the ‘root’ node of a tree representing ‘ items’ has many browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc. Each such browse node may have different child browse nodes representing sub-categories. For example, a parent browse node such as ‘Kitchen & Housewares’ may have a number of child browse nodes such as ‘Dining,’ ‘Cookware,’ etc.”; Col. 3, Ln. 63 – “electronic retailers”);
monitoring, via the electronic platform, engagement metrics relating to how one or more computing devices are used to interact with the interface elements (Bar: Col. 4, Ln. 52 – Col. 5, Ln. 5 – “relevance server 200 also receives relevance data 310…the relevance data 310 includes…a collection of browse data 310A…Included in the browse data 310A is clickstream data…browse data also includes information regarding the nature of the selections being made while in a particular browse node. This includes, but is not limited to, the number of times the item has been selected or clicked upon, the number of times an item has been selected for purchase or placed in a ‘shopping cart,’ and the number of times the item has actually been purchased”; see also Col. 3, Ln. 10-42);
generating, without generating importance signals for each of the plurality of GUI groupings, network traffic-based importance signals, for the plurality of hierarchy levels, based on relationships between the plurality of hierarchy levels and two or more of the plurality of GUI groupings and based on network traffic-weighted data associated with the engagement metrics, wherein a quantity of the plurality of hierarchy levels is less than a quantity of the plurality of GUI groupings (Bar: Col. 5, Ln. 19-24 – “The relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node. The ranked list of relevance scores 330 is then used to determine how to configure the display of items associated with a browse node”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”; Col. 10, Ln. 29-34 and Fig. 5– “a determination of each category an item can be categorized within must be made to determine browse nodes the items should appear within. An item will receive a score for each category into which it falls”; Col. 10, Ln. 60 – Col. 11, Ln. 3 – “a determination is made as to whether the final category match for the item has been made…if it is determined that there are no more categories, subroutine 500 proceeds to block 510 where all possible category fit scores are sorted”; Col. 2, Ln. 14-20 – “a browse relevance score component configured to determine browse relevance scores for each item associated with the category is also provided. The browse relevance score component determines the browse relevance scores based on…browse score”; Col. 4, Ln. 10-14 – “each item associated with a node receives a relevance score…items to become more or less relevant”; Col. 12, Ln. 17-33 – “a Web Page 800 corresponding to the display of items within the browse node ‘Kitchen & Housewares’ 802 will be described. The Web Page 800 is an example of Web page whose display configuration has been arranged according to embodiments of the present invention as described with respect to FIG. 4. The illustrative Web page 800 can include a browse menu 804 displaying a number of different item categories. For example, the browse menu can include categories corresponding to ‘Dining’ 806, ‘Cookware’ 808, ‘Cutlery’ 810, ‘Housewares’ 812, ‘Cook's Tools & Gadgets’ 814, and ‘Small Appliances’ 816. Each item category may further include subcategories that allow a user to narrow his or her search to more specific categories. For example, ‘Dining’ 806 includes sub-nodes representing subcategories for ‘Drinkware,’ ‘Dinnerware,’ and ‘Utensils’”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”; the examiner interprets the browse score to be the network traffic importance signal; the examiner notes that consistent with [0073] and Fig. 5A, computing signals for each hierarchy level of each item (as opposed to every browse node) is interpreted to be generating without generating importance signals for each GUI grouping);
receiving, via the electronic platform and from a computing device of the one or more computing devices, a request to view a GUI grouping of the plurality of GUI groupings (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”);
retrieving, during runtime, based on receiving the request, and after generating the network traffic-based importance signals for the plurality of hierarchy levels without generating importance signals for each of the plurality of GUI groupings, particular network traffic-based importance signals, of the network traffic-based importance signals for the plurality of hierarchy levels, for a hierarchy level, of the plurality of hierarchy levels, associated with the GUI grouping (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”; Col. 10, Ln. 29-34 and Fig. 5– “a determination of each category an item can be categorized within must be made to determine browse nodes the items should appear within. An item will receive a score for each category into which it falls”; Col. 10, Ln. 60 – Col. 11, Ln. 3 – “a determination is made as to whether the final category match for the item has been made…if it is determined that there are no more categories, subroutine 500 proceeds to block 510 where all possible category fit scores are sorted”);
executing a ranking engine to generate a ranked listing for the GUI grouping by, to generate ranking values for the GUI grouping, combining the particular network traffic-based importance signals for the hierarchy level with one or more popularity signals, derived from the engagement metrics, by applying weights, to the particular network traffic-based importance signals for the hierarchy level and the one or more popularity signals (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”); and
causing, via the electronic platform, the GUI, that displays the ranked listing for the, to be generated for the computing device (Bar: Col. 6, Ln. 35-40 – “ranked list of relevance scores 330 is then transmitted to the retail server 106 which utilizes the relevance scores 330 to configure the display of a Web page representation of a browse node”; Col. 5, Ln. 52-60 – “If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”).
Yet Bar does not explicitly disclose non-transitory computer-readable storage devices and weights that are selected using a machine learning model that is trained to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing.
However, Kamotsky teaches a product recommender (Kamotsky: [abstract]), including
non-transitory computer-readable storage devices (Kamotsky: [0284] – “one or more computer program products, such as one or more computer programs tangibly embodied in a non-transitory information carrier (e.g., in a machine readable storage device)”); and
weights that are selected using a machine learning model that is trained to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing (Kamotsky: [0007] – “the step (g) of calculating recommendation scores comprises approximating, by the computer system using a machine learning model, a quadratic function S[p, 1]=(ϕ[p, r].Math.W.sub.p[r, 1])×(ϕ[p, r].Math.W.sub.a[r,1]) to learn the product recommender weights W.sub.p and the attribute- and attribute-value recommender weights W.sub.a based at least in part on key-performance-indicator driving feedback signals”; [0139] – “Machine learning methods may be used to train one or more models to approximate the above-described signal weights W.sub.a and W.sub.p.”; the examiner notes “to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing” is merely intended use/result and accordingly is granted little to no patentable weight. Nonetheless, the limitation has been fully examined).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the non-transitory devices and machine learning of Kamotsky in the system of Bar because Bar already discloses computer mediums and analysis and Kamotsky is merely demonstrating that the mediums may be non-transitory and a model that may be used. Additionally, it would have been obvious to have included non-transitory computer-readable storage devices and weights that are selected using a machine learning model that is trained to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing as taught by Kamotsky because non-transitory devices and machine learning models are well-known and the use of it in a recommendation setting would have allowed for implementation of the method (Kamotsky: [0284]).
In regards to claim 2, the combination Bar/Kamotsky teaches the system of claim 1. Bar further discloses wherein executing the ranking engine to generate the ranked listing for the GUI grouping includes: generating, based on the engagement metrics, the one or more popularity signals for particular interface elements, of interface elements, included in the GUI grouping (Bar: Col. 4, Ln. 55-59 – “the relevance data 310 includes…popularity data 310B”; Col. 5, Ln. 9-10 – “Popularity data 310B is related to the popularity of an item based on the items sales rank, user comments, etc.”; Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; see also Col. 3, Ln. 19-34).
In regards to claim 3, the combination Bar/Kamotsky teaches the system of claim 2. Bar further discloses wherein the one or more popularity signals include trending metrics, and global ordering rate metrics pertaining to each of the particular interface elements included in the GUI grouping (Bar: Col. 6, Ln. 50-53 – “Popularity data 310B includes but is not limited to special characteristics of a product such as whether the item is part of a market trend, based on a popular character, based on a popular theme, the sales rank of the item, etc.”; Col. 5, Ln. 9-10 – “Popularity data 310B is related to the popularity of an item based on the items sales rank, user comments, etc.”; see also Col. 3, Ln. 19-34).
In regards to claim 4, the combination Bar/Kamotsky teaches the system of claim 2. Bar further discloses wherein the ranking values are utilized to order the ranked listing for the GUI grouping (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”; see also Col. 3, Ln. 19-34).
In regards to claim 5, the combination Bar/Kamotsky teaches the system of claim 4. Bar further discloses wherein the ranking values are generated using a weighted combination function that applies the weights to the one or more popularity signals and the particular network traffic-based importance signals (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”; see also Col. 3, Ln. 19-34).
In regards to claim 6, the combination Bar/Kamotsky teaches the system of claim 5. Yet Bar does not explicitly disclose wherein the machine learning model is a linear learning model that is trained to compute the weights for the weighted combination function.
However, Kamotsky teaches a recommender (Kamotsky: [abstract]), including
wherein the machine learning model is a linear learning model that is trained to compute the weights for the weighted combination function (Kamotsky: [0025] – “calculating recommendation scores comprises approximating, by the computer system using a first machine learning model, a linear function custom-character.sub.a2 to learn attribute-and-attribute value weights”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kamotsky with Bar for the reasons identified above with respect to claim 5.
In regards to claim 7, the combination Bar/Kamotsky teaches the system of claim 1. Bar further discloses wherein generating the network traffic-based importance signals includes: for each hierarchy level of the plurality of hierarchy levels, analyzing the engagement metrics for particular interface elements, of the interface elements, in a respective grouping of the plurality of GUI groupings (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; Col. 9, Ln. 60-65 – “routine 400 determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”; see also Col. 3, Ln. 19-34).
In regards to claim 8, the combination Bar/Kamotsky teaches the system of claim 1. Bar further discloses wherein the engagement metrics include click through rate metrics (Bar: Col. 4, Ln. 65 – Col. 5, Ln. 5 – “The browse data also includes information regarding the nature of the selections being made while in a particular browse node. This includes, but is not limited to, the number of times the item has been selected or clicked upon, the number of times an item has been selected for purchase or placed in a "shopping cart," and the number of times the item has actually been purchased”).
In regards to claim 11, Bar discloses a method comprising (Bar: [abstract]):
storing a category classification hierarchy that classifies items, offered via an electronic platform, into a plurality of browse categories, the category classification hierarchy comprising a plurality of hierarchy levels and each of the plurality of browse categories is associated with at least one of the plurality of hierarchy levels (Bar: Col. 4, Ln. 31-36 and Fig. 1 – “a catalog server 104 for retrieving catalog data 102 identifying items organized within one or more browse trees…browse tree is stored”; Col. 3, Ln. 10-42 – “A catalog of items (including each item's description) may be organized into a browse tree structure in order to facilitate searching….various items, which are logically arranged in the form of a hierarchical tree. Items are located within the browse tree based on their category. Each node of the browse tree (a.k.a., a ‘browse node’) may be associated with a category of items in a hierarchical manner…the ‘root’ node of a tree representing ‘ items’ has many browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc. Each such browse node may have different child browse nodes representing sub-categories. For example, a parent browse node such as ‘Kitchen & Housewares’ may have a number of child browse nodes such as ‘Dining,’ ‘Cookware,’ etc.”; Col. 3 , Ln. 19-21 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree”; Col. 3, Ln. 63 – “electronic retailers”);
monitoring user engagement metrics relating to interacting with the items (Bar: Col. 4, Ln. 52 – Col. 5, Ln. 5 – “relevance server 200 also receives relevance data 310…the relevance data 310 includes…a collection of browse data 310A…Included in the browse data 310A is clickstream data…browse data also includes information regarding the nature of the selections being made while in a particular browse node. This includes, but is not limited to, the number of times the item has been selected or clicked upon, the number of times an item has been selected for purchase or placed in a ‘shopping cart,’ and the number of times the item has actually been purchased”);
generating, based on the user engagement metrics and based on relationships between one or more of the plurality of hierarchy levels, one or more of the items, and one or more of the plurality of browse categories, shelf importance signals for the plurality of hierarchy levels, wherein a quantity of the plurality of hierarchy levels is less than a quantity of the plurality of browse categories (Bar: Col. 5, Ln. 19-24 – “The relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node. The ranked list of relevance scores 330 is then used to determine how to configure the display of items associated with a browse node”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”; Col. 2, Ln. 14-20 – “a browse relevance score component configured to determine browse relevance scores for each item associated with the category is also provided. The browse relevance score component determines the browse relevance scores based on…browse score”; Col. 4, Ln. 10-14 – “each item associated with a node receives a relevance score…items to become more or less relevant”; Col. 12, Ln. 17-33 – “a Web Page 800 corresponding to the display of items within the browse node ‘Kitchen & Housewares’ 802 will be described. The Web Page 800 is an example of Web page whose display configuration has been arranged according to embodiments of the present invention as described with respect to FIG. 4. The illustrative Web page 800 can include a browse menu 804 displaying a number of different item categories. For example, the browse menu can include categories corresponding to ‘Dining’ 806, ‘Cookware’ 808, ‘Cutlery’ 810, ‘Housewares’ 812, ‘Cook's Tools & Gadgets’ 814, and ‘Small Appliances’ 816. Each item category may further include subcategories that allow a user to narrow his or her search to more specific categories. For example, ‘Dining’ 806 includes sub-nodes representing subcategories for ‘Drinkware,’ ‘Dinnerware,’ and ‘Utensils’”; the examiner interprets the browse score to be the shelf importance signal);
receiving, from a computing device, a request to view a browse category of the plurality of browse categories (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”);,
retrieving, during runtime, based on receiving the request, and after generating the shelf importance signals for the plurality of hierarchy levels, particular shelf importance signals, of the shelf importance signals, for a hierarchy level, of the plurality of hierarchy levels, associated with the browse category items (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”);
generating a ranked item listing for the browse category based on retrieving, during runtime, the particular shelf importance signals for the hierarchy level and based on one or more weights (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”;); and
transmitting, based on the ranked item listing for the browse category, information to the computing device (Bar: Col. 6, Ln. 35-40 – “ranked list of relevance scores 330 is then transmitted to the retail server 106 which utilizes the relevance scores 330 to configure the display of a Web page representation of a browse node”; Col. 5, Ln. 52-60 – “If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”).
Yet Bar does not explicitly disclose one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement.
However, Kamotsky teaches a product recommender (Kamotsky: [abstract]), including
one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement (Kamotsky: [0007] – “the step (g) of calculating recommendation scores comprises approximating, by the computer system using a machine learning model, a quadratic function S[p, 1]=(ϕ[p, r].Math.W.sub.p[r, 1])×(ϕ[p, r].Math.W.sub.a[r,1]) to learn the product recommender weights W.sub.p and the attribute- and attribute-value recommender weights W.sub.a based at least in part on key-performance-indicator driving feedback signals”; [0139] – “Machine learning methods may be used to train one or more models to approximate the above-described signal weights W.sub.a and W.sub.p.”; the examiner notes “to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing” is merely intended use/result and accordingly is granted little to no patentable weight. Nonetheless, the limitation has been fully examined).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the machine learning of Kamotsky in the system of Bar because Bar already discloses computer mediums and analysis and Kamotsky is merely demonstrating that the mediums may be non-transitory and a model that may be used. Additionally, it would have been obvious to have included one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement as taught by Kamotsky because machine learning models are well-known and the use of it in a recommendation setting would have allowed for implementation of the method (Kamotsky: [0284]).
In regards to claim 12, the combination Bar/Kamotsky teaches the method of claim 11. Bar further discloses wherein generating the ranked item listing for the browse category includes: generating one or more item popularity signals for particular items, of the items, included in the browse category (Bar: Col. 4, Ln. 55-59 – “the relevance data 310 includes…popularity data 310B”; Col. 5, Ln. 9-10 – “Popularity data 310B is related to the popularity of an item based on the items sales rank, user comments, etc.”); and
utilizing the particular shelf importance signals and the one or more item popularity signals to determine an ordering for the ranked item listing (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”).
In regards to claim 13, the combination Bar/Kamotsky teaches the method of claim 12. Bar further discloses wherein the one or more item popularity signals include total order metrics, trending metrics, and global ordering rate metrics pertaining to each of the particular items included in the browse category (Bar: Col. 6, Ln. 50-53 – “Popularity data 310B includes but is not limited to special characteristics of a product such as whether the item is part of a market trend, based on a popular character, based on a popular theme, the sales rank of the item, etc.”; Col. 5, Ln. 9-10 – “Popularity data 310B is related to the popularity of an item based on the items sales rank, user comments, etc.”).
In regards to claim 14, the combination Bar/Kamotsky teaches the method of claim 12. Bar further discloses wherein utilizing both the particular shelf importance signals and the one or more item popularity signals to determine the ordering for the ranked item listing comprises: generating ranking scores for the particular items included in the browse category based, at least in part, on the one or more item popularity signals and the particular shelf importance signals (Bar: Col. 4, Ln. 55-59 – “the relevance data 310 includes…popularity data 310B”; Col. 5, Ln. 9-10 – “Popularity data 310B is related to the popularity of an item based on the items sales rank, user comments, etc.; Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”); and
ordering, based on the ranking scores are utilized to, the ranked item listing for the browse category (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”).
In regards to claim 15, the combination Bar/Kamotsky teaches the method of claim 14. Bar further discloses wherein the ranking scores for are computed using a weighted combination function that applies weights to the one or more item popularity signals and the particular shelf importance signals (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; Col. 9, Ln. 60-65 – “routine 400 determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”).
In regards to claim 16, the combination Bar/Kamotsky teaches the method of claim 15. Yet Bar does not explicitly disclose wherein the machine learning model is a linear learning model that is trained to compute the weights for the weighted combination function.
However, Kamotsky teaches a recommender (Kamotsky: [abstract]), including
wherein the machine learning model is a linear learning model that is trained to compute the weights for the weighted combination function (Kamotsky: [0025] – “calculating recommendation scores comprises approximating, by the computer system using a first machine learning model, a linear function custom-character.sub.a2 to learn attribute-and-attribute value weights”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kamotsky with Bar for the reasons identified above with respect to claim 11.
In regards to claim 17, the combination Bar/Kamotsky teaches the method of claim 11. Bar further discloses wherein generating the shelf importance signals includes: for each hierarchy level of the plurality of hierarchy levels, analyzing the user engagement metrics for particular items, of the items, in particular browse categories, of the plurality of browse categories, associated with the corresponding hierarchy level (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; Col. 9, Ln. 60-65 – “routine 400 determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”).
In regards to claim 18, the combination Bar/Kamotsky teaches the method of claim 11. Bar further discloses wherein the user engagement metrics include: click through rate metrics; add-to-cart rate metrics; and order through rate metrics (Bar: Col. 4, Ln. 65 – Col. 5, Ln. 5 – “The browse data also includes information regarding the nature of the selections being made while in a particular browse node. This includes, but is not limited to, the number of times the item has been selected or clicked upon, the number of times an item has been selected for purchase or placed in a "shopping cart," and the number of times the item has actually been purchased”).
In regards to claim 21, Bar discloses one or more computer-readable medium comprising instructions that, when executed by a processing resource, cause the processing resource to (Bar: Col. 2, Ln. 5-10; Col. 6, Ln. 10-24 and Fig. 3; Col. 4, Ln. 30-35 and Fig. 1):
storing a category classification hierarchy that classifies items, offered via an electronic platform, into a plurality of browse categories, the category classification hierarchy comprising a plurality of hierarchy levels and each of the plurality of browse categories is associated with at least one of the plurality of hierarchy levels (Bar: Col. 4, Ln. 31-36 and Fig. 1 – “a catalog server 104 for retrieving catalog data 102 identifying items organized within one or more browse trees…browse tree is stored”; Col. 3, Ln. 10-42 – “A catalog of items (including each item's description) may be organized into a browse tree structure in order to facilitate searching….various items, which are logically arranged in the form of a hierarchical tree. Items are located within the browse tree based on their category. Each node of the browse tree (a.k.a., a ‘browse node’) may be associated with a category of items in a hierarchical manner…the ‘root’ node of a tree representing ‘ items’ has many browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc. Each such browse node may have different child browse nodes representing sub-categories. For example, a parent browse node such as ‘Kitchen & Housewares’ may have a number of child browse nodes such as ‘Dining,’ ‘Cookware,’ etc.”; Col. 3 , Ln. 19-21 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree”; Col. 3, Ln. 63 – “electronic retailers”);
monitoring user engagement metrics relating to interacting with the items (Bar: Col. 4, Ln. 52 – Col. 5, Ln. 5 – “relevance server 200 also receives relevance data 310…the relevance data 310 includes…a collection of browse data 310A…Included in the browse data 310A is clickstream data…browse data also includes information regarding the nature of the selections being made while in a particular browse node. This includes, but is not limited to, the number of times the item has been selected or clicked upon, the number of times an item has been selected for purchase or placed in a ‘shopping cart,’ and the number of times the item has actually been purchased”);
generating, based on the user engagement metrics and based on one or more relationships between one or more of the plurality of hierarchy levels and one or more of the items, shelf importance signals for the plurality of hierarchy levels of, wherein a quantity of the plurality of hierarchy levels is less than a quantity of the plurality of browse categories (Bar: Col. 5, Ln. 19-24 – “The relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node. The ranked list of relevance scores 330 is then used to determine how to configure the display of items associated with a browse node”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”; Col. 2, Ln. 14-20 – “a browse relevance score component configured to determine browse relevance scores for each item associated with the category is also provided. The browse relevance score component determines the browse relevance scores based on…browse score”; Col. 4, Ln. 10-14 – “each item associated with a node receives a relevance score…items to become more or less relevant”; Col. 12, Ln. 17-33 – “a Web Page 800 corresponding to the display of items within the browse node ‘Kitchen & Housewares’ 802 will be described. The Web Page 800 is an example of Web page whose display configuration has been arranged according to embodiments of the present invention as described with respect to FIG. 4. The illustrative Web page 800 can include a browse menu 804 displaying a number of different item categories. For example, the browse menu can include categories corresponding to ‘Dining’ 806, ‘Cookware’ 808, ‘Cutlery’ 810, ‘Housewares’ 812, ‘Cook's Tools & Gadgets’ 814, and ‘Small Appliances’ 816. Each item category may further include subcategories that allow a user to narrow his or her search to more specific categories. For example, ‘Dining’ 806 includes sub-nodes representing subcategories for ‘Drinkware,’ ‘Dinnerware,’ and ‘Utensils’”; the examiner interprets the browse score to be the shelf importance signal);
receiving, from a computing device, a request to view a browse category of the plurality of browse categories (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”);,
retrieving, during runtime and based on receiving the request, particular shelf importance signals, of the shelf importance signals, for a hierarchy level, of the plurality of hierarchy levels, associated with the browse category (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 8, Ln. 46-49 – “When a new item is introduced, the routine 400 will provide a means of determining where it will be displayed within a Web page”; Col. 8, Ln. 30-35 – “After the category fitness scores are returned at subroutine block 500, at subroutine block 600 the browse scores for the item are calculated”; [Claim 1] – “generating a ranked list of browse relevance scores for each category node, each ranked list including the browse relevance scores for each item associated with the category node; in response to receiving a user-generated request corresponding to one of the category nodes, determining an arrangement of at least a portion of the items associated with the category node based at least in part upon the ranked list of browse relevance scores for the category node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”);
generating a ranked item listing for the browse category based on retrieving, during runtime, the particular shelf importance signals for the hierarchy level and based on one or more weights (Bar: Col. 3, Ln. 19-34 – “the browse tree may be displayed via a user interface as a collection of hyperlinks, each hyperlink corresponding and providing further access to a browse node of the tree…browse nodes corresponding to categories such as ‘Books,’ ‘Electronics,’ ‘Kitchen & Housewares,’ etc…a user may select a hyperlink associated with the…browse node”; Col. 5, Ln. 19-21 – “relevance server 200 uses the relevance data 310 to create a ranked list of relevance scores 330 for each item associated with a browse node”; Col. 5, Ln. 47-60 – “the retail server 110 receives the ranked catalog items from the relevance server 200 and determines a display configuration for the node…the category ‘Kitchen & Housewares’ is represented by its own browse node. If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”; Col. 9, Ln. 60 – Col. 10, Ln. 5 – “determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated at block 440. At block 470, all possible scores for the item per browse node and corresponding category will be organized into a ranked list”; Col. 6, Ln. 50-60 – “the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node”; Col. 9, Ln. 47-56 – “The price range score may be used as a mathematical weight to approximate how its price compares with other similar items. For instance, if a coffee mug is very expensive compared to other coffee mugs, the item can be given a low mathematical weight to pull the overall browse relevance score down. To the contrary, if an item is less expensive than other coffee mugs, the item may be given a high mathematical weight to move its score higher”;); and
transmitting, based on the ranked item listing for the browse category, information to the computing device (Bar: Col. 6, Ln. 35-40 – “ranked list of relevance scores 330 is then transmitted to the retail server 106 which utilizes the relevance scores 330 to configure the display of a Web page representation of a browse node”; Col. 5, Ln. 52-60 – “If the ranked list of relevance scores 330 shows that the coffee mug featuring a popular television personality is the highest ranked item within the ‘Kitchen & Housewares’ browse node, the retail server will configure the display for that node such that the coffee mug is prominently displayed on the Web page representative of the ‘Kitchen & Housewares’ browse node”).
Yet Bar does not explicitly disclose non-transitory computer-readable medium; and one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement.
However, Kamotsky teaches a product recommender (Kamotsky: [abstract]), including
non-transitory computer-readable medium (Kamotsky: [0284] – “one or more computer program products, such as one or more computer programs tangibly embodied in a non-transitory information carrier (e.g., in a machine readable storage device)”); and
one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement (Kamotsky: [0007] – “the step (g) of calculating recommendation scores comprises approximating, by the computer system using a machine learning model, a quadratic function S[p, 1]=(ϕ[p, r].Math.W.sub.p[r, 1])×(ϕ[p, r].Math.W.sub.a[r,1]) to learn the product recommender weights W.sub.p and the attribute- and attribute-value recommender weights W.sub.a based at least in part on key-performance-indicator driving feedback signals”; [0139] – “Machine learning methods may be used to train one or more models to approximate the above-described signal weights W.sub.a and W.sub.p.”; the examiner notes “to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing” is merely intended use/result and accordingly is granted little to no patentable weight. Nonetheless, the limitation has been fully examined).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the non-transitory devices and machine learning of Kamotsky in the system of Bar because Bar already discloses computer mediums and analysis and Kamotsky is merely demonstrating that the mediums may be non-transitory and a model that may be used. Additionally, it would have been obvious to have included non-transitory computer-readable storage devices and one or more weights that are selected using a machine learning model that is trained to optimize one or more types of engagement as taught by Kamotsky because non-transitory devices and machine learning models are well-known and the use of it in a recommendation setting would have allowed for implementation of the method (Kamotsky: [0284]).
In regards to claim 22, the combination Bar/Kamotsky teaches the medium of claim 21. Bar further discloses wherein the hierarchy level is a first hierarchy level of the plurality of hierarchy levels, wherein the browse category is a first browse category of the plurality of browse categories, wherein the particular shelf importance signals are first shelf importance signals of the shelf importance signals, wherein the plurality of hierarchy levels further include a second hierarchy level and a third hierarchy level, wherein the second hierarchy level includes a second browse category and a third browse category that are subsets of the first browse category, wherein the first shelf importance signals include: a first shelf importance signal for a first item in the first hierarchy level, and a second shelf importance signal for a second item in the first hierarchy level and wherein the shelf importance signals further include: a third shelf importance signal for the first item in the second hierarchy level, and a fourth shelf importance signal for a third item in the second hierarchy level (Bar: Col. 2, Ln. 17-20 – “The browse relevance score component determines the browse relevance scores based on information obtained from the…browse score component, and the popularity score component”; Col. 9, Ln. 60-65 – “routine 400 determines browse relevance scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated”; Col. 11, Ln. 27-40 – “the browse score may be determined by adding together the values determined in block 604, block 606, and block 608. For example, if while within the ‘Kitchen & Housewares’ browse node the coffee mug has been selected or clicked X number of times, selected for purchase Y number of times, and actually purchased Z number of times, all these values will be used to produce a browse score for the item under the ‘Kitchen & Housewares’ browse node. To the contrary, if the coffee mug has been selected or clicked A number of times, selected for purchase B number of times, and actually purchased C number of times under the ‘Celebrity Merchandise’ browse node, those values will be used together to produce a score for the item under the ‘Celebrity Merchandise’ browse node”; Col. 12, Ln. 17-33 – “a Web Page 800 corresponding to the display of items within the browse node ‘Kitchen & Housewares’ 802 will be described. The Web Page 800 is an example of Web page whose display configuration has been arranged according to embodiments of the present invention as described with respect to FIG. 4. The illustrative Web page 800 can include a browse menu 804 displaying a number of different item categories. For example, the browse menu can include categories corresponding to ‘Dining’ 806, ‘Cookware’ 808, ‘Cutlery’ 810, ‘Housewares’ 812, ‘Cook's Tools & Gadgets’ 814, and ‘Small Appliances’ 816. Each item category may further include subcategories that allow a user to narrow his or her search to more specific categories. For example, ‘Dining’ 806 includes sub-nodes representing subcategories for ‘Drinkware,’ ‘Dinnerware,’ and ‘Utensils’”).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bar, in view of Kamotsky, in view of previously cited Murahari et al. (US 20170148082 A1), hereinafter Murahari.
In regards to claim 9, the combination Bar/Kamotsky teaches the system of claim 1. Bar further discloses wherein the network traffic-based importance signals, for the plurality of hierarchy levels, are computed in a pre-processing operation (Bar: Col. 4, Ln. 9-14 – “a display configuration is a visual arrangement of items or objects for use in generating a graphical presentation of those items or objects. In one embodiment, each item associated with a node receives a relevance score. This can be done when a node with a group of items is established, as new items are added to an existing node, and/or as events change causing items to become more or less relevant”).
Yet Bar does not explicitly disclose computing offline.
However, Murahari teaches a recommender (Murahari: [0018]), including
computing offline (Murahari: [0022] – “the offline data creation module 102 comprises a “customer segment to product super sub category mapping” algorithm to determine most relevant products for a particular customer segment… The offline data creation module 102 assigns affinity score for each identified product”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the offline computing of Murahari in the combined system of Bar/Kamotsky because Bar/Kamotsky already discloses a computation and Murahari is merely demonstrating that the computation may be offline. Additionally, it would have been obvious to have included computing offline as taught by Murahari because offline computations are well-known and the use of it in a recommendation setting would have provided relevant recommendations (Murahari: [0018]).
In regards to claim 19, the combination Bar/Kamotsky teaches the method of claim 11. Bar further discloses wherein the shelf importance signals are generated in a pre-processing operation (Bar: Col. 4, Ln. 9-14 – “a display configuration is a visual arrangement of items or objects for use in generating a graphical presentation of those items or objects. In one embodiment, each item associated with a node receives a relevance score. This can be done when a node with a group of items is established, as new items are added to an existing node, and/or as events change causing items to become more or less relevant”).
Yet Bar does not explicitly disclose computing offline.
However, Murahari teaches a recommender (Murahari: [0018]), including
generating offline (Murahari: [0022] – “the offline data creation module 102 comprises a “customer segment to product super sub category mapping” algorithm to determine most relevant products for a particular customer segment… The offline data creation module 102 assigns affinity score for each identified product”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the offline computing of Murahari in the combined system of Bar/Kamotsky because Bar/Kamotsky already discloses a computation and Murahari is merely demonstrating that the computation may be offline. Additionally, it would have been obvious to have included computing offline as taught by Murahari because offline computations are well-known and the use of it in a recommendation setting would have provided relevant recommendations (Murahari: [0018]).
Response to Arguments
Applicant’s arguments, filed 04/29/2026, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims do not recite an abstract idea because “the final Office actions fails to identify any ‘abstract idea as it is recited’…and explain why it falls within of the enumerated groupings” (Remarks pages 12-13). The examiner disagrees. The claims recite a process, article of manufacture, and machine for receiving engagement data, performing an analysis, and causing a display of ranked listings. Therefore, the claims being directed to an abstract idea of ranking items according to category to recommend item listings is not a description of the claims at a high level of abstraction untethered from the language of the claim. The examiner points to court decisions, such as Ultramercial Inc. v. Hulu and Cyberfone Systems v. CNN Interactive Group, for examples of claims that, on their face, recited complex ideas/interactions but were construed more broadly in terms of the abstract idea they directed themselves to. The MPEP further highlights that a claim recites a judicial exception (e.g., an abstract idea) when the judicial exception is “set forth” or “described” in the claim. Claims can “describe” an abstract concept without ever explicitly stating the abstract concept, e.g., the claims in Alice “described” the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” Furthermore, the examiner cited to specific language in the claims that was encompassed by the abstract idea. Thus, the claims recite a mental process and certain methods of organizing human activity.
Applicant argues the claims are integrated into a practical application because the claims “conserve computational resources and improve the functioning of the claimed computer system.” (Remarks pages 13-15). The examiner disagrees. Initially, the examiner notes that no formal agreements were made in the examiner interview. Further, the MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of ranking items according to grouping to recommend item listings. This is illustrated in specification paragraph [0003] which discusses the invention relating to relevant recommendations. With respect to Applicant’s argument that the claimed invention provides the benefit of conserving computational resources, the examiner notes that merely decreasing a number of scores calculated is not a technical improvement, but rather an improvement to the abstract idea.
Although the claims include computer technology such as a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute functions; an electronic platform; graphical user interfaces (GUIs); interface elements; graphical user interface (GUI) groupings; network traffic-based importance signals; network traffic-weighted data; a machine learning model that is trained to optimize; hyperlinks; computing devices; a computing device of the one or more computing devices; retrieving during runtime; a ranking engine; transmitting data; execution of computing instructions by one or more processors and stored on one or more non-transitory computer-readable storage devices; and a non-transitory, computer-readable medium comprising instructions that, when executed by a processing resource, cause the processing resource, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of ranking items according to category to recommend item listings in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of ranking items according to category to recommend item listings. The claimed process, while arguably resulting in a more accurate process for ranking items according to category to recommend item listings, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to products while still employing the same server and/or computer components used in conventional systems to improve ranking items according to category to recommend item listings, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application.
35 U.S.C. § 103
Applicant argues the claims are allowable over the cited art because the cited art does not teach or disclose “executing a ranking engine to generate a ranked listing for the GUI grouping by, to generate ranking values for the GUI grouping, combining the particular network traffic-based importance signals for the hierarchy level with one or more popularity signals, derived from the engagement metrics, by applying weights, to the particular network traffic-based importance signals for the hierarchy level and the one or more popularity signals, that are selected using a machine learning model that is trained to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing” (Remarks page 16). The examiner disagrees. Initially, the examiner notes that no formal agreements were made in the interview. Further, Bar discloses executing a ranking engine to generate a ranked listing for the GUI grouping by, to generate ranking values for the GUI grouping, combining the particular network traffic-based importance signals for the hierarchy level with one or more popularity signals, derived from the engagement metrics, by applying weights, to the particular network traffic-based importance signals for the hierarchy level and the one or more popularity signals. Bar discloses this at least in Col. 9, Ln. 60 – Col. 10, Ln. 5, disclosing determining scores for the item based on each browse node into which it can be categorized. In one embodiment, for each browse node into which an item can be categorized, a sum of the category fitness score, browse score, and popularity score is calculated. The sum multiplied by the price range score is calculated, where the price range score is a weight (see Col. 9, Ln. 47-56) Bar additionally discloses in Col. 6, Ln. 50-60 that the popularity score is determined based on the sales rank score and the special prediction boost score. The popularity score provides another means of ensuring that a popular or high selling item is prominently displayed within a Web page for items within a particular browse node. Bar further discloses in Col. 5, Ln. 19-21 creation of a ranked list of relevance scores for each item associated with a browse node. Additionally, Kamotsky is cited as teaching weights that are selected using a machine learning model that is trained to optimize the computing device being used for two or more different types of engagement with a graphical user interface (GUI) that displays the ranked listing. Kamotsky teaches this in [0007], teaching approximating, by the computer system using a machine learning model, a quadratic function to learn the product recommender weights and the attribute- and attribute-value recommender weights based at least in part on key-performance-indicator driving feedback signals. Thus, the cited art teaches this limitation in this claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Previously cited Bender et al. (US 20230289868 A1) teaches product rankings according to categories. Categories are arranged in a hierarchal taxonomy. Products can be ranked for each category and displayed to a user.
NPL reference U, initially cited in the Office action dated 07/01/2025, teaches ranking items according to category, such that an item may be displayed as being number one ranked in a given category. This may be done for categories and subcategories. Subcategories and categories may be in a browse tree.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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