DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant’s amendment and remarks filed March 30, 2026, are responsive to the office action mailed December 29, 2025. Claims 1, 3-6, 8-9, 11-14, 16-17, and 19-23, were previously pending. Claims 1, 9, and 17, have been amended and claims 24-25 are new. Claims 1, 3-6, 8-9, 11-14, 16-17, and 19-25, are therefore currently pending and considered in this office action.
Response to Arguments
Pertaining to rejection under 35 USC § 101 in the previous office action
Applicant's arguments filed March 30, 2026, have been fully considered but they are not persuasive. Claims 1, 3-6, 8-9, 11-14, 16-17, and 19-23, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding step 2A prong one, applicant’s argument does not address the detailed rationale explaining the rejection, and therefore does not address the explicit criteria for identifying the recitation of an abstract idea that is defined in the MPEP and applied as explained in the detailed rationale for the rejection. It is therefore not persuasive.
Regarding step 2A prong two, applicant argues “the claims recite additional elements that reflect "an improvement in the functioning of a computer, or an improvement to other technology or technical field." Remarks p.10. Applicant again does not address the explicit criteria for identifying additional elements as defined in the MPEP and as applied in the detailed rationale for the rejection. Applicant does not identify any additional elements. The argument is therefore not persuasive.
Regarding step 2B applicant argues
“that receiving feedback data from users via the frontend system, applying the feedback data to training updated variant-aware ranking models, and executing the updated ranking models to generate a second ranked list of candidate items represents an unconventional combination of features that confine the claims to a particular useful application." Remarks p.11.
Applicant identifies a single non-specific additional element “the frontend system,” which is not a particular device as defined in the MPEP, together with the abstract idea that the frontend system does not itself implement or operate, and states that the abstract idea is applied. Whether conventional or not there is no recited additional element (again as defined in the MPEP and explained in the rejection) and no recited step or other recitation that indicates that the additional element integrates the abstract idea into a practical application, and there is no assertion of any technical problem or presentation of any technical solution to a technical problem. Applicant again does not address the criteria for making a determination under step 2B as defined in the MPEP and as applied in the detailed rationale for the rejection. The argument is therefore not persuasive.
Pertaining to rejection under 35 USC § 103 in the previous office action
Applicant's arguments filed March 30, 2026, have been fully considered but they are not persuasive. Claims 1, 3-6, 8-9, 11-14, 16-17, and 19-25, are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (Paper No. 20240930; Pub. No. US 2021/0240739 A1) in view of Kalinin et al. (Paper No. 20250403; Patent No. US 9,135,396 B1), further in view of Fu et al. (Paper No. 20240930; Pub. No. CN 113590931 A), and further in view of Sandler et al. (Patent No. US 11,004,135 B1).
Applicant argues
“The cited references fail to teach or suggest "receive feedback data from the frontend system indicative of one or more interactions with the interface; training an updated variant-aware ranking model by iteratively applying the feedback data from the frontend system for each of the interactions with the interface," as recited in Claim 1. For example, Pan recites "variant groups can be suggested to groups of humans to review, and/or can be used without human review" and fails to teach or suggest "receive feedback data from the frontend system indicative of one or more
interactions with the interface; training an updated variant-aware ranking model by iteratively applying the feedback data from the frontend system for each of the interactions with the interface," as recited in Claim 1.” Remarks pp.12-13.
Applicant only discusses Pan and does not address any other of the relied upon references. As detailed in the rejection below, Pan describes receiving feedback from the frontend system, but does not teach that this feedback indicates interaction with the interface. Fu however explicitly describes feedback indicating interaction with the interface, and Sandler teaches “training an updated variant-aware ranking model by iteratively applying the feedback data from the frontend system for each of the interactions with the interface”. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
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, 3-6, 8-9, 11-14, 16-17, and 19-25, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1, 3-6, 8, and 24-25, are directed to a machine, claims 9, 11-14, and 16, are directed to a process, and claims 17 and 19-23 are directed to a manufacture. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving ... a request for an interface, the request including a search query…; identifying, based on the request, a set of candidate items selected from an item catalog, wherein at least one candidate item of the set of candidate items is representative of two or more variant items in the item catalog, wherein the two or more variant items are excluded from the set of candidate items; for each of the two or more variant items, iteratively generating a variant score based on historical interactions for a respective one of the two or more variant items; iteratively inputting the variant score for each variant item to at least one variant-aware ranking model and execute the at least one variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply a semantic context of the request to generate a relevancy score for each of the two or more variant items, and in response, apply the variant score and relevancy score for each variant item to generate a set of recommended items including a ranked list of one or more candidate items of the set of candidate items and at least one of the two or more variant items; generating the interface with a reduced interface element set including the set of recommended items; transmitting the interface to the frontend … that generated the request for the interface; receiving feedback data … indicative of one or more interactions with the interface; training an updated variant-aware ranking model by iteratively applying the feedback data … for each of the interactions with the interface; and executing the updated variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply the semantic context of the request to generate a second relevancy score for each of the two or more variant items, and in response, apply the variant score and the second relevancy score for each variant item to generate a second set of recommended items including a second ranked list of one or more candidate items of the set of candidate items and at least one variant item of the two or more variant items.” Claim 17.
A careful analysis of the above limitations, each on its own and all together combined, results in the conclusion that at least one abstract idea is recited. All limitations in combination altogether recite only a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as mental processes such as an observation, evaluation, judgment, or opinion, and certain methods of organizing human activity, for example commercial interactions (including advertising, marketing or sales activities or behaviors). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements are a non-transitory memory, processor, and “a frontend system,” in claims 1, 3-6, 8, and 24-25, a processor and “frontend system” in claims 9, 11-14, and 16, and a non-transitory computer-readable medium and “a frontend system” in claims 17 and 19-23. Claims 17 and 19-23 provide that the instructions in memory can be executed by a processor.
These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the “frontend system” is configured to interact with one or more user systems and receives a request for an interface including a search query. The interface is not claimed with any specificity or in connection with any particular function in relationship to any device. It appears only to be a non-substantive software element being designated as a conduit for transfers of data and so is not considered an additional element. The at least one processor of claims 1, 3-6, 8, and 24-25 (and theoretically claims 17 and 19-23 as well) is all alone broadly and generally recited as performing all steps in terms of the intended results of functionally nonspecific activities described as being embodied in computer readable instructions. These additional elements do not integrate the judicial exception into a practical application because the claims lack any indication of any particular additional element practically applying any of the abstract elements. The iterative generation of a variant score is an aspirational recitation of an abstract concept, including no particular device(s) or structure for its implementation, and based on a review of the specification no particular detail regarding any machine learning process engaged or how such process would be implemented in any system architecture or structure. The claims are therefore almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of the steps without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Any claimed elements that are not abstract are identified broadly and generally as applying an abstract conceptual method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any particular specific functional acts performed by any particularly identified machines, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the claim are merely generic computer props supporting instructions to implement an abstract idea or other exception in computer environment. MPEP 2106.05(f). Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”); MPEP 2106.05(f)(2). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
This conclusion is supported by applicant's disclosure, which elaborates upon the performance of the presently claimed method at length by describing the abstract ideas in detail while only incidentally or tangentially explaining the preexisting (prior art) computer equipment, without identifying any technical problem that arises within said equipment and without offering a technical solution to any such problem. It ultimately only describes the abstract idea while indicating the intention to “apply it.”
Finally, dependent claims 3-6, 8, 11-14, 16, and 19-25, do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the identification and manipulation of data, and judgements exercised, in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1, 3-6, 8-9, 11-14, 16-17, and 19-25, are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-6, 8-9, 11-14, 16-17, and 19-25, are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (Paper No. 20240930; Pub. No. US 2021/0240739 A1) in view of Kalinin et al. (Paper No. 20250403; Patent No. US 9,135,396 B1), further in view of Fu et al. (Paper No. 20240930; Pub. No. CN 113590931 A), and further in view of Sandler et al. (Patent No. US 11,004,135 B1).
Pan teaches a method for determining a group of variant items to recommend based on a request including items selected in a catalog. Pan further discloses, regarding
Claim 1. A system, comprising:
● a processor (Pan fig. 2); and ● a non-transitory memory storing instructions (see at least Pan fig. 2) that, when executed, cause the processor to:
● receive a request from a frontend system for an interface, the request including a
search query, wherein the frontend system is configured to interact with one or more user systems (see at least Pan ¶0025 “interfaces with an application (e.g., a mobile application), on user device 340, which can allow users to browse and/or search for items (e.g., products”);
Pan teaches
● identify, based on the request, a set of candidate items selected from an item catalog, wherein at least one candidate item of the set of candidate items is representative of two or more variant items in the item catalog (see at least Pan abstract “determining one or more items in the candidate variant items, as filtered, to include in a variant group for the first item,” fig. 4, ¶0002. Please note: “one or more” includes “two or more.”), but does not disclose wherein the two or more variant items are excluded from the set of candidate items.
Kalinin also teaches a method for determining a group of variant items to recommend based on a request including items selected in a catalog, and further discloses, wherein the two or more variant items are excluded from the set of candidate items (see at least Kalinin c3:64-c4:8 and c12:59-67 "presenting only a single search result for a given variant set (e.g., a "parent" search result). The other search results of that variant set (e.g., "child" search results) may be hidden from view in the list of search results.")
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Pan to include wherein the two or more variant items are excluded from the set of candidate items, as taught by Kalinin since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Kalinin in the method of Pan. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Pan in view of Kalinin teaches a) a set of candidate items, b) at least one of the candidate items is representative of two or more variant items, c) a variant score, and d) generating a set of recommended items, and discloses
● for each of the two or more variant items, iteratively generate a variant score (see at least Pan abstract “k-nearest neighbors approach to search for first candidate variant items.... determining a respective distance between the first item and each of the candidate variant items,” ¶0046 “logistic regression model can be trained on pairs of items (e.g., pairs of item identifiers). ...logistic regression model can determine whether the pair is a variant,” ¶¶0066-0068, and Kalinin abstract “similarity score assigned to that aligned item pair, the system may generate an indication specifying that each of a set of items are variants,” c11:39-50 “knowledge base that includes such sets of words may be iteratively updated”); but does not explicitly disclose the variant score is iteratively generated based on historical interactions for a respective variant item of the two or more variant items.
Fu also teaches a) a set of candidate items, b) at least one of the candidate items is representative of two or more variant items, c) a variant score, and d) generating a set of recommended items, and Fu further discloses
● generate a variant score based on historical interactions for a respective variant item of the two or more variant items (see at least Fu p.3¶11 “particular amount or number of user activities for listing items indicates a type of demand for an item based on a user 's history participation of the item,” p.9¶3 “Each variant is associated with a particular number of user activities, impressions, views, and sales”).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Pan in view of Kalinin to include wherein the variant score is determined based on historical interactions for a respective variant item of the two or more variant items, as taught by Fu since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Fu in the method of Pan in view of Kalinin. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Pan in view of Kalinin and further in view of Fu teaches all of the above and discloses a) a set of candidate items, b) at least one of the candidate items is representative of two or more variant items, c) a variant score, and d) generating a set of recommended items, but does not explicitly disclose iteratively input the variant score for each variant item to at least one variant-aware ranking model and execute the at least one variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply a semantic context of the request to generate a relevancy score for each of the two or more variant items, and in response, apply the variant score and the relevancy score for each variant item to generate a set of recommended items including a ranked list of one or more candidate items of the set of candidate items and at least one variant item of the two or more variant items.
Sandler also teaches a) a set of candidate items, b) at least one of the candidate items is representative of two or more variant items, c) a variant score, and d) generating a set of recommended items, and further discloses
● iteratively input the variant score for each variant item to at least one variant-aware ranking model and execute the at least one variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply a semantic context of the request to generate a relevancy score for each of the two or more variant items, and in response, apply the variant score and the relevancy score for each variant item to generate a set of recommended items including a ranked list of one or more candidate items of the set of candidate items and at least one variant item of the two or more variant items (see at least Sandler abstract “disclosure is directed to training, and providing recommendations via, a machine learning model architected to balance relevance and diversity of sets of recommendations…. ranked list of recommendations is provided to a diversity model that maximizes an optimization objective having a first objective that quantifies relevance of a recommendation and a second objective that measures diversity of a set of recommendations. The output of the diversity model is a set of recommendations that have both high relevance and high diversity,” figs. 2A, 2C, 3, c3:20-30 “context of a pantry catalog and larger electronic catalog, the disclosed relevance score determinations can also be applied to other first and second catalogs,” c6:55-67 “semantically similar words are mapped to nearby points,” c7:40-55 “discussed in the example context of natural language words, it will be appreciated that the vector conversion vocabulary can be structured to accommodate non-word features as well,” and in view of Pan abstract “k-nearest neighbors approach to search for first candidate variant items.... determining a respective distance between the first item and each of the candidate variant items,” fig. 4, ¶¶0043, 0045 in view of Fu p.2¶¶3-4 “ranking of the more popular listed item is higher than the list item of not popular. In some cases, a single listed item has a plurality of variants. The "variant" is a particular iteration of the item, an example or a subcategory. … where the MSKU and the listed terms with variants are used for search ranking…. generating user activity data for each variant of the item and ranking each corresponding listed item (and/or variant)”. Please note: although Pan discusses scoring sets of recommended items it also discusses determining the score by valuing the relevance of individual items, such as by the methods above (e.g., k-nearest and distance)).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Pan in view of Kalinin and further in view of Fu to include iteratively input the variant score for each variant item to at least one variant-aware ranking model and execute the at least one variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply a semantic context of the request to generate a relevancy score for each of the two or more variant items, and in response, apply the variant score and the relevancy score for each variant item to generate a set of recommended items including a ranked list of one or more candidate items of the set of candidate items and at least one variant item of the two or more variant items, as taught by Sandler since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Sandler in the method of Pan in view of Kalinin and further in view of Fu. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Pan in view of Kalinin and further in view of Fu and Sandler discloses
● generate the interface with a reduced interface element set including the set of recommended items (see at least Pan abstract “generating candidate variant items,” ¶0025 “server that interfaces with an application (e.g., a mobile application), on user device,” ¶0043 “generating candidate variant items from the item catalog”); and
● transmit the interface to the frontend system that generated the request for the interface (see at least Pan ¶0025 “web server 320 can be in data communication through Internet 330 with one or more user devices, such as a user device 340. .... web server 320 can host one or more websites .... or provide a server that interfaces with an application (e.g., a mobile application), on user device 340, which can allow users to browse and/or search for items (e.g., products),” ¶¶0034-0035 “suggest base variant groups from items provided by one or more sources (e.g., supplier), which can advantageously assist users in selecting items..... variant groups can be suggested to groups of humans”);
● receive feedback data from the frontend system indicative of one or more interactions with the interface (see at least Pan ¶0035 “With hundreds or thousands of updates to the items being received daily,” ¶0074 “there can be hundreds or thousands of updates to items that are received daily,” which describes receiving feedback from the frontend system, but does not teach that this feedback indicates interaction with the interface, in view of Fu p.5¶4 “generator 140 receives the user selection input of the variant type of the user wants to establish the sub-item, and using the specific variant value input by the user to fill the other data object,” ¶5 “in response to receiving the user activity, log engine 150 indicates user activity in the user activity record…. In response to the click, the log engine 150 may generate a first entry within the user activity log 114 to indicate that the user clicks the first listing,” explicitly describing feedback indicating interaction with the interface.);
● training an updated variant-aware ranking model by iteratively applying the feedback data from the frontend system for each of the interactions with the interface (Pan does not explicitly disclose this limitation, but see at least Sandler figs. 2-3, c1:19-25 “Computer learning models can process large volumes of user and item interaction data to provide relevant recommendations,” c10:28-50 “neural network can be trained using training data that includes input data and the correct or preferred output of the model for the corresponding input data. Events and associated data in historical user behavioral data can be converted into vector representations by the vector embedding generator 220, with user profile feature vectors provided to the nodes input layer 212 and corresponding user item interactions (e.g., purchases, rentals, streams, etc.) provided to the nodes of the output layer 216. … The user's entire behavioral data or a window including a subset of the data can be used for the training,” c10:57-c11:5 “The neural network 211 can repeatedly process the input data, and the parameters (e.g., the weight matrices) of the neural network 211 can be modified in what amounts to a trial-and-error process until the model produces (or "converges" on) the correct or preferred output. The modification of weight values may be performed through a process referred to as "back propagation." Back propagation includes determining the difference between the expected model output and the obtained model output, and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output”); and
● execute the updated variant-aware ranking model to independently rank each item in the set of candidate items and each of the two or more variant items and apply the semantic context of the request to generate a second relevancy score for each of the two or more variant items, and in response, apply the variant score and the second relevancy score for each variant item to generate a second set of recommended items including a second ranked list of one or more candidate items of the set of candidate items and at least one variant item of the two or more variant items (see at least Kalinin abstract “generate multiple item pairs each corresponding to a particular item and another item determined to be similar to the particular item. For the particular
item and the other item, each item pair may include a respective sequence of text strings (e.g., a title). … The system may also assign a similarity score to each item pair; … the system may generate an indication specifying that each of a set of items are variants of each other,” figs. 2-5, in view of Fu “The ranking engine 160 is generally responsible for ranking the search results based at least in part on user activity data for one or more variants of one or more listed items. … additionally or alternatively, one or more algorithms may include a semantic analysis engine, such as potential semantic index keyword (LSI) content,” Sandler abstract “disclosure is directed to training, and providing recommendations via, a machine learning model architected to balance relevance and diversity of sets of recommendations…. ranked list of recommendations is provided to a diversity model that maximizes an optimization objective having a first objective that quantifies relevance of a recommendation and a second objective that measures diversity of a set of recommendations,” figs. 2A, 2C, 3, c6:55-67 “semantically similar words are mapped to nearby points”).
Claim 3. The system of claim 1, wherein the variant-aware ranking model generates the relevancy score (see at least Pan abstract, fig. 4, ¶0043).
Claim 4. The system of claim 1, wherein the request for the interface includes a contextual identifier, and wherein the relevancy score is generated based on a correspondence between each variant item and the contextual identifier (see at least Fu p.8¶¶1, 3 “user device presents content, and context of presentation, such as how (or what format and how much content, which may depend on the user device or context) presentation”).
Claim 5. The system of claim 1, wherein the processor executes the instructions to, prior to generating the interface, filter the set of recommended items based on current availability of each variant item (see at least Pan abstract “a first item in an item catalog. ... classifications based on respective pairs comprising the first item and each of the candidate variant items to filter the candidate variant items”).Claim 6. The system of claim 1, wherein the variant score for each variant item is precalculated by a batch process (see at least Pan abstract “using a combination of (a) a k-nearest neighbors approach .... can include determining a respective distance between the first item and each of the candidate variant items, as filtered. ... determining one or more items in the candidate variant items, as filtered, to include in a variant group for the first item, based on a decision function using a predetermined threshold and the respective distance for the each of the candidate variant items”. Please note: “a batch process” as best understood by a person of ordinary skill, is the grouping of items together for efficient processing. This is disclosed by the variant item grouping process already discussed and disclosed throughout.).Claim 8. The system of claim 1, wherein the set of recommended items includes each of the two or more variant items (see at least Pan ¶0034 “suggest base variant groups from items provided by one or more sources (e.g., supplier), which can advantageously assist users in selecting items from among variants,” ¶0061 “to suggest multiple variant groups, as listed below in Table 1, in which each row is a suggested variant group”).
Claim 24. The system of claim 1, wherein the feedback data includes a user click rate or a user interaction rate (Pan in view of Kalinin does not disclose a user click rate or a user interaction rate, although “a user interaction rate” could reasonably be understood to refer to any user interaction and that certainly is disclosed by all the references, however see at least Fu abstract “first user activity data corresponds to a user input metric associated with the first variant. The second user activity data corresponds to a user input metric associated with the second variant,” p.2¶2 “The search engine logic may use the item metrics as signals in a search ranking algorithm. … algorithm can make the ranking of the more popular listed item is higher than the list item of not popular,” p.4¶1 “users can perform user activity, such as clicking”. Please note: the claim language “a user click rate or a user interaction rate” is redundant because a click is an interaction. For future reference however, applicant should be aware that claim language consisting of alternative limitations separated by “or” does not result in further limitation beyond a single alternative because beyond the presence of any single alternative it merely represents contingencies that are not required. Applicant is reminded that optional or conditional elements do not narrow the claims because they can always be omitted. See e.g. MPEP §2111.04 "Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure."; and In re Johnston, 435 F.3d 1381,77 USPQ2d 1788, 1790 (Fed. Cir. 2006) ("As a matter of linguistic precision, optional elements do not narrow the claim because they can always be omitted.").).
Claim 25. The system of claim 1, further comprising executing the at least one variant-aware ranking model of a recommendation engine (see at least Pan ¶0034 “variant group system 310 can use machine learning models to automatically create and/or suggest base variant groups from items provided by one or more sources” in view of Sandler abstract “providing recommendations via, a machine learning model architected to balance relevance and diversity of sets of recommendations. … can output probabilities for each of a number of recommendations. This can be converted into a ranked list of recommendations,” figs. 2-5).
Pertaining to method claims 9, 11-14, and 16, and medium claims 17 and 19-23
Rejection of method claims 9, 11-14, and 16, and non-transitory computer-readable medium claims 17 and 19-23, is based on the same rationale noted above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Raghavan et al., Patent No.: US 10,366,306 B1: teaches identifying an item from among variants of an item type using image to identify item type. Correlation score is determined based on a comparison of item attributes for each item and are compared with a confidence threshold.
● Jarret et al., Patent No.: US 10,990,601 B1: teaches product variants defined by one or more attributes from which a representative variant is selected based on a weight and various components of trained models used to rank variants according to metrics or other criteria, in which variants are assigned scores and compared with a threshold score or ranked within a certain number of positions.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 June 5, 2026
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