Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 12/11/2025. Claims 1-15, 21-25 are currently pending and have been examined. Claims 16-20 stand cancelled. Claims 1-9, 11-15, 21-25 have been amended. The prior art rejections have been overcome by amendment.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-10 are directed to a machine, claims 11-15 are directed to a process, and claims 21-25 are directed to an article of manufacture Therefore, claims 1-15 and 21-25 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. 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 One 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 Two of Step 2A).
Claims 1, 11, and 21 recite at least the following limitations that are believed to recite an abstract idea:
preparing a first model for search item ranking using data and based on a first loss algorithms and a first set of features relating to brand affinity signals and other rerank signals including one or more query-level signals, one or more item-level signals, and one or more query-item-level signals;
preparing a second model for browse item ranking using the data and based on a second loss algorithm and using a second set of features relating to brand affinity signals and the other rerank signals,
wherein the second set of features includes fewer of the other rerank signals and fewer total features than the first set of features;
receiving a request from a user to view a page, wherein the request indicates one of a search request to view a search results page or a browse request to view a browse shelf page;
performing parallel requests including (i) a first request to a service to obtain respective brand affinity scores for the user for each of two or more product types associated with the request, and (ii) a second request to a search means to obtain a baseline ranking of items based on the request,
wherein the parallel requests obtain the respective band affinity scores before initiating a reranking operation with respect to the baseline ranking of items to reduce latency;
generating respective brand affinity signals for the user for respective items in the baseline ranking of items, based on the request and the respective brand affinity scores for the user for the two or more product types associated with the respective items, such that a first brand affinity signal of the respective brand affinity signals for a first item of the respective items associated with a first product type of the two or more product types is different from a second brand affinity signal of the respective brand affinity signals for a second item of the respective items associated with a second product type of the two or more product types while the first item and the second item are associated with an identical brand, wherein the respective brand affinity signals indicate affinities for the first product type and the second product type of the identical brand;
selecting a model, from the first model or the second model, based on whether the request indicates the search request or the browse request, wherein the second model is configured to generate outputs using the second set of features that includes fewer total features than the first set of features than the first model is configured to use to generate outputs;
generating a reranking of the respective items to be displayed on the page, based on the model and based on the respective brand affinity signals for the user for the respective items; and
outputting to the user the reranking of the respective items to be displayed on the page.
The above limitations recite the concept of personalized ranking of search results. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-15 and 21-25 an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A system comprising a processor and a non-transitory computer-readable medium storing processor-executable computing instructions
Training machine learning models
List-wise and pair-wise loss
A user device used by a user
A website
Performing parallel API calls
A search engine
The method being computer-implemented
A non-transitory computer-readable medium storing processor-executable computing instructions
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 2-6, 12-15, and 22-25 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 7-10 these claims are similar to the independent claims except that they recite the further additional elements of XGBoost tree/linear models being trained. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A system comprising a processor and a non-transitory computer-readable medium storing processor-executable computing instructions
Training machine learning models
List-wise and pair-wise loss
A user device used by a user
A website
Performing parallel API calls
A search engine
The method being computer-implemented
A non-transitory computer-readable medium storing processor-executable computing instructions
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable over Prior Art of Record
Claims 1-15 and 21-25 are allowable over prior art though rejected on other grounds [e.g. 35 USC §101] as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1-15 and 21-25 would be allowable if rewritten to overcome the rejections under 35 USC §101 as set forth in this Office Action, and to include all of the limitations of the base claim and any intervening claims.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1-15 and 21-25 are allowable over prior art. The most related prior art patent of record is Khandelwal (US 20080033939 A1), Gangaikondan-Iyer et al (US 20220028513 A1), and Zagorin et al (US 20240144137 A1).
Khandelwal teaches a system including a webstore that offers search engine capabilities to search for product information [0017]. A set of attributes of a category of the searched product is extracted, such as a brand, and how many times a user queries for that brand [0023]. The system calculates score for each attribute brand in a matrix [0037] combining the score with weights to generate a product rank value [0019], which is used to dynamically rank products according to current stored information through updated re-ranking [0046]. The ranked products are output to the user as search results [0019].
Gangaikondan-Iyer teaches a system for recommendations and search [0141] in which APIs are called sequentially and concurrently [0263] or in parallel [0275]. The system uses machine learning models to automatically determine rankings [0128], which are trained to perform steps automatically [0183].
Zagorin teaches systems to rank items to be recommended to a purchaser [Abstract], including using an XGBoost model [0020] that is iteratively trained on training data for prediction queries [0022].
However, each of these references fail to disclose or render obvious at least the limitations of: training a first machine learning model for search item ranking using training data and based on list-wise loss and a first set of features relating to brand affinity signals and other rerank signals including one or more query-level signals, one or more item-level signals, and one or more query-item-level signals; training a second machine learning model for browse item ranking using the training data and based on pair-wise loss and using a second set of features relating to the brand affinity signals and the other rerank signals, wherein the second set of features includes fewer of the other rerank signals and fewer total features than the first set of features; selecting a machine learning model, from the first machine learning model or the second machine learning model, based on whether the request indicates the search request or the browse request, wherein the second machine learning model is configured to generate outputs using the second set of features that includes fewer total features than the first set of features that the first machine learning model is configured to use to generate outputs.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of the cited references, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
The references, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable over prior art (though rejected under 35 USC §101) as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Applicant's arguments filed 12/11/2025 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant argues with reference to Example 39 that the claims recite training of machine learning models in a way that, at least for the amended training steps, the claims are not directed to an abstract idea. Applicant further argues that the training steps “improve[] machine learning by dynamically selecting between the first and second machine learning models based on the type of request received from the user device. This selection between the models reduces use of the model with the larger feature set in favor of the model with the smaller feature set, thereby conserving processing resources relative to using a single, larger model for every request.”
Examiner respectfully disagrees. Example 39 provides a claim that recites machine learning steps without claiming mathematical calculations, or any method of organizing human activity such as a fundamental economic concept or managing interactions between people. In contrast, the pending claims recite steps classified in the concept of personalized ranking of search results. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. The computer-related additional elements are recited at a high level of generality. The claims fail to establish that one model is more processing-intensive than another, nor does the disclosure suggest that using a small model in some circumstances but not others is a solution to a technological problem of computational efficiency. Rather, the claims merely invoke the additional element, such as the machine learning models, as mere instructions to apply the abstract idea to a technical environment [MPEP 2106.05(f)], creating only a general linking between the abstract idea, including steps of selecting one process over another depending on the type of request, and generic computer components.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689