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 .
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.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 03/23/2026, Applicant, on 06/03/2026.
Status of Claims
Claims 1-3, 11-13, and 19-20 are currently amended.
Claim 10 is canceled.
Claims 1-9 and 11-20 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 112
The arguments have been fully considered and they are persuasive.
Accordingly, the Examiner withdraws the rejections of the pending claims under 35 USC § 112 in the present office action.
Response to Arguments - 35 USC § 101
The arguments have been fully considered, but they are not persuasive.
The Examiner respectfully disagrees.
The Examiner submits that the present claims recite an abstract idea. the claim falls into two categories of abstract ideas: Mathematical Concepts: It recites specific mathematical and statistical operations, including minimizing "cross-entropy loss," calculating a "Bayesian inference," and generating a "score" to rank pairs, and Certain Methods of Organizing Human Activity / Data Manipulation: Gathering engagement data over a time period, identifying historical click-through rates (CTR), and sorting/ranking items based on user preferences or metrics are conventional business/data filtering practices (similar to indexing or organizing information). While the claim attempts to solve a technical-sounding issue ("reduce prediction bias"), the solution itself is structured entirely around filtering input data features (using content-based features instead of engagement-based features) and performing calculations.
The claim relies on a "first machine learning model" and a "second machine learning model" used generically. It does not disclose a brand-new neural network architecture or a hardware-level acceleration mechanism. Using cross-entropy loss, Bayesian inference, and content-based features are standard, off-the-shelf mathematical toolkits in data science. The stated purpose—"to reduce prediction bias"—is a manipulation of data values to improve a mathematical prediction, not a physical or structural improvement to computing machinery.
Because the claim merely uses standard computer automation to execute a complex mathematical model for a ranking goal, it is "directed to" the abstract idea. As such, the present claims as amended do not integrate the abstract idea into practical application (Step 2A, Prong 2).
Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step, ’ rendering the claim eligible at Step 2B ” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354.
The limitations recite "determining... with a first machine learning model," "determining a click engagement feature," and "ranking." When viewed individually or as an ordered combination, these steps merely describe the logical, computational workflow of data processing using conventional computing methods. Simply limiting the field of use to online search/recommendation query-item pairs does not supply an inventive concept. The present claims are not eligible under Step 2B.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
Response to Arguments - 35 USC § 103
The arguments have been fully considered, but they are not persuasive.
The Examiner respectfully disagrees.
The Examiner provides the new reference Wang which teaches the amended limitations of “a first machine learning model trained to determine expected click-through-rate (CTRs) using a training objective of reducing a cross-entropy loss and using condensed training data comprising query- item pairs labeled with respective historical CTRs that are based on aggregated engagement data over a time period” as explained under the - 35 USC § 103 rejection (below). Applicant’s arguments in this regard are moot.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 103 in the present office action.
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 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-9 and 11-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-9 and 11-20 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of providing relevant search results for a user.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method comprising: determining expected click-through-rate (CTRs) using a objective of reducing a cross-entropy loss and using condensed data comprising query- item pairs labeled with respective historical CTRs that are based on aggregated engagement data over a time period, an expected CTR of a query-item pair by using content-based features as input to the first model without engagement-based features to reduce prediction bias in the expected CTR; determining a click engagement (CE) feature by determining a Bayesian inference based on the expected CTR and a historical CTR for the query-item pair; determining a score of the query-item pair by using the content-based features and the CE feature; and ranking the query-item pair based in part on the score and the expected CTR”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because predicting user preferences (click-through rates) and ranking items (such as ads or search results) to optimize engagement is a fundamental commercial/business practice. Courts routinely classify these activities as abstract methods of organizing human activity or data manipulation. In addition, the claim recites "reducing a cross-entropy loss," "determining a Bayesian inference," and calculating formulas or scores using variables. These are mathematical algorithms and formulas. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 11 and 19 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 11 and 19 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-9, 12-18, and 20 recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for providing relevant search results for a user. As a result, claims 2-9, 12-18, and 20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “computer-implemented”, “with a first machine learning model trained to determine”, “training”, “with a second machine learning model”. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. The claim relies on a "first machine learning model" and a "second machine learning model" used generically. It does not disclose a brand-new neural network architecture or a hardware-level acceleration mechanism. Using cross-entropy loss, Bayesian inference, and content-based features are standard, off-the-shelf mathematical toolkits in data science. The stated purpose—"to reduce prediction bias"—is a manipulation of data values to improve a mathematical prediction, not a physical or structural improvement to computing machinery. Because the claim merely uses standard computer automation to execute a complex mathematical model for a ranking goal, it is "directed to" the abstract idea. The Federal Circuit clarified in Recentive Analytics v. Fox Corporation (2025) that "claims that do no more than apply established methods of machine learning to a new data environment, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 11 and 19 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A system comprising: a processor; and a non-transitory computer-readable medium” and claim 19 further recites “A non-transitory computer readable storage medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 11 and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-9, 12-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 11, and 19. As a result, claims 2-9, 12-18, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “computer-implemented”, “with a first machine learning model trained to determine”, “training”, “with a second machine learning model”. The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. The limitations recite "determining... with a first machine learning model," "determining a click engagement feature," and "ranking." When viewed individually or as an ordered combination, these steps merely describe the logical, computational workflow of data processing using conventional computing methods. Simply limiting the field of use to online search/recommendation query-item pairs does not supply an inventive concept. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 11 and 19 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A system comprising: a processor; and a non-transitory computer-readable medium” and claim 19 further recites “A non-transitory computer readable storage medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 11 and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-9, 12-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 11, and 19. As a result, claims 2-9, 12-18, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-9 and 11-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
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 non-obviousness.
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.
Claims 1, 2-3, 11-13, and 19-20 are rejected under 35 U.S.C. 103 as being un-patentable over Wakankar et al. (US 20190171728 A1) in view of Wang (Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction, Yikai Wang, E-Commerce and advertising II, Beijing, China 2019.) and in further view of Chen et al. (US 20110302031 A1)
Regarding claim 1. Wakankar teaches a computer-implemented method comprising: [Wakankar, claim 1, Wakankar teaches “A method for providing type-ahead query suggestions, comprising: receiving a partial query entered by a user in a user interface” wherein a computer implemented method]
Wakankar does not specifically teach, however; Wang teaches determining, with a first machine learning model trained to determine expected click-through-rate (CTRs) using a training objective of reducing a cross-entropy loss and using condensed training data comprising query- item pairs labeled with respective historical CTRs that are based on aggregated engagement data over a time period, an expected CTR of a query-item pair by using content-based features as input to the first machine learning model without engagement-based features to reduce prediction bias in the expected CTR; [Wang, page 351, column 2, para. 5, Wang teaches “The pointwise loss function is a cross-entropy loss calculated as:” wherein reducing a cross-entropy loss. Further, Wang teaches “Historical user-item interactions are largely recorded and play a pivotal role in many real-world CTR prediction tasks. In our model, a single sample is composed of a pair of a user and a target item (recommended item), where the user’s information is represented by a series of user’s recent L clicking items, and the target item is selected and exposed to the user by the advertisement” wherein interaction time or time period]
Wakankar teaches generating type-ahead query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring and Wang teaches Sampling and Deep Time-aware Attention for Click-Through Rate Prediction. The two references are in the same field of endeavor as the claimed invention of managing click-through data. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine utilizing determining an expected click-through-rate (CTR) of a query-item pair of Wakankar with using cross-entropy loss and historical condensed training data of Wang 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, with the predictable results of optimizing cold start of ranking and surfacing new products.
Wakankar in view of Wang does not specifically teach, however; Chen teaches determining a click engagement (CE) feature by determining a Bayesian inference based on the expected CTR and a historical CTR for the query-item pair; [Chen, claim 1, Chen teaches “processing the historical click-through data to learn a Bayesian "outer model" of prior Gaussian distributions comprising a set of continuous random variables corresponding to each of the attributes contained in the historical click-through data… performing a joint Bayesian inference using the prior Gaussian distributions of the outer model in combination with the inner model and observations of user clicks in the current query session to learn posterior distributions for the current query session” wherein using Bayesian for the query-item pair]
Wakankar teaches generating type-ahead query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring and Chen teaches A "General Click Model" (GCM) is constructed using a Bayesian network that is inherently capable of modeling "tail queries" by building the model on multiple attribute values that are shared across queries. The two references are in the same field of endeavor as the claimed invention of managing click-through data. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine utilizing determining an expected click-through-rate (CTR) of a query-item pair of Wakankar in view of Wang with using Bayesian for the query-item pair of Chen 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, with the predictable results of optimizing cold start of ranking and surfacing new products.
Further, Wakankar teaches determining a score of the query-item pair with a second machine learning model by using the content-based features and the CE feature; [Wakankar, Abstract, Wakankar teaches “A machine learning model may be used to assist in assigning scores based on metrics on interaction in the search domain. Other embodiments are described and claimed” wherein the “assigning scores based on metrics on interaction in the search domain” is equivalent to “determining a score of the query-item pair” or expected click-through-rate CTR] and ranking the query-item pair based in part on the score and the expected CTR [Wakankar, claim 1, Wakankar teaches “ranking the query suggestion based on the score of each query suggestion;”].
Regarding claim 2. Wakankar in view of Wang and Chen teaches all of the limitations of claim 1 (as above). Wakankar in view of Chen does not specifically teach, however; Wang teaches further comprising: training the first machine learning model to determine the expected CTRs using the training objective of reducing the cross-entropy loss and using the condensed training data (see claim 1 rejection above).
Regarding claim 3. Wakankar in view of Wang and Chen teaches all of the limitations of claim 1 (as above). Further, Wakankar teaches further comprising training the second machine learning model to determine the score of the query-item pair [Wakankar, claim 1, Wakankar teaches “identifying at least one query suggestion corresponding to the type-ahead query candidate and having a suggestion score based on at least one of click-through-rate or context associated with global features of the search domain; scoring of the query suggestions, wherein a score of the query suggestion is based on both the suggestion score associated with the query suggestion and the confidence level of the corresponding type-ahead query candidate” wherein score of the query-item pair].
Regarding claim 11, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 11 is directed to a system which is anticipated by Wakankar claim 17.
Regarding claim 12, the claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise. Claim 2 is a method claim while claim 12 is directed to a system which is anticipated by Wakankar claim 17.
Regarding claim 13, the claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise. Claim 3 is a method claim while claim 13 is directed to a system which is anticipated by Wakankar claim 17.
Regarding claim 19, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 19 is directed to a non-transitory computer readable storage medium which is anticipated by Wakankar claim 9.
Regarding claim 20, the claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise. Claim 2 is a method claim while claim 20 is directed to a non-transitory computer readable storage medium which is anticipated by Wakankar claim 9.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being un-patentable over Wakankar et al. (US 20190171728 A1) in view of Wang and Chen, and in further view of Subramanian et al. (US 20250225558 A1, as supported by provisional 63619589)
Regarding claim 9. Wakankar in view of Wang and Chen teaches all of the limitations of claim 1 (as above). Wakankar in view of Wang and Chen does not specifically teach, however; Subramanian teaches wherein the content-based features comprise: a content quality score; price signals; a text match; and a brand match [Subramanian, para. 0050, Subramanian teaches scoring items, para 0017 teaches “client device 110 determines a weight for items that are priced by weight”, para. 0027 teaches “The NLP tasks include, but are not limited to, text generation, query processing”, and para. 0003 teaches brand tags]
Wakankar teaches generating type-ahead query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring and Subramanian teaches product query scoring. The two references are in the same field of endeavor as the claimed invention of managing click-through data. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine utilizing determining an expected click-through-rate (CTR) of a query-item pair of Wakankar and Wang and using Bayesian for the query-item pair of Chen with using the content-based features of Subramanian 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, with the predictable results of optimizing cold start of ranking and surfacing new products.
Regarding claim 18, the claim recites analogous limitations to claim 9 above, and is therefore rejected on the same premise. Claim 9 is a method claim while claim 18 is directed to a system which is anticipated by Wakankar claim 17.
Conclusion
Applicant's amendments and arguments dated 06/03/2026 necessitated the updating of the 35 USC § 101 and the 35 USC § 103 rejections of the pending claims presented in the present Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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