DETAILED ACTION
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 communications filed on June 17, 2026. The Applicant’s Amendment and Request for Reconsideration has been received and entered.
Claims 1-3, 5-7, and 9-20 are currently pending. Claims 10-20 have been withdrawn in response to the restriction requirement. Claims 1-3, 6, and 9 have been amended. Claims 4 and 8 have been cancelled. Claims 1-3, 5-7, and 9 have been examined in this application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 17, 2026 has been entered.
Response to Arguments
Applicant’s amendments necessitated the new grounds of rejection.
Regarding the rejection of claims 1-3, 5-7, and 9 under 35 USC 101, Applicant’s arguments have been fully considered but they are not persuasive for the reasons set forth infra.
Additionally, the Examiner respectfully argues that the claims recite, inter alia, receiving heterogeneous historical listing data including specific user interaction data reflecting interactions between users and items, generating item interaction data that captures relationships between items using the heterogeneous historical listing data, updating and modifying allocation of items and search ranking rules, receiving feedback reflecting user interactions following implementation, and updating to optimize item distribution and search ranking rules – which are commercial interactions including advertising, marketing, and sales activities/behaviors. Indeed, the purpose of these item distribution and search ranking rules is to “enable users to access item information and otherwise interact with the listing platform, for instance, to purchase, rent, download, or stream items” and “to facilitate users finding items on the listing platform.” (App. Spec. [0001]).
The Examiner respectfully argues that updating storage of item listing by removing listings and implementing modified search ranking rules are generic computer functions performed by generic servers. The recited improvement in search result relevance and reductions in storage consumption across the plurality of platforms and in execution of query operations, network transmissions, and associated input/output operations across the plurality of platforms are merely intended results of implementing the abstract idea and do not integrate the judicial exception into a practical application. Further, the reductions in storage and in execution of query operations, network transmissions, and associated input/output operations across the plurality of platforms are merely inherent, standard consequences of these generic computer functions, and thus not improvements to these technologies.
The Examiner respectfully notes that these claims are not analogous to Ex Parte Desjardins, where the claimed invention improved the operation of a machine learning system, such as by enhancing its training efficiency or preserving prior learning. Conversely, the present claims do not amount to an improvement “in training the machine learning model itself.”
Regarding Applicant’s assertions that amended claim 1 does not merely “use AI” at a high level, the Examiner respectfully argues that the claims recite “using a machine learning model trained to model pairwise interactions between items across different listing platforms” but do not actively recite training the machine learning model or how it is trained, and rather just that a machine learning model is used. Similarly, regarding the reinforcement learning agent, the claims recite the reinforcement learning agent to iteratively learn “a function” without further specifying what the function is. As recited, the machine learning model and reinforcement learning agent are merely tools for implementing the abstract idea of optimizing item allocation and searching ranking rules.
For all the reasons set forth, the previous rejections of claims 1-3, 5-7, and 9 under 35 USC 101 are maintained.
The previous rejection of claims 1-3, 5-7, and 9 under 35 USC 103 has been withdrawn in view of Applicant’s amendments.
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, 5-7, and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1. 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 2A – Prong One. If the claims fall within one of the statutory categories, it must then be determined whether the claims recite an abstract idea, law of nature, or natural phenomenon.
Step 2A – Prong Two. If the claims recite an abstract idea, law of nature, or natural phenomenon, it must then be determined whether the claims recite additional elements that integrate the judicial exception into a practical application. If the claims do not recite additional elements that integrate the judicial exception into a practical application, then the claims are directed to a judicial exception.
Step 2B. If the claims are directed to a judicial exception, it must be evaluated whether the claims recite additional elements that amount to an inventive concept (i.e. “significantly more”) than the recited judicial exception.
In the instant case, claims 1-3, 5-7, and 9 are directed to a manufacture. It is noted that claims 1-3, 5-7, and 9 recite “one or more computer storage media” and Applicant’s specification explicitly disclose “[c]omputer storage media does not comprise signals per se.” (App. Spec. [0054])
A claim “recites” an abstract idea if there are identifiable limitations that fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. In the instant case, claim 1 recites the steps of:
receiving, by at least one of one or more servers of the centralized item distribution and ranking system from servers of a plurality of listing platforms, heterogeneous historical listing data including platform-specific user interaction data reflecting interactions between users and items at each listing platform, wherein the one or more servers of the centralized item distribution and ranking system are distinct from and remote from the servers of the plurality of listing platforms;
generating, by at least one of one or more servers of the centralized item distribution and ranking system using a machine learning model trained to model pairwise interactions between items across different listing platforms, item interaction data that captures cross-platform relationships between items using the heterogeneous historical listing data;
initializing, by at least one of the one or more servers of the centralized item distribution and ranking system, a reinforcement learning agent using the item interaction data to define (i) an initial function for selecting actions, (ii) an initial cross-platform distribution of items across the plurality of listing platforms, and (iii) an initial set of platform-specific search ranking rules for the plurality of listing platforms; and
deploying, by at least one of the one or more servers of the centralized item distribution and ranking system, the reinforcement learning agent to, over a plurality of epochs, iteratively learn a function and update allocation of items listed at each of the plurality of listing platforms and platform-specific search ranking rules for each of the plurality of listing platforms by, at each epoch from the plurality of epochs:
selecting, by at least one of the one or more servers of the centralized item distribution and ranking system and based on a current state comprising a cross-platform distribution of items and platform-specific search ranking rules, an action that: (a) modifies allocation of items listed at each of the plurality of listing platforms, and (b) modifies the platform-specific search ranking rules for each of the plurality of listing platforms;
implementing the action by transmitting control instructions from the centralized item distribution and ranking system to the servers of the plurality of listing platforms, the control instructions causing the servers of the plurality of listing platforms to: (i) update storage of item listings based on the allocation of items by removing item listings and thereby reducing storage consumption across the plurality of listing platforms, and (ii) implement the platform-specific search ranking rules to improve search result relevance and thereby reduce execution of query operations, network transmissions, and associated input/output operations across the plurality of listing platforms;
receiving feedback data reflecting resulting user interactions at the plurality of listing platforms following implementation of the action; and
updating the function based on a reward derived from the feedback data, such that the function is optimized to jointly control both (i) the cross-platform distribution, and (ii) the platform-specific search ranking rules as a unified optimization process.
These underlined portions of the claim limitations set forth certain methods of organizing human activity, particularly commercial interactions including advertising, marketing, and sales activities/behaviors.
Additionally, these steps set forth mental processes, particularly concepts performed in the human mind, including, inter alia, the observation and evaluation of information.
Further, the limitations of the claims are not indicative of integration into a practical application. Taking the independent claim elements separately, the additional elements of performing the steps by at least one of one or more servers of the centralized system from servers of a plurality of platforms, wherein the one or more servers of the centralized system are distinct from and remote from the servers of the plurality of platforms; by transmitting control instructions from the centralized system to the servers of the plurality of platforms, the control instructions causing the servers of the plurality of listing platforms to perform actions; and via platforms, a machine learning model trained, a reinforcement learning agent, and storage merely implement the abstract idea on a computer environment. The recited improvement in search result relevance and reductions in storage consumption across the plurality of platforms and in execution of query operations, network transmissions, and associated input/output operations across the plurality of platforms are merely intended results of implementing the abstract idea and do not integrate the judicial exception into a practical application. Additionally, taking the dependent claim elements separately, the additional elements of performing the steps using a Markov decision process also merely implement the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
Thus, claims 1-3, 5-7, and 9 are directed to an abstract idea.
Regarding the claims, the technical elements of performing the steps by at least one of one or more servers of the centralized system from servers of a plurality of platforms, wherein the one or more servers of the centralized system are distinct from and remote from the servers of the plurality of platforms; by transmitting control instructions from the centralized system to the servers of the plurality of platforms, the control instructions causing the servers of the plurality of listing platforms to perform actions; and via platforms and storage merely implement the abstract idea on a computer environment. Additionally, the Examiner notes that while the claims recite a machine learning model, a reinforcement learning agent, and a Markov decision process these limitations are recited at a high level of generality and thus does not amount to significantly more. The recited improvement in search result relevance and reductions in storage consumption across the plurality of platforms and in execution of query operations, network transmissions, and associated input/output operations across the plurality of platforms are merely intended results of implementing the abstract idea and thus also do not amount to significantly more.
When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1-3, 5-7, and 9 are rejected as ineligible for patenting under 35 USC 101 based upon the same rationale.
Allowable Subject Matter
Claims 1-3, 5-7, and 9 are rejected under 35 U.S.C. 101, but would be allowable if this rejection were overcome. The following is a statement of reasons for the indication of allowable subject matter:
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the features of applicant's invention as the features amount to more than a predictable use of elements in the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hu, Yujing, et al. 2018. Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 368–377. -- reinforcement learning to rank in e-commerce search engine.
Burhani (US PGP 2019/0370649) -- reward for the reinforcement learning neural network reflecting a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.
Zhuang (US Pat No 8,744,978) – user customized ranking criteria.
Hu, Yujing, et al. "Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application." Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018. -- using reinforcement learning to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
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/JENNIFER V LEE/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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