DETAILED ACTION
Status of Claims
In the response filed September 29, 2025, Applicant amended claims 1, 11, and 16. Claims 1-20 are pending in the current application.
Response to Arguments
Claims 1 and 11 were objected to because of the following informalities. Applicant thanks Applicant for amending the claims. The objection has been obviated.
Applicant's arguments with respect to the rejection under 35 U.S.C. 101 have been fully considered but they are not persuasive. First, Applicant asserts that the claims do not recite an abstract idea because the claims are directed to specific improvements through the machine learning model training and implementation. Examiner respectfully disagrees. The claims recited conventional machine learning models without specific improvements to the technology itself. In Recentive Analytics, Inc, the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. In this case, applying machine learning to optimizing offers to targeted user, an activity predating computers, did not transform the abstract idea into a patent-eligible invention. Simply applying generic machine learning techniques to persona segments/activity parameters without improving the underlying technology is insufficient for patent eligibility. Examiner notes that this response applies to Prong One (recites an abstract idea) and Prong Two (integrates judicial exception into practical application/specific improvement). Second, Applicant asserts the claims provide a computational solution to a computational problem because targeting each customer with personalized offers is complex and requires intensive computing resources including targeting experimental offers to evaluate against the performance of offers previously selected by user. Examiner respectfully disagrees. The claim does not offer an improvement in computer-related technology, and nothing in the claims suggest allowing a computer to perform a function that it was not capable of performing before. Rather, in the present claims, a computer is utilized for its well-known functionality of collecting, analyzing, and calculating, and outputting a result based on the collection and analysis. Last, Applicant asserts that, in step 2B, the claims recite significantly more than the judicial exception because the amended claims recite specific arrangement of a trained and retrained machine learning model. Examiner respectfully disagrees. The claims in the present application are not like the claims of Bascom. Bascom was found to be patent eligible because the claims required an arguably inventive distribution of functionality within a network (installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each user). In Bascom, the generic and conventional components, when combined, provided the “inventive concept” of installing a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each user, thus giving the filtering tool additional benefits (i.e. the claims of Bascom were found to be eligible in Step 2B). As stated above, simply applying generic machine learning techniques to persona segments/activity parameters without improving the underlying technology is insufficient for patent eligibility. The arrangement of the limitations in the claim of the present application do not suggest any additional benefits, nor does the arrangement require an arrangement that is non-conventional and non-generic. The rejection is maintained.
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 1-20 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-20 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., non-transitory computer readable storage medium and system) and process (i.e., a method).
Although claims 1-20 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-20 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claims 11 and 16) recite (in bold and italics):
by an offer selection optimizer server:
identifying a pool of targeted users for a given offer characterized by a plurality of offer parameters;
providing the pool of targeted users and the given offer to a machine learning model, the machine learning model being trained on a first plurality of feature vectors, each of the first plurality of feature vectors comprising dimensions corresponding to activity parameters of historical user activity of a user, the machine learning model being trained to output one or more persona segments grouping targeted users by the activity parameters;
receiving, from the machine learning model, a plurality of persona segments, each persona segment characterized by a plurality of activity parameters;
determining a plurality of initial weights, each initial weight of the plurality of initial weights associated with one of the plurality of offer parameters, each initial weight being determined based on a given persona segment of the plurality of persona segments;
determining, using a controlled trajectory model, for each of the plurality of initial weights, a rate of modification based on the offer parameter associated with that initial weight, the controlled trajectory model trained on performance data of user activity, weights, and offer outcomes;
generating a plurality of exploration weights, each exploration weight of the plurality of exploration weights associated with one of the plurality of offer parameters, wherein the generating is based on applying, to the plurality of initial weights, the rate of modification;
generating a plurality of exploration offers based on the plurality of exploration weights by applying the plurality of exploration weights to the plurality of offer parameters;
transmitting, over the network, the plurality of exploration offers to a first plurality of client devices, the first plurality of client devices corresponding to a first subset of the given persona segment;
transmitting, over the network, the given offer to a second plurality of client devices, the second plurality of client devices corresponding to a second subset of the given persona segment;
receiving, from the first plurality of client devices, a first plurality of tracked activities of the first subset associated with the plurality of exploration offers;
receiving, from the second plurality of client devices, a second plurality of tracked activities of the second subset associated with the given offer;
generating a plurality of updated weights by updating the plurality of initial weights based on the first plurality of tracked activities and the second plurality of tracked activities;
calculating a first score for each of the plurality of offer parameters, the first score being a product of a value of the offer parameter and one of the plurality of updated weights associated with the offer parameter and the persona segment;
calculating a segment offer score by summing the plurality of first scores;
selecting a subsequent offer for the given persona segment having a highest segment offer score;
causing the subsequent offer to be presented to users in the given persona segment on the first plurality and second plurality of client devices;
receiving, from the first plurality and second plurality of client devices, user activity of the given persona segment in response to the subsequent offer;
generating a second plurality of feature vectors, each one of the second plurality of feature vector comprising dimensions corresponding to activity parameters of user activity of one of the users of the given persona segment; and
providing, to the machine learning model, the second plurality of feature vectors of the given persona segment to retrain the machine learning model
The identified limitations recite presenting personalized offers based on calculating scores and weighted parameters, which is a method of commercial or legal interactions including contracts, advertising, marketing, or sales activities or behaviors, and business relations. The claim limitations fall within the Certain Methods of Organizing Human Activities groupings of abstract ideas. The performance of the claim limitations using generic computing components i.e., server, processor, client devices, and machine learning model (interpreted as a computer program/environment), does not preclude the claim limitations from being in the certain methods of organizing human activity grouping. Thus, the claimed invention recites an abstract idea.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
As a whole, the additional elements recite using servers, processors, and client devices of a computing system to present personalized offers based on calculating scores and weighted parameters. This judicial exception is not integrated into a practical application. The claims as a whole merely describe how to generally apply the concept of combining and sending the combined queries. The server and processor in the steps are recited at a high-level of generality, i.e., as a generic server, processor, a client device, and machine learning model (interpreted as a computer program/environment), performing a generic computer function of presenting personalized offers based on calculating scores and weighted parameters, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Dependent claims 2-10, 12-15, and 17-20, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. For instance, the dependent claims recite identifying and segmenting a pool of content items, determining a preferred content item, accessing candidate offers based on historical offers, and ranking offers. These limitations are directed to commercial activity or advertising.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". As stated above, the claims as a whole merely describe how to generally apply the concept of combining and sending the combined queries. The server, processor, client devices, and machine learning model (interpreted as a computer program/environment) in the steps are recited at a high-level of generality (i.e., as a generic server and processor performing a generic computer function of presenting personalized offers based on calculating scores and weighted parameters) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-20, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-10, 12-15, and 17-20, when analyzed as a whole are held, to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
AUTHOR(S): Fernanda Title: Optimizing audience segmentation Journal: Navarro [online]. Publication date: 2016.[retrieved on:02/07/2025]. Retrieved from the Internet: <https://works.hcommons.org/records/yjnyq-dh470/files/Navarro+laura+2016.pdf> (Year: 2016)
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685