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 .
Status of the Application
This final office action is in response to the communication filed on 5/11/2026. Claim 12 has been cancelled. Claims 1, 13, 14, 25, and 26 have been amended. Claims 1-12 and 13-26 are currently pending and have been examined below.
Claim Rejections – 35 U.S.C. 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 1-11 and 13-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, Claim 1 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
accessing user-specific data vectors on a plurality of users, wherein the user-specific data vectors include shopping history and video consumption behavior;
developing, a plurality of clusters based on the user-specific data vectors;
associating a user from the plurality of users with one or more clusters from the plurality of clusters;
identifying that the user, from the plurality of users, is viewing media content, wherein the user
is associated with the one or more clusters; and
enabling a purchase of a product for sale to the user, wherein the product for sale is
relevant to the one or more clusters.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations above describe a process of grouping/clustering users based on shopping/consumption history, advertising a product relevant to the cluster and available for purchase to a content viewing user, and enabling the purchase of the product. This is both an advertising activity (i.e., advertising a product based on user history) and a sales activity (i.e., enabling the purchase of the advertised product). Accordingly claim 1 recites an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of:
[developing the clusters] using one or more processors;
inserting a container unit into the media content that is being viewed by the user;
populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying;
[enabling an] ecommerce [purchase of the product];
[the product relevant to] the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window.
wherein the developing comprises continuously training and deploying a machine learning model to develop the plurality of clusters, wherein the training is based on scoring a decision of the machine learning model using actual information obtained via a data exchange;
The additional limitations when viewed individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically:
The recitation of one or more processors to develop the clusters merely generally links the abstract idea to a particular technological environment or merely utilizes a computer as a tool to perform the abstract idea.
Additionally, the limitations inserting a container unit into the media content that is being viewed by the user and populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying are recited at a high level of generality. Paragraph [0033] of Applicant’s specification recites that “[t]he container unit is inserted in a region of an electronic display executing an application and/or browser” and “container unit comprises a graphics region allocated for representation of short-form videos. The representation can include static and/or motion thumbnails, icons, hyperlinks, and/or other suitable representations.” Examiner notes the broadest reasonably interpretation of these limitations in view of Applicant’s specification encompasses displaying a video targeted to the user on any region of a generic user device display. At this level of generality these limitations merely generally link the abstract idea to a particular technological environment (i.e., a generic user device to play video) or merely utilizes a computer as a tool to perform the abstract idea.
Further, the limitation [enabling an] ecommerce [purchase of the product] is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (e-commerce) or merely utilizes a computer as a tool to perform the abstract idea.
Additionally, [the product relevant to] the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (e.g., a generic video playing app or browser) or merely utilizes a computer as a tool to perform the abstract idea.
Finally, with respect to the limitation “wherein the developing comprises continuously training and deploying a machine learning model to develop the plurality of clusters, wherein the training is based on scoring a decision of the machine learning model using actual information obtained via a data exchange”, Examiner respectfully notes that this limitation is recited at a high level merely invoking machine learning as a tool to perform the abstract idea. The recitation of continuous training of a generic machine learning model by comparing a decision of the model to real world information (e.g., comparing an inferred demographic to an actual demographic and positively/negatively scoring the decision based on the comparison) describes the high-level concept of training machine learning with a feedback loop; not an improvement that integrates the abstract idea into a practical application. Examiner adds that this conclusion is consistent with Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) which held that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement . . . Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Recentive further held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Id. at 1216. Accordingly, Examiner takes the position that similar to the claims in Recentive, the claimed continuous training of the model is, at most, the application of generic machine learning to a new data environment and therefore does not represent a technological improvement that integrates the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and only generally link the use of the judicial exception to a particular technological environment. The same analysis applies here in 2B, i.e., mere instructions to apply an exception in a particular technological environment cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, computer-readable medium claim 25 and system claim 26 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1. The additional limitations in claim 25 (i.e., a computer program product embodied in a non-transitory computer readable medium) and the additional limitations of claim 26 (i.e., a memory and one or more processors) add nothing of substance to the underlying abstract idea. The components are merely providing a particular technological environment to implement the abstract idea.
Dependent claims 2-11 and 13-24 are rejected on a similar rational to the claims upon which they depend. Specifically:
With regard to claims 2-11 and 13-24, the additional limitations only serve to further narrow the abstract idea or generally link the abstract idea to a particular technological environment and do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Response to Arguments
35 U.S.C. 103
Applicant's arguments, see pages 10-14, filed 5/11/2026 with respect to the rejection(s) of claims 1-11 and 13-26 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn.
35 U.S.C. 101
Applicant's arguments, see pages 7-10, filed 5/11/2026 with respect to the rejection(s) of claims 1-11 and 13-26 under 35 U.S.C. 101 have been fully considered but are not persuasive.
First, Applicant argues that:
The currently amended claim 1 is not directed to "certain methods of organizing human activity" because the core of the amended claim 1 now is drawn to a specific, processor-implemented machine-learning architecture that continuously trains and deploys a model based on scoring the model's own decisions against actual information obtained via a data exchange. This is not a mental process, nor a business practice, nor a human-organizing activity. The amendment introduces a technical feedback loop in which the system evaluates the correctness of its clustering decisions using real-world ground-truth data and uses those scores to continuously retrain and redeploy the model. Humans cannot perform continuous model retraining, model deployment, or vector-space clustering in real time, and these operations are not organizational rules or commercial interactions-they are computational improvements to the functioning of the machine-learning system itself As amended, amended claim 1 is directed to a specific technological solution that improves clustering accuracy through a processor-implemented continuous-training mechanism, and therefore falls outside the "certain methods of organizing human activity" category (remarks page 8).
Examiner respectfully disagrees. The claims recite a process of grouping/clustering users based on shopping/consumption history, advertising a product relevant to the cluster and available for purchase to a content viewing user, and enabling the purchase of the product. This is both an advertising activity (i.e., advertising a product based on user history) and a sales activity (i.e., enabling the purchase of the advertised product) (remarks page 8). With respect recitation of a “technical feedback loop” including continuous re-training of the model, Examiner notes that, at most, this is an additional limitation to be considered under Step 2A, prong 2 and Step 2B. Examiner respectfully notes that the amended continuous training limitation is recited at a high level merely invoking machine learning as a tool to perform the abstract idea. The recitation of continuous training of a generic machine learning model by comparing a decision of the model to real world information (e.g., comparing an inferred demographic to an actual demographic and positively/negatively scoring the decision based on the comparison) describes the high-level concept of training machine learning with a feedback loop; not an improvement that integrates the abstract idea into a practical application. Examiner adds that this conclusion is consistent with Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) which held that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement . . . Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Recentive further held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Id. at 1216. Accordingly, Examiner takes the position that similar to the claims in Recentive, the claimed continuous training of the model is, at most, the application of generic machine learning to a new data environment and therefore does not represent a technological improvement that integrates the abstract idea into a practical application.
Second, Applicant argues that:
Furthermore, under Step 2A, Prong Two, the amended claim 1 now integrates the alleged abstract idea into a specific practical application by reciting a processor-implemented machine-learning architecture that continuously trains and deploys a model based on scoring the model's own clustering decisions using actual information obtained via a data exchange. This continuous-training feedback loop is not a generic instruction to "apply it on a computer," nor is it a mere presentation of information. Instead, it is a technical control mechanism that improves the accuracy and operation of the clustering system itself by using real-world data to automatically refine the model's parameters and redeploy updated versions. The examiner's prior analysis, focused on generic processors, generic displays, and generic e-commerce, does not address this technical improvement in the currently amended claim 1. The currently amended claim 1 now requires a self-correcting ML pipeline that dynamically updates user-specific data vectors and cluster assignments based on scored model performance, thereby integrating the abstract idea into a technological process that improves the functioning of the underlying machine-learning system. Accordingly, as currently amended, claim 1 is no longer a generic implementation of organizing human activity, but instead recites a technologically rooted, processor-implemented improvement that satisfies Step 2A, Prong Two.
Examiner respectfully disagrees. With respect to the recitation of a “continuous-training feedback loop” Examiner respectfully disagrees that this limitation improves the functioning of the underlying machine-learning system. The recitation of continuous training of a generic machine learning model by comparing a decision of the model to real world information (e.g., comparing an inferred demographic to an actual demographic and positively/negatively scoring the decision based on the comparison) describes the high-level concept of training machine learning with a feedback loop; not an improvement that integrates the abstract idea into a practical application. Examiner adds that this conclusion is consistent with Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) which held that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement . . . Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Recentive further held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Id. at 1216. Accordingly, Examiner takes the position that similar to the claims in Recentive, the claimed continuous training of the model is, at most, the application of generic machine learning to a new data environment and therefore does not represent a technological improvement that integrates the abstract idea into a practical application.
Third, Applicant argues that:
Under Step 2B, the currently amended claim 1 now recites significantly more than the alleged abstract idea because it introduces a non-conventional, non-generic machine-learning architecture that continuously trains and redeploys the clustering model based on scoring the model's own decisions using actual information obtained via a data exchange. This is not a routine or conventional computer function. Conventional systems do not evaluate their own clustering outputs against real-world ground-truth data, generate performance scores, and then automatically retrain and redeploy updated models in a closed-loop pipeline. The examiner's Step 2B analysis, which focused on generic processors, generic displays, and generic e-commerce, does not address this newly added technical mechanism recited in the currently amended claim 1. The continuous-training feedback loop constitutes an inventive concept because it improves the accuracy and operation of the underlying machine-learning system itself, rather than merely using a computer as a tool. Courts have repeatedly recognized that claims reciting specific improvements to computer functionality, particularly improvements to model training, data processing, or system accuracy, provide the "significantly more" required at Step 2B. Here, the amendment adds precisely such a technical improvement: a processor-implemented, self-correcting ML pipeline that departs from conventional practice and therefore supplies the inventive concept into the currently amended claim 1 (remarks page 9).
Examiner respectfully disagrees. With respect to the recitation of a “continuous-training feedback loop” Examiner respectfully disagrees that this limitation constitutes an inventive concept because it improves the accuracy and operation of the underlying machine-learning system itself. The recitation of continuous training of a generic machine learning model by comparing a decision of the model to real world information (e.g., comparing an inferred demographic to an actual demographic and positively/negatively scoring the decision based on the comparison) describes the high-level concept of training machine learning with a feedback loop; not an improvement that integrates the abstract idea into a practical application. Examiner adds that this conclusion is consistent with Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) which held that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement . . . Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Recentive further held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Id. at 1216. Accordingly, Examiner takes the position that similar to the claims in Recentive, the claimed continuous training of the model is, at most, the application of generic machine learning to a new data environment and therefore does not represent a technological improvement that amounts to significantly more than the abstract idea.
Fourth, Applicant argues that:
Additionally, the applicant further notes the USPTO's August 4, 2025 memorandum titled Reminders on Evaluating Subject Matter Eligibility of Claims under 35 U.S.C. 101. As stated therein: "While an additional limitation (or combination) that merely applies the judicial exception on a generic computer may not render a claim eligible on its own, an additional limitation (or combination) that meaningfully limits the judicial exception can render it eligible." The present claims include multiple such limitations that meaningfully limit any alleged abstract idea. Finally, the memorandum points out: "In order to make a rejection of a claim under any of the statutory bases (i.e., 35 U.S.C. 101, 102, 103, 112), unpatentability must be established by a preponderance of the evidence." The applicant respectfully submits that no such evidentiary showing has been made in the current 101 rejection (remarks pages 9-10).
Examiner respectfully disagrees and replies that, as noted above, the recitation of continuous training of a generic machine learning model by comparing a decision of the model to real world information (e.g., comparing an inferred demographic to an actual demographic and positively/negatively scoring the decision based on the comparison) describes the high-level concept of training machine learning with a feedback loop; not an improvement that integrates the abstract idea into a practical application. Examiner notes that this conclusion is consistent with Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) which held that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement . . . Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Recentive further held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Id. at 1216. Accordingly, Examiner takes the position that similar to the claims in Recentive, the claimed continuous training of the model is, at most, the application of generic machine learning to a new data environment and therefore the preponderance of the evidence demonstrates the ineligibility of the claims under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication Number 20210326674 (“Liu”) discloses clustering users using a k-means clustering algorithm and determining a similarity between clustered users and relevant content
US Patent Application Publication Number 20210409800 (“Downing”) discloses when a video is playing a container can update product and service objects being shown playing and video be presented in a container that supports interaction and purchase without leaving the current environment
US Patent Application Publication Number 20230325630 (“Wu”) discloses weight parameters trained using historical transaction data can be used to update a user node vector representation
US Patent Application Publication Number 20220122161 (“Perera”) discloses causing product information to be displayed as a second overlay on a video, wherein a user can access the product information within the second overlay, and in response to receiving a user input for adding a specified product from the set of products to a shopping cart, add the specified product to the shopping cart within the second overlay; and generate a configuration file having the interactive layer for the video.”
US Patent Application Publication Number 20190075339 (“Smith”) discloses a process for a viewer to view video content augmented with targeted advertisements
US Patent Publication Number 11051067 (“Baxter”) discloses an in-video shopping system receiving a user request during playback of a video, to add at least one item displayed in an interactive shopping overlay to an electronic shopping cart and subsequently completely a checkout process from the shopping car from within the interactive shopping overlay
However, the prior art fails to teach each and every limitation as claimed, and would involve hindsight reasoning to arrive at the claimed invention. Therefore, the claims are considered allowable over the prior art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLAN J WOODWORTH, II whose telephone number is (571)272-6904. The examiner can normally be reached Mon-Fri 9:00-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
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