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 9/2/2026. Claims 1-4, 11-14, and 20 are currently pending and have been examined. Claims 7-10 & 17-20 have been newly cancelled; claims 5-10 and 15-20 now stand cancelled; claims 1, 11, and 20 have been amended. The claim objections have been overcome by amendment.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/2/2026 has been entered.
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 1-4, 11-14, and 20 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 1-4 are directed to a process, claims 11-14 are directed to an article of manufacture, and claim 20 is directed to a machine. Therefore, claims 1-4, 11-14, and 20 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 20 at least the following limitations that are believed to recite an abstract idea:
retrieving a set of user data for a user of a system, wherein the set of user data comprises information describing one or more interactions by the user with the system;
accessing a model trained to predict an exploration score for the user, wherein the exploration score describes a likelihood that the user will interact with content for which the user has less than a threshold measure of familiarity, and the model is trained by: receiving user data for a plurality of test users of the system, computing, for each test user of the plurality of test users, a label describing the exploration score for a corresponding user, wherein computing the label for a test user of the plurality of test users comprises:
Randomly selecting an initial exploration score for the test user,
Repeatedly adjusting the initial exploration score by:
Computing a measure of familiarity for each of a set of content items, wherein computing the measure of familiarity for a content item of the set of content items comprises computing a ratio of a number of previous interactions by the test user with the content item to a number of times the content item was previously presented to the test user;
Presenting the set of content items to the test user in a user display;
Receiving a user interaction with one of the set of content items; and
Incrementing or decrementing the initial exploration score based on whether the item of the user interaction has a measure of familiarity above a threshold value;
Selecting a final exploration score for the test user based on the repeated adjusting of the initial exploration score, and
Selecting the final exploration score as the label, and
training the model based at least in part on the user data and the label for each user of the plurality of users;
applying the model to predict the exploration score for the user based at least in part on the set of user data for the user;
receiving a request from the user to access a display comprising content recommended to the user;
identifying a set of candidate content to recommend to the user based at least in part on the exploration score for the user and information describing a set of previous interactions by the user with a plurality of items;
computing, for each candidate content item of the set of candidate content, a measure of familiarity of the candidate content item to the user as a ratio of a number of previous interactions by the user with the candidate content item to a number of times the candidate content item was previously presented to the user;
determining that the exploration score for the user is at least a threshold score;
responsive to determining that the exploration score for the user is at least the threshold score, ranking the set of candidate content such that a rank of a candidate content item of the set of candidate content is inversely proportional to the measure of familiarity of the candidate content item to the user;
selecting a set of content to recommend to the user from the ranked set of candidate content;
determining an arrangement of the selected set of content based on the ranking;
generating the display comprising the selected set of content arranged according to the arrangement, wherein the arrangement positions a first content presentation unit comprising the selected set of content above a second content presentation unit of the display; and
sending the display to the user, wherein sending the display causes the user to display the display.
The above limitations recite the concept of personalized recommendations. 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 1-4, 11-14, and 20 recite 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 computer system comprising a processor and a computer-readable medium
an online system
a machine learning model
a user interface
a client device
A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps
A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions
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 2-4, 12-14 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. 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 computer system comprising a processor and a computer-readable medium
an online system
a machine learning model
a user interface
a client device
A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps
A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions
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 1-4, 11-14, and 20 are allowable over prior art though rejected on other grounds (e.g. 101) as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1-4, 11-14, and 20 would be allowable over prior art if rewritten to overcome the rejections above 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 1-4, 7-14, 17-20 are allowable over prior art. The most related prior art patent of record include Zheng et al (US 20180157759 A1), hereinafter Zheng, Nath et al (US 20230342831 A1) hereinafter Nath, Frank et al (US 20160224803 A1), hereinafter Frank, Lu et al (US 20240265309 A1) hereinafter Lu, Khan (US 20250278645 A1), and Brown et al (US 20250285010 A1) hereinafter Brown.
Zheng discloses a system that receives user interaction data with media content [0049] and stores historical user interactions and user profile data [0067]. A trained machine learning model is used to determine the likelihood that a particular user will interact with a particular media item if presented [0030]. The model can be trained based on historical data [0045]. The system filters out media items already seen by the user to ensure recommendations are new to the user [0031]. The machine learning model considers user characteristics including the interactions of similar users [0045] The user engages with a GUI to request media content recommendations [0033], causing the system to use the ML model to select media items, and ranks them based on the probability of user interaction [0031]. The top-ranked items are then presented to the user [0047].
Nath teaches a machine-learning recommendation system [Abstract] in which historical data of a plurality of customers and products is used to train an ML model [0034], including transaction data reflecting user satisfaction/preference with product interactions [0041] The training data includes labels associated corresponding to the historical data, such as an indication of a correct recommendation to be inferred based on ground truth [0034]. The model is iteratively trained on imputed examples from the training data, causing weights or biases to be adjusted in response to the difference between inferred outputs of the model and the ground truth associated with the training data’s label [0035].
Frank teaches recommendation systems using machine learning [0896], in which the ML model can make suggestions specific to a user based on scores associated with similar users’ historical data [0865], including that the ML model starts with random initial values that are iteratively improved as the ML model iterates during the training process to accurately output scores associated with the training examples [01290].
Lu teaches an AI item recommendation technique [Abstract] in which historical interaction data with an item is obtained [0109] and used by a trained ML model that maps relationships between items [0120]. The system retains the recommendation model based on new historical user interaction data [0219], with the training iteratively adjusting model parameters to minimize loss in the training data sample [0239-0244]. Nodes of the model are randomly initiated to select parameters for the training [0155]. The trained model outputs icons that are most likely to be interacted with, with the most likely items ranked most highly [0218].
Khan teaches an AI-driven product recommendation model that uses an ML knowledge graph [Abstract] and continuously retrains based on user feedback and interactions [0015], relying on historical interactions to determine user preferences and training the model using labeled input data to predict user interest in products [0032].
Brown teaches item recommendations using trained machine learning models [Abstract], wherein the model is trained based on labeled training data corresponding to historical user interest features in items [0025] The model is retrained based on updated training data comprising new user interactions to iteratively improve the accuracy of ML results [0027]. The ML model outputs predictions indicating confidence in whether items should be recommended to a user [0048].
However, each of these limitations fail to disclose or render obvious at least the limitations that: the machine-learning model is trained by: receiving user data for a plurality of test users of the online system, computing, for each test user of the plurality of test users, a label describing the exploration score for a corresponding user, wherein computing the label for a test user of the plurality of test users comprises: randomly selecting an initial exploration score for the test user, repeatedly adjusting the initial exploration score by: computing a measure of familiarity for each of a set of content items, wherein computing the measure of familiarity for a content item of the set of content items comprises computing a ratio of a number of previous interactions by the test user with the content item to a number of times the content item was previously presented to the test user; presenting the set of content items to the test user in a user interface; receiving a user interaction with one of the set of content items; and incrementing or decrementing the initial exploration score based on whether the item of the user interaction has a measure of familiarity above a threshold value; and selecting a final exploration score for the test user based on the repeated adjusting of the initial exploration score, and selecting the final exploration score as the label, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users.
Each of these references fail to disclose or render obvious the combination of limitations in the independent claims 1, 11, or 20, alone or in obvious combination. Therefore, at least for the combination of elements recited in the independent claims, the independent claims and those that depend thereon are allowable over prior art if rewritten to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments filed 9/2/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that the claims recite an improvement to “the technical field of computing quantities from user interaction logs to train a supervised machine-learning model.” Applicant argues that “the technical problem addressed in that a quantity derived from a system’s own interaction logs confounds a user’s response with the system’s presentation history,” such that “a raw interaction count reflects what the system displayed as much as what the user chose,” such that the process would “track which items the system surfaced rather than how the user responded.” Applicant argues that the claims address this problem “by computing familiarity as a normalized ratio at both ends of the pipeline: inside the label loop for each test user, and at request time for each candidate content item. Specifically, the system computes each content item’s measure of familiarity as a ratio of the user’s previous interactions with the item to the number of times the item was previously presented to that user.” Applicant argues that this “makes both the training label and the rank a function of the user’s response rate rather than of the system’s display history,” and reiterates that “an unnormalized count cannot separate the user’s choice from the system’s display decisions. Reciting the identical ratio on the training side and the request side puts the quantity the model was trained against and the quantity the ranking consumes on one scale, which is what lets a score learned from a set of test users govern an ordering computed for a different user.”
Examiner disagrees. With reference to the rejection above, the argued functionality of computing a ratio of user responses to times an item was presented is part of the abstract idea itself. Alleged improvements to accuracy stemming from determining user interaction rates from actual user interactions instead of opportunities for interactions, in order to calculate user familiarity, are at best business improvements rooted solely in this abstract idea. The claims recite a system that performs this calculation for each iteration, with previous/test iterations being used to refine the performance of the system. Preparing/training the method by performing the same steps/calculations that will be performed when the method is executed is similarly part of the abstract idea. The additional elements, rather than integrating this abstract idea into a practical application by improving a technical field, are invoked as mere instructions to apply the abstract idea, including the calculation and application of the ration, to a technological environment [MPEP 2106.05(f)].
Applicant further argues that the claims provide an improvement to “to generate interface layouts in an online content-serving system,” stating that “a fixed interface layout applies one ordering rule and one element position to every request, so it cannot express two opposite orderings of the same candidate set for users at opposite ends of the exploration-score range.” Applicant argues that “the claims further address the fixed-layout limitation by generating both the ordering and the interface layout from the normalized quantity at request time, …so that the interface the system emits differs structurally between requests without a second layout being stored.”
Examiner disagrees. With reference to the rejection above, the argued functionality of ordering content on a display, in a way specific to each user based on their exploration score, is part of the abstract idea, such that any alleged improvement from personalizing the layout of recommended content by ranking items based on a user-specific score is at most a business improvement rooted solely in the abstract idea. The additional elements, such as the method being performed by a computer and the display being a UI, rather than integrating this abstract idea into a practical application, are invoked as mere instructions to apply the abstract idea, including the calculation and application of the ration, to a technological environment [MPEP 2106.05(f)].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J SULLIVAN whose telephone number is (571)272-9736. The examiner can normally be reached Mon - Fri 8-5 PT.
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, Marissa Thein can be reached on (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.J.S./
Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
Prosecution Insights