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 .
Status of Claims
This Office Action is in response to the communication filed on 09/05/2025.
Claim 1 has been previously cancelled.
Claims 3 and 14 have been cancelled.
Claims 2, 6, 13 and 17 have been amended.
6. Claims 2, 4-13 and 15-23 are currently pending and are considered below.
Information Disclosure Statement
7. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 2, 4-13 and 15-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 2, recites a method, which is a statutory class, executed by user device, input/output circuitry and machine learning model: the method, comprising:
receiving, from a user device, a request for media content, wherein the request is associated with a user profile;
based on receiving the request for the media content:
identifying a timepoint in the media content to add supplemental content;
accessing a wish list associated with the user profile and a second user profile;
accessing a calendar associated with the user profile;
providing the wish list and the calendar to a machine learning (ML) model,
wherein the machine learning model is trained to match textual description of the wish list to events in the calendar;
correlating, using the trained machine learning model, the wish list with an event in the calendar; and
receiving, from the ML model, ML output comprising a selection of an item from the wish list and a selection of the event in the calendar; and
based on the ML output, providing, for display via a user interface at the user device, (a) the supplemental content at the timepoint in the media content, wherein the supplemental content is based on the selected item in the wish list and (b) a countdown timer.
The steps of
receiving, from a user device, a request for media content, wherein the request is associated with a user profile;
based on receiving the request for the media content:
identifying a timepoint in the media content to add supplemental content;
accessing a wish list associated with the user profile and a second user profile;
accessing a calendar associated with the user profile;
providing the wish list and the calendar to a machine learning (ML) model,
wherein the machine learning model is trained to match textual description of the wish list to events in the calendar;
correlating, using the trained machine learning model, the wish list with an event in the calendar; and
receiving, from the ML model, ML output comprising a selection of an item from the wish list and a selection of the event in the calendar; and
based on the ML output, providing, for display via a user interface at the user device, (a) the supplemental content at the timepoint in the media content, wherein the supplemental content is based on the selected item in the wish list and (b) a countdown timer,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for generating an advertisement for output based on an item on a wish list. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to providing data associated with the person.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as tailored personalized marketing, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a user device, input/output circuitry and machine learning model. The user device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions of receiving, a request for media content; identifying a timepoint in the media content; accessing a wish list; accessing a calendar; providing the wish list and the calendar to a machine learning (ML) model; and receiving, ML output; and providing, (a) the supplemental content and (b) a countdown timer and correlating the wish list) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation providing the wish list and the calendar to a machine learning (ML) model; and receiving, from the ML model, ML output comprising a selection of an item from the wish list and a selection of an event in the calendar, this features are considered math, and therefore is a part of the abstract idea. Because the machine learning model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a user device, input/output circuitry and machine learning model amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); providing a user with tailored information like advertisements based on information known about the user such as a location, address, or personal characteristics and a time of day is a fundamental practice long prevalent in our system); In re Morsa, 809 F. App’x 913, 917 (Fed. Cir. 2020). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. That is, a user device, input/output circuitry and machine learning model, performing commercial interactions including: receiving, a request for media content; identifying a timepoint in the media content; accessing a wish list; accessing a calendar; providing the wish list and the calendar to a machine learning (ML) model; and receiving, ML output; and providing, (a) the supplemental content and (b) a countdown time, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, claims 2 and 13 are not eligible.
As for dependent claims 4-12 and 15-23, these claims recite limitations that further define the same abstract idea noted in claims 2 and 13. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 2, 4-13 and 15-23 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
10. Applicant's arguments filed on 09/05/2025 with respect to the rejection of claims 2, 4-13 and 15-23 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
11. Applicant argued that “…Referring to Example 39 of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG, issued January 7, 2019), use of a trained machine learning model within a specific combination of steps is eligible patent subject matter that does not recite a judicial exception, such as an abstract idea of organizing human activity (e.g., a fundamental economic concept or commercial and legal interactions). (2019 PEG, page 7).
In Example 39, a method is described for using a trained neural network to detect human faces such that false positives from classifying non-facial images are minimized. The claim of Example 39 was found eligible because its steps were found to not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people, and therefore does not recite a judicial exception…” Remarks pages 8-9
12. The examiner notes that the claims in Example 39 is directed to "creating a
first training set comprising the collected set of digital facial images, the modified set of
digital facial images, and a set of digital non- facial images; training the neural network
in a first stage using the first training set; creating a second training set for a second
stage of training comprising the first training set and digital non-facial images that are
incorrectly detected as facial images after the first stage of training; and training the
neural network in a second stage using the second training set". The instant claim generates advertisement for output based on an item on a wish list. The computer has not been improved rather it uses the generic computer and a high level machine learning model to execute the abstract idea. As such, the rejection of claims 2, 4-13 and 15-23 under 35 U.S.C. 101 are maintained.
Conclusion
13. The prior art of record Maycotte (U.S. Pub. No. 2015/0088635) and Dixon (U.S. pub. No. 2015/0348095) does not expressly teach in response to (a) the receiving the request to obtain media content and (b) the correlating by the trained machine learning model, generating, a reminder for the item on the wish list, the reminder comprising a countdown timer counting from a current date to the event.
14. Updated search for prior art found are:
15. Rapaport et al. (U.S. Pub. No. 2012/0290950) teaches the deal counter indicates how many nearby neighbors have also signed up for the neighborhood group discount (and/or other promotional offering) before the offer deadline lapses. Next to the sign-up count there is a countdown timer decrementing from 30 minutes towards zero. Soon the required minimum number of acceptances is reached, well before the countdown timer reaches zero, but does not specifically teach a countdown timer counting from a current event in the calendar (see at least paragraphs 0023-0025).
16. Carrigan et al. (U.S. Pub. No. 2016/0357355) teaches a posting user can include a countdown timer in a post. To define a countdown timer, a user interface can be provided via which the user can specify a “target” time where the countdown ends. Whenever the post is rendered (e.g., as described below), the rendering process can use the target time and the current time to render a countdown image (which can be a dynamic image that updates while being displayed to reflect the passage of time). Thus, for example, an artist can create a post announcing an upcoming album release (or other future event), and users viewing the post can see the time remaining until the release, but does not explicitly teach a countdown timer counting from a current event in the calendar (see at least paragraph 0091).
17. 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 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.
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached at 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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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 12/27/2025