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 .
Detailed Action
This Office Action is made in reply to Application 19/449,853 filed 15 January 2026. As originally filed, Claims 1 – 20 are presented for examination.
Compact Prosecution
For the purpose of compact prosecution, the examiner contacted Todd Hopfinger, the attorney of record, to request a Terminal Disclaimer which would place the application in condition for allowance. Mr. Hopfinger indicated that a request was sent to the Applicant and a response was not received.
Claim Objections
Claims 1, 9 and 16 are objected to because of the following informalities:
• “when the specific content asset relates a specified content category” in Claim 1 line 16 should apparently be –when the specific content asset relates to a specified content category--;
• “when the specific content asset relates a specified content category” in Claim 9 lines 19 - 20 should apparently be –when the specific content asset relates to a specified content category--;
• “when the specific content asset relates a specified content category” in Claim 16 line 17 should apparently be –when the specific content asset relates to a specified content category--. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 20 of U.S. Patent No. 12,563,270 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because instant claim 1 is anticipated by the conflicting patented claim 1 as shown in the table below. The difference between the instant examined claim and the conflicting patented claim is that the conflicting patented claim is narrower in scope and falls within the scope of the examined claim. Thus, the species or sub-genus claimed in the conflicting patent anticipates the examined claimed genus. Therefore, a patent to the examined claim genus would improperly extend the right to exclude granted by a patent to the species or sub-genus should the genus issue as a patent after the species or sub-genus. See MPEP §804(II)(B)(1).
19/449,853
US Patent 12,563,270 B2
1. A computer-implemented method for creating dynamic banners, the method comprising:
generating, by at least one processor and based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting a user's interests;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
selecting, based on the trained machine learning model, a specific content asset from the first subset of content assets or the second subset of content assets, wherein the selecting of the content asset is based on the trained machine learning model being trained to weight content asset subset generation based on when the specific content asset relates a specified content category;
receiving, at a media device, the content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
1. A computer-implemented method for creating dynamic banners, the method comprising:
generating, by at least one computer processor and based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting interests of a user;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the interests of the user and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
generating, based on the trained machine learning model, a third call to the content recommendation system for a third subset of content assets for the target banner template, wherein the third subset of the content assets includes a third number of content selections unrelated to the interests of the user and absent from either the first subset or the second subset of content assets;
determining, based on the first number, the second number, and the third number, a collective number of the first subset of content assets, the second subset of content assets, and the third subset of content assets;
selecting, based on an exploration strategy, a specific content asset from the collective number of the content assets, wherein the exploration strategy selects the specific content asset based on a frequency of occurrence of each of the first number, the second number, and the third number relative to the collective number;
receiving, at a media device, the specific content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
2. The method of claim 1, wherein the composite banner comprises an endemic banner.
2. The method of claim 1, wherein the composite banner comprises an endemic banner.
3. The method of claim 1, wherein the media device comprises an Over-the-Top (OTT) device.
3. The method of claim 1, wherein the media device comprises an Over-the-Top (OTT) device.
4. The method of claim 1, wherein the first subset of content assets comprises the content most closely matching historical interactions of the user.
4. The method of claim 1, wherein the first subset of content assets comprises content most closely matching historical interactions of the user.
5. The method of claim 1, wherein the first subset of content assets comprises popular current content.
5. The method of claim 1, wherein the first subset of content assets comprises popular current content.
6. The method of claim 1, wherein the first subset of content assets comprises highest rated current content.
6. The method of claim 1, wherein the first subset of content assets comprises highest rated current content.
7. The method of claim 1, wherein the trained machine learning model comprises an exploratory component to select the second subset of content assets.
7. The method of claim 1, wherein the trained machine learning model comprises an exploratory component to select the third subset of content assets.
8. The method of claim 1, wherein the target banner template is selected based on any of: opening an application (App);
executing a first-time view;
subscribing to a service;
resumption of watching targeted content; completion of watching targeted content; or completion of watching a sponsorship program.
8. The method of claim 1, wherein the target banner template is selected based on any of:
opening an application (App);
executing a first-time view;
subscribing to a service;
resumption of watching targeted content;
completion of watching targeted content; or
completion of watching a sponsorship program.
9. A system, comprising:
a memory; and
at least one processor coupled to the memory and configured to perform operations comprising:
generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting a user's interests;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
selecting, based on the trained machine learning model, a specific content asset from the first subset of content assets or the second subset of content assets, wherein the selecting of the content asset is based on the trained machine learning model being trained to weight content asset subset generation based on when the specific content asset relates a specified content category;
receiving, at a media device, the content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
9. A system, comprising:
a memory; and
at least one processor coupled to the memory and configured to perform operations comprising:
generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting interests of a user;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the interests of the user and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
generating, based on the trained machine learning model, a third call to the content recommendation system for a third subset of content assets for the target banner template, wherein the third subset of the content assets includes a third number of content selections unrelated to the interests of the user and absent from either the first subset or the second subset of content assets;
determining, based on the first number, the second number, and the third number, a collective number of the first subset of content assets, the second subset of content assets, and the third subset of content assets;
selecting, based on an exploration strategy, a specific content asset from the collective number of the content assets, wherein the exploration strategy selects the specific content asset based on a frequency of occurrence of each of the first number, the second number, and the third number relative to the collective number;
receiving, at a media device, the specific content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
10. The system of claim 9, where the composite banner comprises an endemic banner.
10. The system of claim 9, where the composite banner comprises an endemic banner.
11. The system of claim 9, where the system comprises a streaming media device platform for an Over-the-Top (OTT) device.
11. The system of claim 9, where the system comprises a streaming media device platform for an Over-the-Top (OTT) device.
12. The system of claim 9, wherein the first subset of content assets comprises the content most closely matching historical interactions of the user.
12. The system of claim 9, wherein the first subset of content assets comprises content most closely matching historical interactions of the user.
13. The system of claim 9, wherein the first subset of content assets comprises popular current content.
13. The system of claim 9, wherein the first subset of content assets comprises popular current content.
14. The system of claim 9, wherein the first subset of content assets comprises highest rated current content.
14. The system of claim 9, wherein the first subset of content assets comprises highest rated current content.
15. The system of claim 9, wherein the trained machine learning model comprises an exploratory component to select the second subset of content assets.
15. The system of claim 9, wherein the trained machine learning model comprises an exploratory component to select the third subset of content assets.
16. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting a user's interests;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
selecting, based on the trained machine learning model, a specific content asset from the first subset of content assets or the second subset of content assets, wherein the selecting of the content asset is based on the trained machine learning model being trained to weight content asset subset generation based on when the specific content asset relates a specified content category;
receiving, at a media device, the content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
16. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting interests of a user;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the interests of the user and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
generating, based on the trained machine learning model, a third call to the content recommendation system for a third subset of content assets for the target banner template, wherein the third subset of the content assets includes a third number of content selections unrelated to the interests of the user and absent from either the first subset or the second subset of content assets;
determining, based on the first number, the second number, and the third number, a collective number of the first subset of content assets, the second subset of content assets, and the third subset of content assets;
selecting, based on an exploration strategy, a specific content asset from the collective number of the content assets, wherein the exploration strategy selects the specific content asset based on a frequency of occurrence of each of the first number, the second number, and the third number relative to the collective number;
receiving, at a media device, the specific content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device.
17. The non-transitory computer-readable medium of claim 16, wherein the composite banner comprises an endemic banner and the at least one computing device comprises an Over-the-Top (OTT) device.
17. The non-transitory computer-readable medium of claim 16, wherein the composite banner comprises an endemic banner and the at least one computing device comprises an Over-the-Top (OTT) device.
18. The non-transitory computer-readable medium of claim 16, wherein the trained machine learning model comprises an exploratory component to select the second subset of content assets.
18. The non-transitory computer-readable medium of claim 16, wherein the trained machine learning model comprises an exploratory component to select the third subset of content assets.
19. The non-transitory computer-readable medium of claim 16, wherein the first subset of content assets comprises the content most closely matching historical interactions of the user.
19. The non-transitory computer-readable medium of claim 16, wherein the first subset of content assets comprises content most closely matching historical interactions of the user.
20. The non-transitory computer-readable medium of claim 16, wherein the first subset of content assets comprises popular or highest rated current content.
20. The non-transitory computer-readable medium of claim 16, wherein the first subset of content assets comprises popular or highest rated current content.
Reasons for Allowance
Note: If a Terminal Disclaimer were filed and the claim objections were overcome, the reasons for allowance are presented below.
The following is an examiner's statement of reasons for allowance:
Independent Claims 1, 9 and 16 are considered allowable since when reading the claims
in light of the specification, as per MPEP §2111.01 none of the references of record alone or in
combination disclose or suggest the combination of limitations specified in independent claim 1,
including
"generating, by at least one processor and based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the first subset of content assets includes a first number of content selections reflecting a user's interests;
generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
selecting, based on the trained machine learning model, a specific content asset from the first subset of content assets or the second subset of content assets, wherein the selecting of the content asset is based on the trained machine learning model being trained to weight content asset subset generation based on when the specific content asset relates a specified content category;
receiving, at a media device, the content asset;
stitching the specific content asset into the target banner template to form a composite banner; and
rendering the composite banner on a display of the media device;
The examiner has found that the prior art(s) does not appear to teach or suggest or render
obvious the claimed limitation(s) as recited in independent claim(s). The prior art of record fail(s)
to teach or suggest individually or in combination the independent claims.
Examiner presents Stein et al., US Pub. 2014/0297739 A1 (hereinafter Stein) as teaching using a trained machine-learning algorithm to recommend media content based on the user's friends as well as user's likes and interests ([0040], [0056] - [0057], [0062] - [0064] and [0085]). Stein further discloses that media content/previews may be represented by an image which may be selected by a user to begin playing, (Fig. 3, [0049], and [0075]). However, Stein does not clearly demonstrate the concept of stitching the content asset into a target banner template to form a composite banner and rendering the banner on a display.
To supplement the teachings of Stein, the examiner presents Kushner et al., US Pub.
2022/0309543 A1 (hereinafter Kushner) as teaching integrating an image into a first advertisement template to generate a customized advertisement and providing the customized advertisement to the user, (Fig. 3, [0005], [0069] and [0072]). However, the combination of Stein and Kushner does not clearly teach the limitation "wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of content assets being selected by the user at a frequency above a threshold value," as claimed.
Examiner presents Looney et al., US Pub. 2011/0208852 A1 as teaching that neural networks can be used to generate intelligent algorithms which allow for filtering, ranking, sorting and/or displaying information which serve sponsors with taste-targeted banner advertisement to an audience who is likely much more interested in their product, ([0067] and [0074 ]-[0075]). But the combination of Stein, Kushner and Looney does not clearly demonstrate the limitation that a number of content selections are related to user’s interests and differ from the first subset of content assets, as claimed.
Examiner further presents Shirley et al., US Pub. 2016/0247189 A1 as teaching using dynamic banners for the promotion of events or other information, ([0007] and [0016]). But Shirley does not disclose content selections unrelated to user's interests and selecting a content asset based on the trained machine learning model being trained to weight content asset subset generation based on when specific content asset relates to a specified content category, as claimed.
Examiner further presents AMIR et al., US Pub. 2024/0031616 A1 as teaching the banner advertisement may be used to preferentially recommend live-streaming, ([0126]). To receive a positive rating for a live-stream to be preferentially recommended, the live-stream needs to satisfy the evaluation criteria regarding the required number of established viewers, ([0098]). While Amir discloses preferentially recommending content, this recommendation is not based on the content assets being selected by the user at a frequency above a threshold, as claimed.
Examiner presents Johansen et al., US Pub. 2019/0114347 A1 as teaching that search suggestions are ranked based on content recently and/or frequently accessed by a user, ([0046]). However, Johansen does clearly demonstrate selecting content asset based on the content asset relating to a specified content category, as claimed.
Examiner presents Durham et al, US Patent 9,299,331 B1 as teaching the Adsorption
machine-learning algorithm, a label propagation algorithm originally designed to generate video
recommendation across a large library of videos using sparse video consumption data. The
adsorption algorithm takes items with known good labels and using connections within the data
set, adds labels to other related items, (col. 4 II. 38 - 61). However, Durham either alone or in
any combination of Stein, Kushner, Looney, Shirley AMIR and Johansen does not clearly
demonstrate selecting a specific content asset based on how it relates to a specified content category, as claimed.
Examiner presents Trabelsi et al., US Pub. 2017/0243244 A1 as teaching a content
discovery and recommendation service that recommends content based on considerations such
as high diversity categories with high weight/confidence level of the selected categories, a
clustering algorithm which may result in a small number of groups of semantically similar titles,
and searching for common occurrences of sets of categories that appear together frequently,
([0120]). However, Trabelsi either alone or in any combination of Stein, Kushner, Looney,
Shirley, AMIR, Johansen and Durham does not clearly demonstrate the limitations of
independent Claim 1. Therefore, Claim 1 is considered allowable.
Claims 9 and 16 are considered allowable for the same reasons stated above. The
dependent claims 2 - 8, 10 - 15 and 17 - 20 are allowed because they further limit independent
claims 1, 9 and 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zheng et al., US Pub. 2023/0076209 A1 teach generating personalized banner images using machine learning, ([0002]). In machine-learning program is trained, for example, when a user selects a particular online displayed banner image, the machine-learning program is updated with additional features which will be used in further training so that future online banner images will be more personalized, (]0048]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Cynthia M FOGG whose telephone number is (571)272-2741. The examiner can normally be reached Monday-Friday 7:00-3:30.
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, Nathan Flynn can be reached at (571)272-1915. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CYNTHIA M FOGG/Primary Examiner, Art Unit 2421
