Detailed Action
Claims 1-20 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/24/25 has been considered.
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.
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Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,367,431(18/646,433). Although the claims at issue are not identical, they are not patentably distinct from each other because ‘433 teaches the instant claims and differs in well known and obvious verbiage and/or limitations(ie claims 7,17 are well known and obvious features given the teachings of ‘433).
Instant claims
12,367,431
1. A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
in response to receiving a content request associated with a content collection, identifying a candidate content item for integration into the content collection, the candidate content item being associated with a first value, and the content collection to be presented on a device of a user;
automatically generating, using at least one machine learning model, a select value and a skip value for the candidate content item, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
18. The system of claim 1, wherein the second value comprises a content relevancy value indicative of relevance of the candidate content item to the user.
generating, for the candidate content item, a second value that is based on the first value, the select value, and the skip value;
automatically selecting the candidate content item from a plurality of candidate content items based on the second value generated for the candidate content item meeting at least one predetermined criterion;
automatically integrating the candidate content item into the content collection; and
causing presentation of the content collection on the device of the user.
2. The system of claim 1, wherein the device of the user generates the content request, and the integrating of the candidate content item into the content collection comprises adding the candidate content item to one or more other content items selected prior to the generation of the content request.
3. The system of claim 1, wherein the selecting of the candidate content item from the plurality of candidate content items comprises selecting multiple candidate content items from the plurality of candidate content items based on respective second values generated for the multiple candidate content items, the multiple candidate content items comprise a first content item and a second content item, and the operations further comprising:
performing a comparison of the second value generated for the first content item and the second value generated for the second content item; and
based on a result of the comparison, integrating the first content item and the second content item into the content collection by adding the first content item to a first area and adding the second content item to a second area.
4. The system of claim 3, wherein the first area is a first placeholder area between a first pair of pre-selected content items and the second area is a second placeholder area between a second pair of pre-selected content items.
5. The system of claim 3, wherein the content collection comprises an ephemeral message content collection, and the first content item is positioned so as to appear before the second content item in the ephemeral message content collection based on the comparison.
6. The system of claim 1, wherein the content collection comprises an ephemeral message content collection.
7. The system of claim 6, the operations further comprising: before receiving the content request, generating the ephemeral message content collection without the candidate content item; and generating a placeholder area for the candidate content item, the placeholder area remaining blank until the content request is received.
8. The system of claim 7, wherein, after receiving the content request, the candidate content item is integrated into the ephemeral message content collection for presentation between a first pre-selected content item and a second pre-selected content item.
9. The system of claim 1, wherein the content request is generated in response to the user navigating to a predetermined page within an application.
10. The system of claim 9, wherein the generating of the second value, the selection of the candidate content item, and the integration of the candidate content item are performed substantially in real time while the user is on the page.
11. The system of claim 1, wherein the candidate content item can be skipped by performing a first device input action through the device and the candidate content item can be selected by performing a second device input action through the device.
12. The system of claim 11, wherein the selecting of the candidate content item from the plurality of candidate content items comprises selecting multiple candidate content items from the plurality of candidate content items based on respective second values generated for the multiple candidate content items, wherein respective ones of the multiple candidate content items and one or more pre-selected content items are presented in sequence in the content collection, and the content collection is navigable by performing the first device input action or the second device input action.
13. The system of claim 11, wherein the first device input action comprises at least one of a tap gesture or a swipe gesture.
14. The system of claim 11, wherein the second device input action at least one of a tap gesture or a swipe gesture.
15. The system of claim 1, wherein the at least one machine learning model implements a random forest scheme.
16. The system of claim 1, wherein the at least one machine learning model comprises an ensemble classifier.
17. The system of claim 1, wherein the first value comprises a value selected by a creator of the candidate content item.
19. A method comprising:
in response to receiving a content request associated with a content collection, identifying a candidate content item for integration into the content collection, the candidate content item being associated with a first value, and the content collection to be presented on a device of a user;
automatically generating, using at least one machine learning model, a select value and a skip value for the candidate content item, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a second value that is based on the first value, the select value, and the skip value;
automatically selecting the candidate content item from a plurality of candidate content items based on the second value generated for the candidate content item meeting at least one predetermined criterion;
automatically integrating the candidate content item into the content collection; and
causing presentation of the content collection on the device of the user.
20. At least one non-transitory machine-readable storage device embodying instructions that, when executed by at least one machine, cause the at least one machine to perform operations comprising:
in response to receiving a content request associated with a content collection, identifying a candidate content item for integration into the content collection, the candidate content item being associated with a first value, and the content collection to be presented on a device of a user;
automatically generating, using at least one machine learning model, a select value and a skip value for the candidate content item, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a second value that is based on the first value, the select value, and the skip value;
automatically selecting the candidate content item from a plurality of candidate content items based on the second value generated for the candidate content item meeting at least one predetermined criterion;
automatically integrating the candidate content item into the content collection; and
causing presentation of the content collection on the device of the user.
1. A method comprising:
in response to receiving a content request associated with aggregated content to be presented on a device of a user, identifying a candidate content item, the candidate content item having a bid value;
automatically generating, using a machine learning model, a relevancy value for the candidate content item, the relevancy value indicating whether the candidate content item is likely to be skipped by the user, the relevancy value being associated with a select value and a skip value, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a combined value by adjusting the bid value for the candidate content item using the relevancy value generated for the candidate content item;
automatically selecting the candidate content item from a plurality of candidate content items based on the combined value generated for the candidate content item;
automatically integrating the candidate content item into at least one placeholder area among one or more pre-selected content items as part of the aggregated content; and
causing presentation of the aggregated content on the device of the user.
2. The method of claim 1, wherein the device of the user generates the content request, and the one or more pre-selected content items are selected prior to the generation of the content request.
3. The method of claim 1, wherein the selection of the candidate content item from the plurality of candidate content items comprises selecting multiple candidate content items from the plurality of candidate content items based on respective combined values generated for the multiple candidate content items, the multiple candidate content items comprise a first content item and a second content item, the at least one placeholder area comprises a first placeholder area and a second placeholder area, and the method further comprises: detecting that the combined value generated for the first content item is higher than the combined value generated for the second content item; and in response to detecting that the combined value generated for the first content item is higher than the combined value generated for the second content item, automatically integrating the first content item into the first placeholder area and the second content item into the second placeholder area.
4. The method of claim 3, wherein the first placeholder area is between a first pair of pre-selected content items of the one or more pre-selected content items and the second placeholder area is between a second pair of pre-selected content items of the one or more pre- selected content items.
5. The method of claim 3, wherein the aggregated content comprises an ephemeral message content collection, and the first content item is positioned so as to appear before the second content item in the ephemeral message content collection.
6. The method of claim 1, wherein the aggregated content comprises an ephemeral message content collection.
7. The method of claim 6, wherein the candidate content item is integrated into the ephemeral message content collection for presentation between a first pre- selected content item of the one or more pre-selected content items and a second pre-selected content item of the one or more pre-selected content items.
8. The method of claim 1, wherein adjusting the bid value for the candidate content item comprises automatically boosting or attenuating the bid value based on whether the relevancy value is positive or negative for the candidate content item with respect to the user.
9. The method of claim 1, wherein the content request is generated in response to the user navigating, within an application, to a page configured to receive the aggregated content.
10. The method of claim 9, wherein the generating of the combined value, the selection of the candidate content item, and the integration of the candidate content item are performed while the user is on the page.
11. The method of claim 1, wherein the user is enabled to skip the candidate content item by performing a first device input action through the device, the user is enabled to select the candidate content item by performing a second device input action through the device, and the device is configured to distinguish between the first device input action and the second device input action.
12. The method of claim 11, wherein the selection of the candidate content item from the plurality of candidate content items comprises selecting multiple candidate content items from the plurality of candidate content items based on respective combined values generated for the multiple candidate content items, the aggregated content comprises a content collection in which respective ones of the multiple candidate content items and the one or more pre- selected content items are presented in sequence and navigable by performing the first device input action or the second device input action.
13. The method of claim 11, wherein the first device input action comprises at least one of a tap gesture or a swipe gesture.
14. The method of claim 11, wherein the second device input action at least one of a tap gesture or a swipe gesture.
17. The method of claim 1, wherein the machine learning model implements a random forest scheme.
18. The method of claim 1, wherein the machine learning model comprises an ensemble classifier.
15. The method of claim 1, wherein causing the presentation of the aggregated content on the device of the user comprises transmitting the aggregated content to the device in response to the content request.
16. The method of claim 1, further comprising storing the aggregated content including the candidate content item.
19. A system comprising: one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
in response to receiving a content request associated with aggregated content to be presented on a device of a user, identifying a candidate content item, the candidate content item having a bid value;
automatically generating, using a machine learning model, a relevancy value for the candidate content item, the relevancy value indicating whether the candidate content item is likely to be skipped by the user, the relevancy value being associated with a select value and a skip value, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a combined value by adjusting the bid value for the candidate content item using the relevancy value generated for the candidate content item;
automatically selecting the candidate content item from a plurality of candidate content items based on the combined value generated for the candidate content item;
automatically integrating the candidate content item into at least one placeholder area among one or more pre-selected content items as part of the aggregated content; and
causing presentation of the aggregated content on the device of the user.
20. A non-transitory machine-readable storage device embodying instructions that, when executed by at least one machine, cause the at least one machine to perform operations comprising:
in response to receiving a content request associated with aggregated content to be presented on a device of a user, identifying a candidate content item, the candidate content item having a bid value;
automatically generating, using a machine learning model, a relevancy value for the candidate content item, the relevancy value indicating whether the candidate content item is likely to be skipped by the user, the relevancy value being associated with a select value and a skip value, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a combined value by adjusting the bid value for the candidate content item using the relevancy value generated for the candidate content item;
automatically selecting the candidate content item from a plurality of candidate content items based on the combined value generated for the candidate content item;
automatically integrating the candidate content item into at least one placeholder area among one or more pre-selected content items as part of the aggregated content; and
causing presentation of the aggregated content on the device of the user.
Allowable Subject Matter
Claims 1-20 allowed over prior art and the applicant is advised to file Terminal Disclaimer to overcome the Double Patenting Rejection.
REASONS FOR ALLOWANCE
The following is an examiner’s statement of reasons for allowance: the prior art singly or in combination does not teach the totality of the independent claims when read in light of the specification, specifically para.23,69-70.
The closest prior are of record is US 2018/0139293 issued to Dimson et al.(Dimson), which teaches para.31, teaches information such as content items a user has viewed, viewing duration, and other actions that is used to train machine learning models, para.41; teaches training data previously presented content to the user and indication that the user selected an option to like the content items, the model is trained using the user’s action or inaction when making predications for other content items; para.40,scoring using machine learning model, 42, training and utilization of models for prediction of whether user takes a particular action on content items such as like, post comment, view content, etc, para.43-44,score content item determined based on combination of likelihood of the user selecting a “like” option with respect to the content item and a likelihood the user posting a comment in response to the content item, therefore teaches
As per claims 1, 19, 20, in response to receiving a content request associated with a content collection, identifying a candidate content item for integration into the content collection, the candidate content item, and the content collection to be presented on a device of a user; using at least one machine learning model; automatically selecting the candidate content item from a plurality of candidate content items; automatically integrating the candidate content item into the content collection; and causing presentation of the content collection on the device of the user.
However these prior art does not teach nor would it be obvious to one ordinary skill in the art to combine to teach
As per claims 1, 19, 20, in response to receiving a content request associated with a content collection, identifying a candidate content item for integration into the content collection, the candidate content item being associated with a first value, and the content collection to be presented on a device of a user;
automatically generating, using at least one machine learning model, a select value and a skip value for the candidate content item, the select value indicating a likelihood that the user will select the candidate content item, and the skip value indicating a likelihood that the user will bypass the candidate content item;
generating, for the candidate content item, a second value that is based on the first value, the select value, and the skip value;
automatically selecting the candidate content item from a plurality of candidate content items based on the second value generated for the candidate content item meeting at least one predetermined criterion;
automatically integrating the candidate content item into the content collection; and
causing presentation of the content collection on the device of the user.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
US 2014/0012671 issued to Ye et al., teaches targeted online advertisement includes receiving a request for an ad impression to be provided to a user in a network environment. The request includes a first content and a second content. The method also includes, using a processor, determining a context of the first content and a context of the second content, determining a correlation between the context of the first content and the context of the second content, and identifying a plurality of ads as candidates for consideration. The method further includes, using the processor, ranking the plurality of identified ads, selecting an ad among the plurality of identified ads based at least in part on a result of the ranking, and providing the selected ad as the ad impression to be displayed to the user in response to receiving the request
US 2018/0189668 issued to Ray et al, teaches measuring and predicting content dissemination in social networks includes computing a “virality score” for popularity of social media content, a “pattern” of diffusion of the content, and a “hype” parameter of such content without requiring a “friendship graph” or “information diffusion” structured data. It also includes an iterative, predictive model, which predicts future performance (or future rate of dissemination) of the content while mitigating a class imbalance problem inherent in predicting viral posts, and which provides updates to the model based on actual performance results.
US 2016/0189234 issued to Tang et al., teaches a social networking system selects content items for presentation to a user. To promote user interaction with selected content items, the social networking system scores content items based at least in part on similarity in appearances of the content items to an appearance of a content item for which the social networking system is compensated for presentation (a “sponsored content item”). For example, a model is applied to features describing appearance of a content item to generate the score for a content item. When selecting content items for presentation, a score associated with a content item may modify the likelihood of the content item being selected. A content item with a score indicating greater than a threshold similarity in appearance to an appearance of a sponsored content item may be penalized when the social networking system selects content for presentation.Any inquiry concerning this communication or earlier communications from the examiner should be directed to BACKHEAN TIV whose telephone number is (571)272-5654. The examiner can normally be reached on Mon.-Thurs. 5:30-3:30.
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/BACKHEAN TIV/
Primary Examiner
Art Unit 2459
Prosecution Insights