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 action is in response to appeal conference decision made on 21 April 2026 for the Appeal filed on 2 February 206. Claims 1-20 are currently pending and have been examined.
Reopening of Prosecution After Appeal Brief
In view of the appeal filed on 2 February 2026, PROSECUTION IS HEREBY REOPENED. New grounding of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
Terminal Disclaimer
The terminal disclaimer filed on 25 November 2024 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of application no of 18/477, 735 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Claim Rejections - 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.
Step 1: The claims 1-8 are method, 9-16 are medium and claims 17-20 are system. Thus, each independent claims, on its face, is directed to one of the statutory categories of 35 U.S.C § 101. However, the claims 1-20 are rejected under 35 U.S.C § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2-Prong 1: independent claims (1, 9 and 17) recite identifying, a content distribution campaign including data from a first source; obtaining, , a content provider input including feedback for the content distribution campaign, wherein the feedback includes an adjustment to a parameter of the content distribution campaign and a request for additional data from a second source external to the user experience platform; generating a prompt based on the data, and the additional data and the feedback embedding, wherein the prompt includes the feedback embedding and a text instruction to the machine learning model to generate content for a modified content distribution campaign based on the data and the additional data; and generating the content for the modified content distribution campaign based on the prompt. \
These limitation as drafted is a process, under broadest reasonable intepration, fall within:
Method of Organizing Human Activity/Business Method: Identifying campaigns, obtaining feedback (input), and modifying parameters are methods of managing business/advertising.
Mental Process/Data Manipulation: Encoding inputs, generating prompts, and content generation can be seen as data processing that a human could hypothetically perform, which is considered ineligible "mental process".
Step 2-Prong 2: The claim recites the combination of additional elements of content generation using machine learning (ML) is recited at high level of generality (i.e., as generic computer components) that merely applies established AI techniques to new data—without disclosing specific improvements to the AI model itself . Further, The generic computer components are "Conventional": used to implements the steps of "identifying a campaign," "obtaining user feedback," "encoding" via a multimodal encoder, and "generating a prompt" for a "machine learning model" are views as conventional, well-understood steps, even if combined. This generic computer components limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The Federal Circuit held that "applying generic machine learning techniques to new data environments" is not patent eligible. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is ineligible
Dependent claims 2-8, 10-16 and 17-20, these claims merely provide additional abstract concept and narrow abstract idea of claims 1, 9 and 15. Further, claims 1-20 are recited as such a high level that the claimed steps amount to no more than mental process such as concept perform by the human mind (including an observation, evaluation , judgment, opinion) because modified content is generated that meets specified parameter. Thus, the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Arora et al (US Pub., 2019/0180357 A1) in view of Maher et al (US Pub., 2010/0293050 A1)
With respect to claim 1, Arora teaches a method for content generation(paragraph [0022], discloses media content formatted and paragraph [0028], discloses systems and methods for facilitating the delivery of electronic content) , comprising:
identifying, by a user experience platform, a content distribution campaign including data from a first source(paragraph [0039], discloses setting up a content campaign that includes content item , content provider may provide information at the content campaign or content group level that the content selector .));
encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content [embedded space] and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ));
generating a prompt for a machine learning model based on the data and the additional data and the feedback embedding, and the additional data and the feedback embedding, wherein the prompt includes the feedback embedding and a text instruction to the machine learning model to generate content for a modified content distribution campaign based on the data and the additional data (Fig. 3A, paragraph [0023], discloses feedback may be in the form of binary feedback , for example, the question can be “Was this content item relevant to you?” The feedback option may include a “Yes” button and “No” button [instructions ], …, provide additional prompts or question to receive feedback as to why the suer believes the content item relative or not relevant.., (paragraph [0032], discloses data processing system can prompt on the user of the computing device for consent to obtain one or more type of network activity information, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content [embedded space] paragraph [0054], discloses the dropdown menu can include signal response to prompt or query in the … ) ;
encoding the prompt to obtain a prompt embedding(paragraph [0053], discloses provide the perform a feedback via electronic survey interface and can include button and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ); and
generating, using the machine learning model, the content for the modified content distribution campaign based on the prompt embedding using the machine learning model(paragraph [0020], discloses using electric feedback signal received from comping device to adjust the only content item .., data processing system can change, adjust modify or alter an auction score of a content item .., adjustment may lead to a new auction e.g., content item that data processing system predict to be less annoying that is less native feedback …, paragraph [0087]-[0088], discloses the machine learing module historical feedback signal form multiple computing device in response to instance of electronic survey interface provide ed …, data processing system can select the first content item based on a comparison the first score and …, adjust ranking of one or more candidate content item can be different form the initial ranking.., see also paragraphs [0055]-[0060]) .
Arora teaches the above elements including obtaining, by a user interface of the user experience platform, a content provider input including feedback (paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign, paragraph [0021], discloses when a content item with a lower or smaller predicted negative feedback signal (but not the highest bid) .., paragraph [0022], discloses data processing system can generate an electronic survey (or feedback) interface to obtain feedback signals for content time ) and certain data may treated in one or more ways before it stored or used so that certain information about user is removed when generating parameters (e.g., demographic parameters [adjustment parameter] ) for example, a user’s identify may be treated so that no identify information can be determined for user or user geographic location …) and the dropdown menu can include signal response to prompt or query in the electronic survey interface (paragraph [0054]) . Arora failed to teach wherein the feedback includes an adjustment to a parameter of the content distribution campaign and a request for additional data from a second source external to the user experience platform.
However, Maher teaches wherein the feedback includes an adjustment to a parameter of the content distribution campaign (paragraph [0043], discloses content provide can get real-time feedback on the performance of their content .., based on statistic proved by data warehouse content provider can modify the rules assocted with their content item content provider can modify rules, paragraph [0063], discloses content provider can get real-time feedback on the performance of content , based on statistic provided by a data warehouse content provider can modify the rules assocted with the content item paragraph [0093], discloses feedback could include an identification of the winning bid and abstract user provide and content information and paragraph [0132], discloses feedback could include content information regard the ads that or content was rendered .., additional information provided such as the winning bid price or the like.., and paragraph [0494]-[0496], dislcies content license may contain the content provider preference and rules , ad license my contain bidding control user profile .., bidding controls.. ) and a request for additional data from a second source external to the user experience platform(paragraph [0044], discloses obtained or requested by the user, and whether and which advertisements [additional data] to render in connection therewith and paragraph [0061], dislcies content provider and content aggregator deploy their content packing content items with rules (e.g., rules of type enforced by a DRM engine) that require rendering application to optimally choose ads that fill content ad-slots identified withing the content .. ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for can generate, maintain, create, or train machine learning models that can predict a feedback signal based on historical, previous or past feedback signals of Arora modify by providing content provider with the option to include an identification of winning bid, and abstracted user profile, contextual and content information and the like of Maher in order to use this information to take further action (e.g., by increasing their bid prices by distributing updated controls for use in future auctions (see Maher, paragraph [0093]).
With respect to claim 2, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method identifying, by the user experience platform, a plurality of content elements of the content distribution campaign, wherein the feedback comprise individual feedback for each of the plurality of content elements(Fig. 3A, 305, 310d, 310e, paragraph [0019], discloses data processing system can receive form a plurality of computing devices , feedback signals assocted with content items.., to improve a content selection [individual feedback] for each plurlity of content elements], and paragraph [0051], discloses data processing can provide an online electronic content item for display with a webpage…, receiving feedback signals via an electronic survey.., provide, render, or display content items 305,
325 or 330 from a content provider …, to third-party content items, supplemental content items, or advertisements).
With respect to claim 3, Arora in view of Maher teaches elements of claim 1, furthermore Arora teaches generating, using the machine learning model, an additional content element based on the individual feedback for a corresponding content element of the plurality of content elements, (Fig. 3A, 305, 310d, 310e, paragraph [0019], discloses data processing system can receive form a plurality of computing devices , feedback signals assocted with content items.., to improve a content selection [individual feedback] for each plurlity of content elements], and paragraph [0051], discloses data processing can provide an online electronic content item for display with a webpage…, receiving feedback signals via an electronic survey.., provide, render, or display content items 305, 325 or 330 from a content provider …, to third-party content items, supplemental content items, or advertisements). Arora failed to teach wherein the modified content distribution campaign includes the additional content element
However, Maher teaches wherein the modified content distribution campaign includes the additional content element(paragraph [0044], discloses obtained or requested by the user, and whether and which advertisements [additional data] to render in connection therewith and paragraph [0061], dislcies content provider and content aggregator deploy their content packing content items with rules (e.g., rules of type enforced by a DRM engine) that require rendering application to optimally choose ads that fill content ad-slots identified withing the content .. ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for can generate, maintain, create, or train machine learning models that can predict a feedback signal based on historical, previous or past feedback signals of Arora modify by providing content provider with the option to include an identification of winning bid, and abstracted user profile, contextual and content information and the like of Maher in order to use this information to take further action (e.g., by increasing their bid prices by distributing updated controls for use in future auctions (see Maher, paragraph [0093]).
With respect to claim 4, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method further comprising updating, by the user experience platform, the content distribution campaign with the modified content distribution campaign based on the content provider input (paragraph [0020], discloses based on the adjustment the data processing system can select content item with a lower bid price and lower predicted annoyance to with an auction over a content item and paragraph [0091], discloses modifying the online content item scores with the predicted dislike signal .. ).
Arora teaches the above elements including receiving, by the user experience platform, a content provider input (paragraph [0020], discloses the data processing system can change, adjust modify or alter an auction score of a content item according a likelihood that a feedback signal for the content item …, and paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign).
Arora failed to teach input indicating to accept the modified content distribution campaign.
However, Maher teaches input indicating to accept the modified content distribution campaign(paragraph [0062], discloses content provider may have an incitive to accept bids from distributes). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for provided by a content provider [a content provider input]) including feedback for the content distribution campaign of Arora with a feature of accepting the proposed bids of Maher in order to receive incentives (ss Mahar, paragraph [0062])
With respect to claim 5, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method further comprising: receiving, by the user experience platform, a user input from a user of the content distribution campaign, wherein the feedback includes the user input(paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign, paragraph [0021], discloses when a content item with a lower or smaller predicted negative feedback signal (but not the highest bid) .., paragraph [0022], discloses data processing system can generate an electronic survey (or feedback) interface to obtain feedback signals for content time).
With respect to claim 6, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method further comprising: monitoring, by the user experience platform, a performance of the content distribution campaign to obtain a performance metric, wherein the feedback is based on the performance metric ( paragraph [0048], discloses the machine learing technique for example a supervised machine learing technique neural network, regression technique, liner regression techniques, Bayesian estimator to obtain feature data and signal data assocted with serval content item impression and train the model using the feature data and corresponding signal data of each of the historical content item .., statical process to estimate ..).
With respect to claim 7, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method further comprising: receiving, by the user experience platform, a modification input from a content provider and identifying, by the machine learning model, a modification intent based on the modification input, wherein the modified content distribution campaign is based on the modification intent(paragraph [0020], discloses a likelihood that a feedback signal for the content item will be negative [intent based on the modification input].., receive or more negative feedback .., based on this adjustment ).
With respect to claim 8, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches the method further comprising: updating, by the user experience platform, the machine learning model based on the feedback(paragraph [0056], discloses generate feedback signals with a value and store the feedback signal in data repository .., and paragraph [0060], discloses generate, maintain, train or update a model via the machine learing engine the historical sigla [feedback]).
With respect to claim 9, Arora teaches a non-transitory computer readable medium storing code for content generation (paragraphs [ 0098], disclose computer readable medium ), comprising:
identifying, by a user experience platform, a content distribution campaign including data from a first source(paragraph [0039], discloses setting up a content campaign that includes content item , content provider may provide inofrmtion at the content campaign or content group level that the content selector .));
encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content [embedded space] and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ));
generating a prompt for a machine learning model based on the data and the additional data and the feedback embedding, and the additional data and the feedback embedding, wherein the prompt includes the feedback embedding and a text instruction to the machine learning model to generate content for a modified content distribution campaign based on the data and the additional data (Fig. 3A, paragraph [0023], discloses feedback may be in the form of binary feedback , for example, the question can be “Was this content item relevant to you?” The feedback option may include a “Yes” button and “No” button [instructions ], …, provide additional prompts or question to receive feedback as to why the user believes the content item relative or not relevant.., (paragraph [0032], discloses data processing system can prompt on the user of the computing device for consent to obtain one or more type of network activity information, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content .. paragraph [0054], discloses the dropdown menu can include signal response to prompt or query in the … ) ;
encoding the prompt to obtain a prompt embedding(paragraph [0053], discloses provide the perform a feedback via electronic survey interface and can include button and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ); and
generating, using the machine learning model, the content for the modified content distribution campaign based on the prompt embedding using the machine learning model(paragraph [0020], discloses using electric feedback signal received from comping device to adjust the only content item .., data processing system can change, adjust modify or alter an auction score of a content item .., adjustment may lead to a new auction e.g., content item that data processing system predict to be less annoying that is less native feedback …, paragraph [0087]-[0088], discloses the machine learing module historical feedback signal form multiple computing device in response to instance of electronic survey interface provide ed …, data processing system can select the first content item based on a comparison the first score and …, adjust ranking of one or more candidate content item can be different form the initial ranking.., see also paragraphs [0055]-[0060]) .
Arora teaches the above elements including obtaining, by a user interface of the user experience platform, a content provider input including feedback (paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign, paragraph [0021], discloses when a content item with a lower or smaller predicted negative feedback signal (but not the highest bid) .., paragraph [0022], discloses data processing system can generate an electronic survey (or feedback) interface to obtain feedback signals for content time ) and certain data may treated in one or more ways before it stored or used so that certain information about user is removed when generating parameters (e.g., demographic parameters [adjustment parameter] ) for example, a user’s identify may be treated so that no identify information can be determined for user or user geographic location …) and the dropdown menu can include signal response to prompt or query in the electronic survey interface (paragraph [0054]) . Arora failed to teach wherein the feedback includes an adjustment to a parameter of the content distribution campaign and a request for additional data from a second source external to the user experience platform.
However, Maher teaches wherein the feedback includes an adjustment to a parameter of the content distribution campaign (paragraph [0043], discloses content provide can get real-time feedback on the performance of their content .., based on statistic proved by data warehouse content provider can modify the rules assocted with their content item content provider can modify rules, paragraph [0063], discloses content provider can get real-time feedback on the performance of content , based on statistic provided by a data warehouse content provider can modify the rules assocted with the content item paragraph [0093], discloses feedback could include an identification of the winning bid and abstract user provide and content information and paragraph [0132], discloses feedback could include content information regard the ads that or content was rendered .., additional information provided such as the winning bid price or the like.., and paragraph [0494]-[0496], dislcies content license may contain the content provider preference and rules , ad license my contain bidding control user profile .., bidding controls.. ) and a request for additional data from a second source external to the user experience platform(paragraph [0044], discloses obtained or requested by the user, and whether and which advertisements [additional data] to render in connection therewith and paragraph [0061], dislcies content provider and content aggregator deploy their content packing content items with rules (e.g., rules of type enforced by a DRM engine) that require rendering application to optimally choose ads that fill content ad-slots identified withing the content .. ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for can generate, maintain, create, or train machine learning models that can predict a feedback signal based on historical, previous or past feedback signals of Arora modify by providing content provider with the option to include an identification of winning bid, and abstracted user profile, contextual and content information and the like of Maher in order to use this information to take further action (e.g., by increasing their bid prices by distributing updated controls for use in future auctions (see Maher, paragraph [0093]).
With respect to claim 10, Arora in view of Maher teaches elements of claim 9, furthermore, Arora teaches the non-transitory computer readable medium the code futher comprising instructions, that when executed by the at least processor, cause the at least one processor to perform operation comparing:
identifying, by the user experience platform, a plurality of content elements of the content distribution campaign, wherein the feedback comprise individual feedback for each of the plurality of content elements(Fig. 3A, 305, 310d, 310e, paragraph [0019], discloses data processing system can receive form a plurality of computing devices , feedback signals assocted with content items.., to improve a content selection [individual feedback] for each plurlity of content elements], and paragraph [0051], discloses data processing can provide an online electronic content item for display with a webpage…, receiving feedback signals via an electronic survey.., provide, render, or display content items 305,
325 or 330 from a content provider …, to third-party content items, supplemental content items, or advertisements).
With respect to claim 11, Arora in view of Maher teaches elements of claim 10, furthermore Arora teaches the a non-transitory computer readable medium the code further comprising instruction that when executed by the at least processor, cause the at least one processor to perform operation comparing: generating, using the machine learning model, an additional content element based on the individual feedback for a corresponding content element of the plurality of content elements, (Fig. 3A, 305, 310d, 310e, paragraph [0019], discloses data processing system can receive form a plurality of computing devices , feedback signals assocted with content items.., to improve a content selection [individual feedback] for each plurlity of content elements], and paragraph [0051], discloses data processing can provide an online electronic content item for display with a webpage…, receiving feedback signals via an electronic survey.., provide, render, or display content items 305, 325 or 330 from a content provider …, to third-party content items, supplemental content items, or advertisements). Arora failed to teach wherein the modified content distribution campaign includes the additional content element
However, Maher teaches wherein the modified content distribution campaign includes the additional content element(paragraph [0044], discloses obtained or requested by the user, and whether and which advertisements [additional data] to render in connection therewith and paragraph [0061], dislcies content provider and content aggregator deploy their content packing content items with rules (e.g., rules of type enforced by a DRM engine) that require rendering application to optimally choose ads that fill content ad-slots identified withing the content .. ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for can generate, maintain, create, or train machine learning models that can predict a feedback signal based on historical, previous or past feedback signals of Arora modify by providing content provider with the option to include an identification of winning bid, and abstracted user profile, contextual and content information and the like of Maher in order to use this information to take further action (e.g., by increasing their bid prices by distributing updated controls for use in future auctions (see Maher, paragraph [0093]).
With respect to claim 12, Arora in view of Maher teaches elements of claim 9, furthermore, Arora teaches the non-transitory computer readable medium the code further comprising instruction that when executed by the at least processor, cause the at least one processor to perform operation comparing: updating, by the user experience platform, the content distribution campaign with the modified content distribution campaign based on the content provider input (paragraph [0020], discloses based on the adjustment the data processing system can select content item with a lower bid price and lower predicted annoyance to with an auction over a content item and paragraph [0091], discloses modifying the online content item scores with the predicted dislike signal .. ).
Arora teaches the above elements including receiving, by the user experience platform, a content provider input (paragraph [0020], discloses the data processing system can change, adjust modify or alter an auction score of a content item according a likelihood that a feedback signal for the content item …, and paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign).
Arora failed to teach input indicating to accept the modified content distribution campaign.
However, Maher teaches input indicating to accept the modified content distribution campaign(paragraph [0062], discloses content provider may have an incitive to accept bids from distributes). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for provided by a content provider [a content provider input]) including feedback for the content distribution campaign of Arora with a feature of accepting the proposed bids of Maher in order to receive incentives (ss Mahar, paragraph [0062])
With respect to claim 13, Arora in view of Maher teaches elements of claim 9, furthermore, Arora teaches the a non-transitory computer readable medium the code further comprising instruction that when executed by the at least processor, cause the at least one processor to perform operation comparing:: receive, a user input from a user of the content distribution campaign, wherein the feedback includes the user input (paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign, paragraph [0021], discloses when a content item with a lower or smaller predicted negative feedback signal (but not the highest bid) .., paragraph [0022], discloses data processing system can generate an electronic survey (or feedback) interface to obtain feedback signals for content time).
With respect to claim 14, Arora in view of Maher teaches elements of claim 9, furthermore, Arora teaches a non-transitory computer readable medium the code further comprising instruction executable by the processor to : monitoring a performance of the content distribution campaign to obtain a performance metric, wherein the feedback is based on the performance metric( paragraph [0048], discloses the machine learing technique for example a supervised machine learing technique neural network, regression technique, liner regression techniques, Bayesian estimator to obtain feature data and signal data assocted with serval content item impression and train the model using the feature data and corresponding signal data of each of the historical content item .., statical process to estimate ..).
With respect to claim 15, Cavander in view of Divakaran teaches elements of claim 9, furthermore, Cavander teaches the a non-transitory computer readable medium the code further comprising instruction that when executed by the at least processor, cause the at least one processor to perform operation comparing: receive a modification input from a content provider and identifying, by the machine learning model, a modification intent based on the modification input, wherein the modified content distribution campaign is based on the modification intent(paragraph [0043], discloses the marketing elements based on the input data and to generate a marketing plan based on the scenario data).
With respect to claim 16, Arora in view of Maher teaches elements of claim 1, furthermore, Arora teaches a non-transitory computer readable medium the code further comprising instruction that when executed by the at least processor, cause the at least one processor to perform operation comparing: update, the machine learning model based on the feedback(paragraph [0056], discloses generate feedback signals with a value and store the feedback signal in data repository .., and paragraph [0060], discloses generate, maintain, train or update a model via the machine learing engine the historical sigla [feedback]).
With respect to claim 17, Arora teaches a system (paragraph [0097], disclose computer system ), comprising:
a memory components and ; processing device coupled to the memory components the processing device configured to perform operation (paragraph [0009] discloses processor and memory) comprising:
identifying, by a user experience platform, a content distribution campaign including data from a first source(paragraph [0039], discloses setting up a content campaign that includes content item , content provider may provide inofrmtion at the content campaign or content group level that the content selector .));
encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content [embedded space] and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ));
generating a prompt for a machine learning model based on the data and the additional data and the feedback embedding, and the additional data and the feedback embedding, wherein the prompt includes the feedback embedding and a text instruction to the machine learning model to generate content for a modified content distribution campaign based on the data and the additional data (Fig. 3A, paragraph [0023], discloses feedback may be in the form of binary feedback , for example, the question can be “Was this content item relevant to you?” The feedback option may include a “Yes” button and “No” button [instructions ], …, provide additional prompts or question to receive feedback as to why the user believes the content item relative or not relevant.., (paragraph [0032], discloses data processing system can prompt on the user of the computing device for consent to obtain one or more type of network activity information, paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widower other user interface elements and may overlay at least a portion of publisher content .. paragraph [0054], discloses the dropdown menu can include signal response to prompt or query in the … ) ;
encoding the prompt to obtain a prompt embedding(paragraph [0053], discloses provide the perform a feedback via electronic survey interface and can include button and paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the on the content item [encoding prompt] ); and
generating, using the machine learning model, the content for the modified content distribution campaign based on the prompt embedding using the machine learning model(paragraph [0020], discloses using electric feedback signal received from comping device to adjust the only content item .., data processing system can change, adjust modify or alter an auction score of a content item .., adjustment may lead to a new auction e.g., content item that data processing system predict to be less annoying that is less native feedback …, paragraph [0087]-[0088], discloses the machine learing module historical feedback signal form multiple computing device in response to instance of electronic survey interface provide ed …, data processing system can select the first content item based on a comparison the first score and …, adjust ranking of one or more candidate content item can be different form the initial ranking.., see also paragraphs [0055]-[0060]) .
Arora teaches the above elements including obtaining, by a user interface of the user experience platform, a content provider input including feedback (paragraph [0021], discloses a bid provided by a content provider [a content provider input]) including feedback for the content distribution campaign, paragraph [0021], discloses when a content item with a lower or smaller predicted negative feedback signal (but not the highest bid) .., paragraph [0022], discloses data processing system can generate an electronic survey (or feedback) interface to obtain feedback signals for content time ) and certain data may treated in one or more ways before it stored or used so that certain information about user is removed when generating parameters (e.g., demographic parameters [adjustment parameter] ) for example, a user’s identify may be treated so that no identify information can be determined for user or user geographic location …) and the dropdown menu can include signal response to prompt or query in the electronic survey interface (paragraph [0054]) . Arora failed to teach wherein the feedback includes an adjustment to a parameter of the content distribution campaign and a request for additional data from a second source external to the user experience platform.
However, Maher teaches wherein the feedback includes an adjustment to a parameter of the content distribution campaign (paragraph [0043], discloses content provide can get real-time feedback on the performance of their content .., based on statistic proved by data warehouse content provider can modify the rules assocted with their content item content provider can modify rules, paragraph [0063], discloses content provider can get real-time feedback on the performance of content , based on statistic provided by a data warehouse content provider can modify the rules assocted with the content item paragraph [0093], discloses feedback could include an identification of the winning bid and abstract user provide and content information and paragraph [0132], discloses feedback could include content information regard the ads that or content was rendered .., additional information provided such as the winning bid price or the like.., and paragraph [0494]-[0496], dislcies content license may contain the content provider preference and rules , ad license my contain bidding control user profile .., bidding controls.. ) and a request for additional data from a second source external to the user experience platform(paragraph [0044], discloses obtained or requested by the user, and whether and which advertisements [additional data] to render in connection therewith and paragraph [0061], dislcies content provider and content aggregator deploy their content packing content items with rules (e.g., rules of type enforced by a DRM engine) that require rendering application to optimally choose ads that fill content ad-slots identified withing the content .. ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for can generate, maintain, create, or train machine learning models that can predict a feedback signal based on historical, previous or past feedback signals of Arora modify by providing content provider with the option to include an identification of winning bid, and abstracted user profile, contextual and content information and the like of Maher in order to use this information to take further action (e.g., by increasing their bid prices by distributing updated controls for use in future auctions (see Maher, paragraph [0093]).
With respect to claim 18, Arora in view of Maher teaches elements of claim 17, furthermore, Arora teaches the system wherein the processing devices is further configured to perform operations comprising: updating, by the user experience platform, the content distribution campaign with the modified content distribution campaign based on the content provider input (paragraph [0020], discloses based on the adjustment the data processing system can select content item with a lower bid price and lower predicted annoyance to with an auction over a content item and paragraph [0091], discloses modifying the online content item scores with the predicted dislike signal .. ). Arora failed to teach input indicating to accept the modified content distribution campaign.
However, Maher teaches input indicating to accept the modified content distribution campaign(paragraph [0062], discloses content provider may have an incitive to accept bids from distributes). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention form methodology for provided by a content provider [a content provider input]) including feedback for the content distribution campaign of Arora with a feature of accepting the proposed bids of Maher in order to receive incentives (ss Mahar, paragraph [0062])
With respect to claim 19, Arora in view of Maher teaches elements of claim 17, furthermore, Arora teaches the system wherein the processing devices is further configured to perform operations comprising: monitoring by the user experience platform a performance of the content distribution campaign to obtain a performance metric, wherein the feedback is based on the performance metric( paragraph [0048], discloses the machine learing technique for example a supervised machine learing technique neural network, regression technique, liner regression techniques, Bayesian estimator to obtain feature data and signal data assocted with serval content item impression and train the model using the feature data and corresponding signal data of each of the historical content item .., statical process to estimate ..).
With respect to claim 20, Arora in view of Maher teaches elements of claim 17, furthermore, Arora teaches the system wherein the processing devices is further configured to perform operations comprising: updating the machine learning model based on the feedback(paragraph [0056], discloses generate feedback signals with a value and store the feedback signal in data repository .., and paragraph [0060], discloses generate, maintain, train or update a model via the machine learing engine the historical sigla [feedback]).
The prior art on the record:
Arora et al (US Pub., 2019/0180357 A1) discloses the present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request.
Maher et al (US Pub., 2010/0293050 A1) discloses systems and methods are described for targeting advertisements to a user of an electronic device. In one embodiment, the user's device receives multiple advertisements and at least one content item.
NOURI et al. (Pub. No.: US 2025/0336392 Al) discloses systems and methods relate to executing a task using a machine learning model based on prompt generation and collaborative interactions with a user. The machine language model generating a set of questions based on a task request. The user interactively answers the questions. A task processor generates a set of question-answer pairs based on the questions generated by the machine learning model and the answers given by the user.
Response to Arguments
Applicant’s arguments of 35 U.S.C 103 rejection filed on 2 February 2026 with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SABA DAGNEW/Primary Examiner, Art Unit 3682
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