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 amendment filed on 30 July 2026 . Claims 1, 9 and 17 have been amended. Claims 1-20 are currently pending and have been examined.
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 therefore, 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 input, 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), multimodal encoder comparing artificial neural networks are 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) and futher view of Rothberg et al (US Pub., 2019/0347523 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 .));
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] ) in the multimodal embedding space(paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the content item); 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]) , wherein the prompt embedding is provided as input to the machine learning model(paragraph [0040], discloses the data processing system can then provide the candidate content item to the machine learning engine to perform futher selection techniques or processing to selected a content item, paragraph [0041], discloses the content selected can pass to instruct, query, provide or otherwise interface with the machine learning Engen to commucation information assocted with the online real-time auction and paragraph [0087], discloses the data processing system can generate the machine learning model using feedback signals …, electronic survey interface at least partially overlaid on content item display with the webpage on the computing device …); .
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 , paragraph [0024], discloses the system can futher determine, identify detect or otherwise obtain features assocted with the content item or the impression of the content item , the feature includes e.g. time of day, subject matter of the web page on which the content item is displayed, keywork used to select the content item information about computing device, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input including feedback] , etc.) 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 provided 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]).
Arora and Maher teaches the above elements, Arora futher teaches encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input ] , etc. , paragraph [0052], discloses select the provider content item 305, 325 and 330 for display with the publisher content [provider input] paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widow 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] )) and the data processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network, linear regression technique, a Bayesian estimator, etc., to generate the model (paragraph [0087]) and Mahar teaches business model used for ad delivery (paragraph [534]). Arora and Maher failed to teach , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space.
However, Rothberg teaches , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space(paragraphs [0019], and [0034], discloses the first encoder comprises a neural network).
Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network of Arora and business model used for delivery of Maher with the encoder and decoder are neural networks) or any other suitable training algorithm of Rothberg in order to allow a neural network to understand relationships and correlation across entirely diffent sensory input.
With respect to claim 2, Arora in view of Maher and futher view of Rothberg 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 plurality 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 and futher view of Rothberg 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 and futher view of Rothberg 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 (see, Mahar, paragraph [0062])
With respect to claim 5, Arora in view of Maher and futher view of Rothberg 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 and futher view of Rothberg 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 and futher view of Rothberg 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 and futher view of Rothberg 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 information at the content campaign or content group level that the content selector .));
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] ) in the multimodal embedding space(paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the content item); 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]) , wherein the prompt embedding is provided as input to the machine learning model(paragraph [0040], discloses the data processing system can then provide the candidate content item to the machine learning engine to perform futher selection techniques or processing to selected a content item, paragraph [0041], discloses the content selected can pass to instruct, query, provide or otherwise interface with the machine learning Engen to commucation information assocted with the online real-time auction and paragraph [0087], discloses the data processing system can generate the machine learning model using feedback signals …, electronic survey interface at least partially overlaid on content item display with the webpage on the computing device …); .
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 , paragraph [0024], discloses the system can futher determine, identify detect or otherwise obtain features assocted with the content item or the impression of the content item , the feature includes e.g. time of day, subject matter of the web page on which the content item is displayed, keywork used to select the content item information about computing device, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input including feedback] , etc.) 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 provided 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]).
Arora and Maher teaches the above elements, Arora futher teaches encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input ] , etc. , paragraph [0052], discloses select the provider content item 305, 325 and 330 for display with the publisher content [provider input] paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widow 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] )) and the data processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network, linear regression technique, a Bayesian estimator, etc., to generate the model (paragraph [0087]) and Mahar teaches business model used for ad delivery (paragraph [534]). Arora and Maher failed to teach , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space.
However, Rothberg teaches , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space(paragraphs [0019], and [0034], discloses the first encoder comprises a neural network).
Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network of Arora and business model used for delivery of Maher with the encoder and decoder are neural networks) or any other suitable training algorithm of Rothberg in order to allow a neural network to understand relationships and correlation across entirely diffent sensory input.
With respect to claim 10, Arora in view of Maher and further view of Rothberg 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 and further view of Rothberg 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 and further view of Rothberg 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 and further view of Rothberg 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 and further view of Rothberg 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, Arora in view of Maher and further view of Rothberg 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 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 [0035], dislcies content can include a request for an online adveritment, promotion coupon product .., and 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 and further view of Rothberg 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 obtain feature assocted with each candidate content item .., ]).
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 information at the content campaign or content group level that the content selector .));
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] ) in the multimodal embedding space(paragraph [0055], discloses an electronic survey interface that appears embedded or overlaid on the content item); 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]) , wherein the prompt embedding is provided as input to the machine learning model(paragraph [0040], discloses the data processing system can then provide the candidate content item to the machine learning engine to perform futher selection techniques or processing to selected a content item, paragraph [0041], discloses the content selected can pass to instruct, query, provide or otherwise interface with the machine learning Engen to commucation information assocted with the online real-time auction and paragraph [0087], discloses the data processing system can generate the machine learning model using feedback signals …, electronic survey interface at least partially overlaid on content item display with the webpage on the computing device …); .
Arora teaches the above elements including obtaining, by a user interface of the user experience platform, a content provider input including feedback for content distribution campaign (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 , paragraph [0024], discloses the system can futher determine, identify detect or otherwise obtain features assocted with the content item or the impression of the content item , the feature includes e.g. time of day, subject matter of the web page on which the content item is displayed, keywork used to select the content item information about computing device, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input including feedback] , etc.) 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 provided 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]).
Arora and Maher teaches the above elements, Arora futher teaches encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space(Fig. 3A, paragraph [0039], discloses the content sector 130 may select the content item based on network activity information , browsing history information, profile information[provider input ] , etc. , paragraph [0052], discloses select the provider content item 305, 325 and 330 for display with the publisher content [provider input] paragraph [0053], discloses the viewer of content item may receive a prompt to prove feedback for the content item.., displayed as pop-up notification widow 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] )) and the data processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network, linear regression technique, a Bayesian estimator, etc., to generate the model (paragraph [0087]) and Mahar teaches business model used for ad delivery (paragraph [534]). Arora and Maher failed to teach , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space.
However, Rothberg teaches , wherein the multimodal encoder comprises an artificial neural network and the multimodal embedding space comprise a continuous vector space(paragraphs [0019], and [0034], discloses the first encoder comprises a neural network).
Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for processing system can use a statistical model or supervised machine learning model such as, e.g., a neural network of Arora and business model used for delivery of Maher with the encoder and decoder are neural networks) or any other suitable training algorithm of Rothberg in order to allow a neural network to understand relationships and correlation across entirely diffent sensory input.
With respect to claim 18, Arora in view of Maher and futher view of Rothberg 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 and futher view of Rothberg 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 and futher view of Rothberg 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.
Rothberg et al (US Pub., 2019/0347523 A1) discloses techniques for performing a prediction task using a multimodal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.
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 101 rejections with respect to claims 1-20 filed 30 July 2026 have been fully considered but they are not persuasive. Applicants’ arguments of claim 1 is not directed to a certain method of organizing human activity. For example the Office Action’s characterization of claim 1 at pages 3-4 of the Office Action omits at least “encoding, by a multimodal encoder, the content provider input to obtain a feedback embedding in a multimodal embedding space” , “encoding the prompt to obtain a prompt embedding” and “generating, using the machine learning model, the content for the modified content distribution campaign based the prompt embedding” as recited in claim 1. When the limitation of claim 1 are read as a whole, claim 1 does not recite a commercial interaction of advertising, marketings, or sales activities or behaviors, or behaviors, or the management of business relations, as such is not persuasive. The independent claims are directed to managing a content distribution camping, collecting feedback, and generating content are fundamental business and marketing practices, which is purely a certain method of organizing human activity,
The claims also recites encoding... to obtain a feedback embedding," "continuous vector space," and "generating a prompt" are mathematical manipulations of data and algorithms”, which is purely fails into mathematical concept/mental process.
Furthermore, the core of the claim is taking data from two sources, mathematical encoding into embeddings, and using a machine learning model to output new content. Courts frequently hold that collecting data, analyzing it mathematically, and displaying the results is an abstract idea.
Applicants’ arguments of the additional elements of a user interface of a user experience platform, multimodal encoder comprising an artificial neural network that encodes … (see page 16, of remark section) is not persuasive.
The claims merely uses generic, functional computer components ("user experience platform," "multimodal encoder," "artificial neural network," "machine learning model") to perform the abstract idea.
The additional elements Lacks Technological Improvement; the claim does not improve the functioning of the computer itself or the neural network. Instead, it uses existing AI/ML concepts (embeddings, encoders, prompts) to improve the business process of content generation.
Furthermore, the claims and its “inventive concept" evaluated the elements individually and as an ordered combination and found to be directed to an abstract idea.
Generic Elements: Tools like neural networks, multimodal encoders, vector spaces, and machine learning models are considered conventional, generic computer tools in modern software engineering.
Functional Language: The claim describes what the system does (identifying, obtaining, encoding, generating) rather than how the underlying technology is structurally or algorithmically changed to achieve a new technical result.
The ordered combination simply describes the automation of a content creation workflow using standard machine learning techniques, which does not amount to "significantly more" than the abstract idea itself. Therefore, the 35 U.S.C 101 rejections is maintained.
Applicant’s arguments of 35 U.S.C 103 rejection filed on 30 July 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. Applicant further argued that the feedback signals described by Arora are provided by viewers of a display content item in response to the electric survey interface and are not described by Arora as being included in an input provided by a content provider is not persuasive.
While, Arora in paragraph [0021], discloses bid provided by the content provider, when the content item with a lower, or smaller predicted negative when a content item with lower or smaller predicted negative feedback [including feedback] .., adjust the prices using a function of the negative feedback score of both winner and the runner up and paragraph [0039], discloses content provider may provide additional indicators when setting up a content campaign that include content item The content provider may provide information at the content campaign or content group level that the content selector 130 may identify by performing a lookup…, the content select 130 may select the content item based on network activity information, browsing history information, profile information [feedback].., etc., Mahre 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 provided by data warehouse content provider can modify the rules[parameter] 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, the combination of Arora and Mahre addressed the claimed limitation.
Applicants’ arguments of Arora failed to teach “wherein the feedback includes an adjustment to a parameter of the content distribution camping and a request for additional data from second source external to the user experience platform” and instead relies on Mahre to tec limitation is not persuasive. As addresses above, Maher teaches the limitation as recited paragraphs, Furthmore, Mahre in paragraphs [0376]-[0379], discloses ad control includes the following rules [parameter] Adi: Bid a price of 10 cents at dinner time (6:00 to 8:00 pm), otherwise bid 5 cents, cap at 10,000 impressions. Ad2: Bid a price of 6 cents at lunch time (11 :00 to 2:00 pm), otherwise bid 3 cents, cap at 20,000 impressions. [0379] Ad3: Bid a price of 2 cents throughout the day except when near an electronics store then the bid is 9 cents .., and paragraph [0542], dislcies dynamically delivery update control for ads and content advertisers will get real-time feedback on their ad campaign .., modify the rules assocted with an ad dynamically (increase, the minimum bid price). Thus, Maher addressed the claimed limitation.
Applicants’ arguments of Arora and Maher do not teach or suggest II. “encoding by a multimodal encored the content provider input to obtain feedback embedding in a multimodal embedding space is not persuasive. For example, see paragraph [0055], of Arora, the data processing system 120 can provide an electronic survey interface that appears embedded or overlayed on the content item [embedding space] , Arora address the claimed limitation and encoder comprising neural network is address by Rothberg.
Arora and Maher don to teach or suggest III “generating a prompt to a machine learning model based on the data , the additional data, and the feedback.. …” is not persuasive. Arora at least in paragraph [0053], electronic survey interface 310 can be displays as a pop-up, notification window or other user interface elements and may overlay at least a portion of the publisher content or provider content item [generating a prompt…], see paragraph [0034] of applicant’s specification , the content generation system provides a prompt including the feedback and the context, which is within the scope of paragraph [0053] of Arora’s specification.
Applicants’ arguments of Arora and Maher does not teach or suggest IV “generating using the machine learing model the content for the modified content distribution campaign …” is not persuasive. For example, see paragraph [0020] of Arora’s reference, the data processing system can change, adjust, modify or alter an auction score of a content item …, the content items the data processing system expect or predicts to be less annoying, that is get less negative feedback as measure by he survey [modified content]… thus, Arora addressed the claimed limitation.
Applicants’ arguments of V there is no prima facie case that claims 1, 9 and 17 are obvious over Arora and Maher is not persuasive. As indicated above, the examiner proved sufficient prove that the combination of Arora and Maher, thus, the 35 U.S.C 103 rejections with respect to claims 1-20 is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicants are reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicants are reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SABA DAGNEW/Primary Examiner, Art Unit 3682
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