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 the Application
This non-final office action is in response to the preliminary claim amendment filed on 6/20/2025. Claim 1 has been cancelled. Claims 2-21 have been added. Claims 2-21 have been examined in this application. This communication is the first action on the merits.
Claim Rejections – 35 U.S.C. 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.
Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, independent claim 1 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
receiving a request from a client associated with a user account to provide content to the client
retrieving first content associated with the first content platform and second content associated with one or more additional content platforms from the set of content platforms;
determining a subset of the content to present to the user, wherein the subset of the content
comprises at least a portion of the second content from the one or more additional content
platforms;
determining a delivery method for the subset of the content utilizing based on a set of unified cross-platform metrics; and
providing, for display, the subset of the content based on the delivery method.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations recited above describe a method of selecting advertisements from a second platform to display on a first platform based on cross-platform metrics which is an advertising activity that falls within the certain methods or organizing human activities grouping of abstract ideas.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of:
[a request from] a client device;
[provide content to the client] device using a first content platform from a set of content platforms;
standardizing the subset of the content in a format to be used at the first content platform;
[determining a delivery method for the subset of the content] utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics; and
[providing, for display] on the client device [the subset of the content] using the first content platform.
The additional limitations when viewed individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically,
With respect to the limitation [a request from] a client device and [providing, for display] on the client device [the subset of the content] using the first content platform are recited at a high level of generality and merely generally link the abstract idea to a particular technological environment (i.e. a generic client device to display content via a generic content platform).
With respect to the limitation [provide content to the client] device using a first content platform from a set of content platforms, this limitation is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (i.e. a generic content platform to provide content).
With respect to the limitation standardizing the subset of the content in a format to be used at the first content platform, this limitation is recited at a high level of generality. There is no explanation of how the content is standardized or what format is required for the first content platform. At this level of generality this limitation only recites the idea of a solution (standardizing the content) without reciting the steps of performing the solution. See MPEP 2106.05(f)(1)).
Finally, with respect to the limitation [determining a delivery method for the subset of the content] utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics, this limitation is also recited at a very high level of generality. Examiner notes that Applicant’s published specification only discloses training a machine learning model at a high level of generality. See paragraph [0070] which recites “perform training of one or more ML models based on the unified cross-platform metrics in order to, for example, improve optimization of content delivery or predict an optimal placement of content within regions or sections of a content platform.” Neither Applicant’s claims nor specification recite the specific model used, the specific input to the model (only that the training is based on cross-platform metrics generally), the specific output of the model, or how specifically the model is trained. At this level of generality these limitations only recite the idea of a solution (training a generic model to determine a delivery method) without reciting the steps of performing the solution. See MPEP 2106.05(f)(1)). Because the claim limitations attempt to cover any solution to the identified problem (any model that can optimize placement) with no restriction on how the result is accomplished and no description of the specific model for accomplishing the result, these limitations do not integrate the abstract idea into a practical application. Further, Examiner notes that Recentive Analytics, Inc. v. Fox Corp. et al., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) recently held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Here, Examiner takes the position that determining a delivery method utilizing a generic machine learning model trained on generic unified cross platform metrics is the mere application of generic machine learning to a new data environment. Because no improvement to the underlying machine learning models is disclosed, this limitation does not integrate the abstract idea into a practical application.
Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, independent system 9 and non-transitory computer-readable medium claim 16 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1. The system comprising one or more processors of independent claim 9 and the non-transitory computer-readable medium of claim 16 add nothing of substance to the underlying abstract idea. At best, the components in independent claims 9 and 16 merely provide an environment to implement the abstract idea.
Dependent claims 3-8, 10-15, and 17-21 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of independent claims 2, 9, and 16 without significantly more. Specifically, dependent claims 3-8, 10-15, and 17-21 merely further narrow the abstract idea or generally link the abstract idea to a particular technological environment.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 2-5, 8-13, and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number 20230300395 (“Karoui”) in view of US Patent Publication 10311371
(“Hotchkies”)
Claim 2
As per claim 2, Karoui teaches a computer-implemented method:
receiving a request from a client device associated with a user account to provide content to the client device using a first content platform from a set of content platforms ([0003] “determining a request to present one or more content items.” And, [0084] “client application can request a new information insert or advertisement from the original information serving platform.” And, [0031] “client application 115 executing at the UE.” And, [0047] “the client application has connectivity to a dynamic advertising management platform” and “advertising management platform enables connectivities to respective multiple advertising serving platform.” And, [0037] “the client application interacts with the content aggregating service, the service providers, the content serving platforms, the information serving platforms, and/or the content management platforms through the dynamic information management platform.” And, [0061] “profile information, etc. associated with the UE or a user.”);
retrieving first content associated with the first content platform and second content associated with one or more additional content platforms from the set of content platforms ([0027] “managing content and associated information sourced from multiple platforms . . . access multiple content items from multiple service providers.” And, [0093] “content and associated information sourced from multiple platforms.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.”);
determining a subset of the content to present to the user, wherein the subset of the content comprises at least a portion of the second content from the one or more additional content platforms ([0066] “one or more content items using inventory available . . . select content from any of the service providers integrated into the content aggregating service.” And, [0071] “a content item is selected.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.” Examiner interprets the content selected as the subset of content.);
standardizing the subset of the content in a format to be used at the first content platform ([0066] “the content aggregating service via, for instance, the client application to select content from any of the service providers integrated into the content aggregating service.”); And, [0070] “[o]nce the content and associated information are cached, downloaded, or streamed, the dynamic information management platform optionally determines at least one format or rule for presenting the information based, at least in part, on . . . the at least one platform , . . . For example, the format may include a standardized format such as the Digital Video Ad Serving Template (VAST) format, or may include any other format compatible with . . . the client application . . . the dynamic information management system may transcode the information or advertisement from a format provided by the information serving platform to a format compatible with the client application.”);
providing, for display on the client device, the subset of the content using the first content platform ([0033] “information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.” And, [0070] “presenting the information based . . . on . . . the at least one platform.” And, [0079] “the client application plays or presents the information inserts or advertisements directly from the information serving platform.” And, see Figures 7A and 7B. And, [0033] “the information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.”);
Karoui does not explicitly teach but Hotchkies teaches:
determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 4, lines 43-50] “collect or derive . . . the content delivery performance metrics . . . may include user-specific valuations, such as a purchase propensity corresponding to a likelihood that a user makes a related purchase (including its associated timing, type, amount, frequency, etc.) after receiving a response to the user's content request.”).
Karoui does not explicitly teach but Hotchkies teaches:
[providing the subset of content] based on the delivery method ([col. 5, lines 8-10] “[i]n accordance with the content delivery strategy, the content delivery management service may transmit a response to the content requesting device.” And, [col. 9, lines 15-16] “determining and implementing optimized content delivery strategy in response to content requests based on machine learning models.” And, [col. 34, lines 5-13] “rendering responses to content requests; determining an ATF configuration for rendering a target response on a computing device associated with a user based, at least in part, on the machine learning model, wherein the target response corresponds to a target content request of the user.”).
Therefore, it would have been obvious to modify Karoui to include determining a subset of the content to present to the user as taught by Hotchkies to include determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics [providing the subset content] based on the delivery method as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 3
As per claim 3, Karoui does not explicitly teach but Hotchkies teaches:
wherein the set of unified cross-platform metrics comprises user interactions with content at a content platform ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data)”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the set of unified cross-platform metrics comprises user interactions with content at a content platform as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 4
As per claim 4, Karoui does not explicitly teach but Hotchkies teaches:
wherein the user interactions comprise clicks, impressions, or conversions ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data)”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the user interactions comprise clicks, impressions, or conversions as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 5
As per claim 5, Karoui further teaches:
wherein the subset of the content further comprises at least a portion of the first content from the first content platform ([0066] “one or more content items using inventory available . . . select content from any of the service providers integrated into the content aggregating service.” And, [0071] “a content item is selected.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.”).
Claim 8
As per claim 8, Karoui does not explicitly teach but Hotchkies teaches:
wherein the determining the delivery method for the subset of content utilizing the trained ML model comprises providing the trained ML model with user interaction information from the first content platform and the one or more additional content platforms ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data)” And, [col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 4, lines 43-50] “collect or derive . . . the content delivery performance metrics . . . may include user-specific valuations, such as a purchase propensity corresponding to a likelihood that a user makes a related purchase (including its associated timing, type, amount, frequency, etc.) after receiving a response to the user's content request.” And, [col. 23, lines 28-45] “interactions to one or more content providers.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the determining the delivery method for the subset of content utilizing the trained ML model comprises providing the trained ML model with user interaction information from the first content platform and the one or more additional content platforms as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 9
As per claim 9, Karoui teaches a system comprising:
one or more processors ([0007] “processor can be coupled to the memory.”);
a memory coupled to the one or more processors, wherein the memory includes instructions executable by the one or more processors to ([0007] “processor can be coupled to the memory.”);
responsive to a request from a client device associated with a user account, retrieve first content associated with a first content platform and second content associated with one or more additional content platforms ([0003] “determining a request to present one or more content items.” And, [0084] “client application can request a new information insert or advertisement from the original information serving platform.” And, [0031] “client application 115 executing at the UE.” And, [0047] “the client application has connectivity to a dynamic advertising management platform” and “advertising management platform enables connectivities to respective multiple advertising serving platform.” And, [0037] “the client application interacts with the content aggregating service, the service providers, the content serving platforms, the information serving platforms, and/or the content management platforms through the dynamic information management platform.” And, [0061] “profile information, etc. associated with the UE or a user.” And, [0027] “managing content and associated information sourced from multiple platforms . . . access multiple content items from multiple service providers.” And, [0093] “content and associated information sourced from multiple platforms.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.”);
determine a content subset to present to the user, wherein the content subset comprises at least a portion of the second content ([0066] “one or more content items using inventory available . . . select content from any of the service providers integrated into the content aggregating service.” And, [0071] “a content item is selected.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.” Examiner interprets the content selected as the content subset.);
providing, for display on the client device, the content subset using the first content platform ([0033] “information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.” And, [0070] “presenting the information based . . . on . . . the at least one platform.” And, [0079] “the client application plays or presents the information inserts or advertisements directly from the information serving platform.” And, see Figures 7A and 7B. And, [0033] “the information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.”);
Karoui does not explicitly teach but Hotchkies teaches:
determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 4, lines 43-50] “collect or derive . . . the content delivery performance metrics . . . may include user-specific valuations, such as a purchase propensity corresponding to a likelihood that a user makes a related purchase (including its associated timing, type, amount, frequency, etc.) after receiving a response to the user's content request.”).
Karoui does not explicitly teach but Hotchkies teaches:
[providing the content subset] based on the delivery method ([col. 5, lines 8-10] “[i]n accordance with the content delivery strategy, the content delivery management service may transmit a response to the content requesting device.” And, [col. 9, lines 15-16] “determining and implementing optimized content delivery strategy in response to content requests based on machine learning models.” And, [col. 34, lines 5-13] “rendering responses to content requests; determining an ATF configuration for rendering a target response on a computing device associated with a user based, at least in part, on the machine learning model, wherein the target response corresponds to a target content request of the user.”).
Therefore, it would have been obvious to modify Karoui to include the coupon placement location within the ad unit may be optimally selected to be the most effective for leading to higher coupon clipping rate.to include determining a subset of the content to present to the user as taught by Karoui to include determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics [providing the subset content] based on the delivery method as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 10
As per claim 10, Karoui does not explicitly teach but Hotchkies teaches:
determine the delivery method for the subset of content utilizing the trained ML model by:
determining prioritized content using the trained ML model based on interaction information from the one or more additional content platforms ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 6, lines 34-29] “content delivery management service may further assign priorities for retrieval and rendering of various resources embedded in a response, so that portions of content that likely interest users most can be presented first in the ATF.” And, [col. 26, lines 54-57] “retrieves embedded resources from the content provider or CDN service provider based on their associated priorities and renders or displays content in accordance with the ATF configuration.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include determine the delivery method for the subset of content utilizing the trained ML model by: determining prioritized content using the trained ML model based on interaction information from the one or more additional content platforms as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 11
As per claim 11, Karoui does not explicitly teach but Hotchkies teaches:
determine the delivery method for the subset of content utilizing the trained ML model by:
merging user interaction information from the first content platform and the one or more additional content platforms; and determining prioritized content using the trained ML model based on the merged user interaction information ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 6, lines 34-29] “content delivery management service may further assign priorities for retrieval and rendering of various resources embedded in a response, so that portions of content that likely interest users most can be presented first in the ATF.” And, [col. 26, lines 54-57] “retrieves embedded resources from the content provider or CDN service provider based on their associated priorities and renders or displays content in accordance with the ATF configuration.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include determine the delivery method for the subset of content utilizing the trained ML model by: merging user interaction information from the first content platform and the one or more additional content platforms; and determining prioritized content using the trained ML model based on the merged user interaction information as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 12
As per claim 12, Karoui does not explicitly teach but Hotchkies teaches:
wherein the set of unified cross-platform metrics comprises user interactions with content at the first content platform and the one or more additional content platforms ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data)”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the set of unified cross-platform metrics comprises user interactions with content at the first content platform and the one or more additional content platforms as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 13
As per claim 13, Karoui does not explicitly teach but Hotchkies teaches:
wherein the memory further includes instructions executable by the one or more processors to determine the delivery method for the subset of content utilizing the trained ML model by determining a prioritization of the subset of content ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 6, lines 34-29] “content delivery management service may further assign priorities for retrieval and rendering of various resources embedded in a response, so that portions of content that likely interest users most can be presented first in the ATF.” And, [col. 26, lines 54-57] “retrieves embedded resources from the content provider or CDN service provider based on their associated priorities and renders or displays content in accordance with the ATF configuration.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the memory further includes instructions executable by the one or more processors to determine the delivery method for the subset of content utilizing the trained ML model by determining a prioritization of the subset of content as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 16
As per claim 16, Karoui teaches a non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to:
receive a request from a client device associated with a user account to provide content to the client device using a first content platform; ([0003] “determining a request to present one or more content items.” And, [0084] “client application can request a new information insert or advertisement from the original information serving platform.” And, [0031] “client application 115 executing at the UE.” And, [0047] “the client application has connectivity to a dynamic advertising management platform” and “advertising management platform enables connectivities to respective multiple advertising serving platform.” And, [0037] “the client application interacts with the content aggregating service, the service providers, the content serving platforms, the information serving platforms, and/or the content management platforms through the dynamic information management platform.” And, [0061] “profile information, etc. associated with the UE or a user.”);
retrieve first content associated with the first content platform and second content associated with one or more additional content platforms ([0027] “managing content and associated information sourced from multiple platforms . . . access multiple content items from multiple service providers.” And, [0093] “content and associated information sourced from multiple platforms.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.”);
determine a content subset to present to the user, wherein the content subset comprises at least a portion of the first content and the second content ([0066] “one or more content items using inventory available . . . select content from any of the service providers integrated into the content aggregating service.” And, [0071] “a content item is selected.” And, [0056] “client application includes one or more components for receiving and presenting information or advertisements to a user . . . receives information inserts (e.g., advertising data) from . . . the information serving platforms.” Examiner interprets the content selected as the subset of content.);
provide, for display on the client device, the content subset using the first content platform ([0033] “information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.” And, [0070] “presenting the information based . . . on . . . the at least one platform.” And, [0079] “the client application plays or presents the information inserts or advertisements directly from the information serving platform.” And, see Figures 7A and 7B. And, [0033] “the information serving platforms deliver information or other content that can be inserted into the content provided by the content serving platforms.”);
Karoui does not explicitly teach but Hotchkies teaches:
utilize a trained machine learning (ML) model trained based on a set of unified cross-platform metrics to determine a delivery method for the content subset ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 4, lines 43-50] “collect or derive . . . the content delivery performance metrics . . . may include user-specific valuations, such as a purchase propensity corresponding to a likelihood that a user makes a related purchase (including its associated timing, type, amount, frequency, etc.) after receiving a response to the user's content request.”).
Karoui does not explicitly teach but Hotchkies teaches:
[providing the subset of content] based on the delivery method ([col. 5, lines 8-10] “[i]n accordance with the content delivery strategy, the content delivery management service may transmit a response to the content requesting device.” And, [col. 9, lines 15-16] “determining and implementing optimized content delivery strategy in response to content requests based on machine learning models.” And, [col. 34, lines 5-13] “rendering responses to content requests; determining an ATF configuration for rendering a target response on a computing device associated with a user based, at least in part, on the machine learning model, wherein the target response corresponds to a target content request of the user.”).
Therefore, it would have been obvious to modify the Karoui to include determining a subset of the content to present to the user as taught by Karoui to include determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics [providing the subset content] based on the delivery method as taught by Hotchkies in order to “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 17
As per claim 17, Karoui does not explicitly teach but Hotchkies teaches:
cause the at least one processor to train the ML model based on the set of unified cross-platform metrics ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include determining a subset of the content to present to the user as taught by Karoui to include determining a delivery method for the subset of the content utilizing a trained machine learning (ML) model trained based on a set of unified cross-platform metrics [providing the subset content] based on the delivery method as taught by Hotchkies in order to “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 18
As per claim 18, Karoui does not explicitly teach but Hotchkies teaches:
train the ML model based on user interactions observed at least one of: i) the first content platform, and ii) the one or more additional content platform ([claim 48] “model is trained on user interaction data.” And, “user interaction data (e.g., scrolling, dwelling, or clicking actions on Web pages or applications, browsing history, searching history, purchase history, product review history, user preference or setting history, or user location data.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.”).
Karoui does not explicitly teach but Hotchkies teaches:
use the trained ML model to determine prioritized content ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 6, lines 34-29] “content delivery management service may further assign priorities for retrieval and rendering of various resources embedded in a response, so that portions of content that likely interest users most can be presented first in the ATF.” And, [col. 26, lines 54-57] “retrieves embedded resources from the content provider or CDN service provider based on their associated priorities and renders or displays content in accordance with the ATF configuration.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include train the ML model based on user interactions observed at least one of: i) the first content platform, and ii) the one or more additional content platform and use the trained ML model to determine prioritized content as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 19
As per claim 19, Karoui does not explicitly teach but Hotchkies teaches:
when executed by at least one processor, cause the at least one processor to determine the delivery method for the content subset utilizing the trained ML model and based on user interaction information from the first content platform and the one or more additional content platforms ([claim 48] “model is trained on user interaction data.” And, [col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 4, lines 43-50] “collect or derive . . . the content delivery performance metrics . . . may include user-specific valuations, such as a purchase propensity corresponding to a likelihood that a user makes a related purchase (including its associated timing, type, amount, frequency, etc.) after receiving a response to the user's content request.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include when executed by at least one processor, cause the at least one processor to determine the delivery method for the content subset utilizing the trained ML model and based on user interaction information from the first content platform and the one or more additional content platforms as taught by Hotchkies in order to create a “content delivery strategy . . . that is predicted be associated with optimized performance in accordance with a criterion or metric” ([col. 12, lines 63-67]).
Claim 20
As per claim 20, Karoui does not explicitly teach but Hotchkies teaches:
determine the delivery method for the content subset utilizing the trained ML model by determining prioritized content ([col. 23, lines 28-45] “the content delivery management service may feed the model with at least some known components of a candidate content delivery strategy as another part of the input. The model will then generate the one or more performance predictions for applying the candidate strategy in response to the incoming content request. The content delivery management service may apply the model to the incoming content request in conjunction with a number of candidate strategies, compare the predictions made by the model with respect to each candidate strategy, and select a strategy that yields a best predicted performance metric that the content provider is set to optimize.” And, [col. 4, lines 19-23] “The model can be a supervised learning model (e.g., a decision tree or artificial neural network) trained on historical data related to the processing of content requests and corresponding content delivery performance.” And, [col. 6, lines 34-29] “content delivery management service may further assign priorities for retrieval and rendering of various resources embedded in a response, so that portions of content that likely interest users most can be presented first in the ATF.” And, [col. 26, lines 54-57] “retrieves embedded resources from the content provider or CDN service provider based on their associated priorities and renders or displays content in accordance with the ATF configuration.”).
Claim 21
As per claim 21, Karoui further teaches:
cause the at least one processor to standardize the content subset in a format to be used at the first content platform ([0066] “the content aggregating service via, for instance, the client application to select content from any of the service providers integrated into the content aggregating service.”); And, [0070] “[o]nce the content and associated information are cached, downloaded, or streamed, the dynamic information management platform optionally determines at least one format or rule for presenting the information based, at least in part, on . . . the at least one platform , . . . For example, the format may include a standardized format such as the Digital Video Ad Serving Template (VAST) format, or may include any other format compatible with . . . the client application . . . the dynamic information management system may transcode the information or advertisement from a format provided by the information serving platform to a format compatible with the client application.”).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number 20230300395 (“Karoui”) in view of US Patent Publication 10311371
(“Hotchkies”) as applied to claim 2 above, and in further view of US Patent Application Publication Number 20160055548 (“Vaysman”).
Claim 6
As per claim 6, Karoui does not explicitly teach but Vaysman teaches:
wherein determining the delivery method for the subset of content utilizing the trained ML model comprises determining a placement of the subset of content ([0063] “employ machine learning to identify optimal coupon placement locations for an ad unit . . . the coupon placement location within the ad unit may be optimally selected to be the most effective for leading to higher coupon clipping rate.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein determining the delivery method for the subset of content utilizing the trained ML model comprises determining a placement of the subset of content as taught by Vaysman so that “the coupon placement location within the ad unit may be optimally selected to be the most effective for leading to higher coupon clipping rate” (Vaysman [0063]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number 20230300395 (“Karoui”) in view of US Patent Publication 10311371
(“Hotchkies”) as applied to claim 2 above, and in further view of US Patent Application Publication Number 20200160373 (“Thimmaiah”).
Claim 7
As per claim 7, Karoui does not explicitly teach but Thimmaiah teaches:
wherein determining the delivery method for the subset of content utilizing the trained ML model comprises determining a minimal loss and an optimal use of one or more specified resources ([0038] “automatically optimizing sponsored content campaigns for a sponsored content provider for a particular consumption category across different content publisher networks.” And, [0055] “allocate an advertising budget to one or more audiences across one or more content publisher networks, budget optimizer can be used to allocate the budget across one or more content publisher networks based on the allocation scores.” And, [0055] “The performance of the campaign may be monitored, and the amount allocated to target audiences may be adjusted to optimize the effectiveness of the campaign. For instance, if the initial audience does not result in a positive return on investment (or if a better return on investment is predicted if more money is spent on another audience), money in the budget may be reallocated to audiences expected to perform better regarding return on investment.” Examiner notes that allocating the budget across multiple content publisher networks to maximize return on investment minimizes spending loss and is an optimal use of the available spending limits.).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein determining the delivery method for the subset of content utilizing the trained ML model comprises determining a minimal loss and an optimal use of one or more specified resources as taught by Thimmaiah in order to “optimize the effectiveness of the campaign” (Thimmaiah [0055]) and so that “efficient and cost conscience interactions can be accomplished” (Thimmaiah [0039]).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number 20230300395 (“Karoui”) in view of US Patent Publication 10311371
(“Hotchkies”) as applied to claim 9 above, and in further view of US Patent Application Publication Number 20220408155 (“Foyle”).
Claim 14
As per claim 14, Karoui does not explicitly teach but Foyle teaches:
wherein the memory further includes instructions executable by the one or more processors to determine the content subset by sorting the first content and the second content by a quality score or a utility score ([0054] “Pertinence indicators may be automatically updated based on the analysis of historic performance of associated items of media content “ And, [0011] “quality prediction score for items of media content is determined based on values of pertinence indicators for the media content.” And, [0068] “create a subset of items of media content that comprises at least one item of media content having quality prediction score higher than the determined threshold.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the memory further includes instructions executable by the one or more processors to determine the content subset by sorting the first content and the second content by a quality score or a utility score as taught by Foyle because “determining the threshold and further the subset of items of media content reduces time for curation of desirable media content by automatically selecting media content having high quality prediction score that apprises a quality thereof” and “[h]enceforth, user operating the user device is provided with the subset of items of media content comprising best quality media content that can be used as a powerful endorsement for products, services, brands” (Foyle [0068]) increasing the advertisement effectiveness and improving the cross-platform metrics
Claim 15
As per claim 14, Karoui does not explicitly teach but Foyle teaches:
wherein the memory further includes instructions executable by the one or more processors to determine the content subset based at least in part on user data, contextual data or historical data ([0054] “Pertinence indicators may be automatically updated based on the analysis of historic performance of associated items of media content “ And, [0011] “quality prediction score for items of media content is determined based on values of pertinence indicators for the media content.” And, [0068] “create a subset of items of media content that comprises at least one item of media content having quality prediction score higher than the determined threshold.”).
Therefore, it would have been obvious to modify the combination of Karoui and Hotchkies to include wherein the memory further includes instructions executable by the one or more processors to determine the content subset based at least in part on user data, contextual data or historical data as taught by Foyle because “determining the threshold and further the subset of items of media content reduces time for curation of desirable media content by automatically selecting media content having high quality prediction score that apprises a quality thereof” and “[h]enceforth, user operating the user device is provided with the subset of items of media content comprising best quality media content that can be used as a powerful endorsement for products, services, brands” (Foyle [0068]) increasing the advertisement effectiveness and improving the cross-platform metrics.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication Number 20180315061 (“Brown”) discloses collecting data from a plurality of platforms and utilizing APIs that enable application developers to communicate interaction events that represent user activity to a measurement engine.
US Patent Application Publication Number 20210357952 (“Liu”) discloses estimating an interaction rate with delivered content using a machine learning model
US Patent Application Publication Number 20180012250 (“Malca”) discloses optimizing advertising content placement using machine learning algorithms
US Patent Publication Number 9699265 (“Sahota”) discloses combining content from multiple platforms
US Patent Application Publication Number 20220272420 (“Tucker”) discloses a content transition platform may be configured to transition an active communication session from a first user device to a second user device.
US Patent Application Publication Number 20150142865 (“Shimizu”) discloses storing anonymous personal data for providing a service without requesting a user ID or a password and providing the service to the user, to thereby eliminate the need for the user to perform an operation for user registration.
US Patent Application Publication Number 9953063 (“Spasojevic”) discloses collecting highly relevant content from sources and improving relevance machine learned models, in order to produce an optimized content for users.
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
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