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 .
Response to Amendment
The Amendment filed 05/19/2026 has been entered. Claims 1-20 remain pending in the application.
Allowable Subject Matter
Claim 8, 16+spec is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. "wherein updating the machine learning model comprises: [claim 8] according to a predefined masking value” (see pgpub ¶63)
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.
Claims 1-7, 9-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over TSOY et al. (US 20220327134 A1 hereinafter Tsoy) in view of Cetin et al. (US 20110246286 A1 Cetin) and
As to independent claim 1, Tsoy teaches a method, performed at a computer system comprising a processor and a computer-readable medium, comprising: [processor and memory ¶25]
accessing a machine learning model trained to predict a likelihood of a target event given a display of a content item by an online system to a user in a presentation context of a plurality of different presentation contexts, wherein the machine learning model is trained by: [accesses an ML algorithm (model) to predict a score used as a likelihood of interaction (event) ¶10, ¶99 " A relevance score indicating a predicted likelihood that the user will interact with a content element may be determined for each of the content elements 301-05"], [dataset includes ranking and positioning presentation contexts ¶105]
obtaining a plurality of training examples, wherein each training example comprises a context-dependent set of training features associated with a previous display of a content item and a label indicating whether the target event occurred, [records of previous interactions used for training with labels including view events and attributes ¶105, ¶10 "Records of previous user interactions with interfaces displaying content elements may be used as training data for the MLA. A training dataset may be generated, where each content element in the training dataset is assigned a label."… "a number of times the content element has been viewed, a user rating corresponding to the content element, and/or any other attributes of the content element"]
updating the machine learning model, for each of the training examples, based on an error between a prediction by the machine learning model of whether the target event occurred and a label indicating whether the target event occurred; [adjusts/retrains MLA (updates) based on a difference between score and label (error, how off it was) ¶136-137 "The predicted relevance score may then be compared to the label for the content element that was determined at step 540. A difference between the predicted relevance score and the label may be determined, and the MLA may be adjusted based on the difference."]
obtaining a set of input features associated with an opportunity to present content by the online system to a viewing user in a presentation context of the opportunity; [Fig. 6 605 receives content to use as input for a user output ¶138-147 " set of content elements may be received. Any number and/or type of content elements may be received"]
applying the machine learning model to the obtained set of input features to predict a likelihood of the target event given a display of a candidate content item by the online system to the viewing user in the presentation context; [predicts according to received content and ranks for positioning using MLA Fig. 6 610 ¶143-145 " a relevance score may be predicted for each of the content elements received at step 605."]
generating a user interface in the presentation context that selectively includes the candidate content item based on the predicted likelihood; and [Fig. 6 620 step for generating interface accordingly ¶147 "an interface with the content elements may be output, such as the interface 300. The interface may display the content elements in their ranked order"]
sending the user interface to a device of the viewing user, the sending causing the device of the viewing user to display the user interface. [user can view and scroll interface accordingly ¶147, ¶7]
Tsoy does not specifically teach wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context.
However, Cetin teaches wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and [features are based on presentation context (query, add, user and location) ¶30 "features 308 may be derived using any information available in the context of the search, including the query, ad, user, and location"]
wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context, and [features are derived according to context information (context dependent) including unknown/missing (inapplicable) features Fig. 4, ¶30 "The predictor may have certain features or data available for calculating a click probability. For each advertisement and/or query, there may be features that are unknown and may need to be estimated for accurately determining the click probability. In one example, a new ad that has never been shown would not have any past CTR information and would have several missing features. The click-prediction features 308 may be derived using any information available in the context of the search"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy by incorporating the wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context disclosed by Cetin because both techniques address the same field of machine learning and by incorporating Cetin into Tsoy provides models with more accurate and reliable predictions in different environments [Cetin ¶10]
Tsoy and Cetin do not specifically teach wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features.
However, Sabharwal teaches wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features [trains using examples from different contexts (search ranking, item recommendations and digital components) and sources (sets) which are joined see Fig. 2 132 with common ids and data sets ¶46 " joins in features from different feature sources obtained from the database "; ¶26 "a machine learning (ML) feature data pipeline that is shared among various product areas or teams of an electronic platform (e.g. website), such as search ranking, item recommendation, and digital components (e.g. advertisements)."].
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy and Cetin by incorporating the wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features disclosed by Sabharwal because all techniques address the same field of data machine learning and by incorporating Sabharwal into Tsoy and Cetin alleviates the time and computational intensity of machine learning pipelines [Sabharwal ¶3].
As to dependent claim 2, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context comprises a search interface, and the second presentation context comprises a browsing page. [Cetin search results ¶12 browser ¶13], [Tsoy content feed Fig. 3 (browsing page) ¶97]
As to dependent claim 3, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context and the second presentation context generate different input features. [Cetin query and search context driven features ¶30, ¶35]
As to dependent claim 4, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the plurality of training examples features comprises deriving the context dependent sets of training features from historically observed data associated with presenting content in at least the first presentation context and the second presentation context. [Tsoy records of previous interactions used for training ¶105, ¶10]
As to dependent claim 5, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the plurality of training examples features comprises deriving the context-dependent sets of training features are from one or more of: user data, item data, event occurrences, and scoring metrics associated with historical orders of the online system. [Cetin item data (ad data) and records with impressions, clicks (occurrences, metrics) ¶16]
As to dependent claim 6, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the set of input features comprises masking a subset of the input features relating to data that is inapplicable to the presentation context. [Cetin unknown/missing (inapplicable) features can be imputed ¶30, ¶37 " In cases with missing data, the generative model infers a posterior distribution over the missing values, and imputes them"]
As to dependent claim 7, the rejection of claim 1 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the target event comprises a selection of the candidate content item. [Tsoy predict a score used as a likelihood of interaction (event) ¶10, ¶99 " A relevance score indicating a predicted likelihood that the user will interact with a content element may be determined for each of the content elements 301-05"], [dataset includes ranking and location contexts ¶105]
As to independent claim 9, Tsoy teaches a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing steps including: [processor, memory and instruction ¶25]
accessing a machine learning model trained to predict a likelihood of a target event given a display of a content item by an online system to a user in a presentation context of a plurality of different presentation contexts, wherein the machine learning model is trained by: [accesses an ML algorithm (model) to predict a score used as a likelihood of interaction (event) ¶10, ¶99 " A relevance score indicating a predicted likelihood that the user will interact with a content element may be determined for each of the content elements 301-05"], [dataset includes ranking and positioning presentation contexts ¶105]
obtaining a plurality of training examples, wherein each training example comprises a context-dependent set of training features associated with a previous display of a content item and a label indicating whether the target event occurred, [records of previous interactions used for training with labels including view events and attributes ¶105, ¶10 "Records of previous user interactions with interfaces displaying content elements may be used as training data for the MLA. A training dataset may be generated, where each content element in the training dataset is assigned a label."… "a number of times the content element has been viewed, a user rating corresponding to the content element, and/or any other attributes of the content element"]
updating the machine learning model, for each of the training examples, based on an error between a prediction by the machine learning model of whether the target event occurred and a label indicating whether the target event occurred; [adjusts/retrains MLA (updates) based on a difference between score and label (error, how off it was) ¶136-137 "The predicted relevance score may then be compared to the label for the content element that was determined at step 540. A difference between the predicted relevance score and the label may be determined, and the MLA may be adjusted based on the difference."]
obtaining a set of input features associated with an opportunity to present content by the online system to a viewing user in a presentation context of the opportunity; [Fig. 6 605 receives content to use as input for a user output ¶138-147 " set of content elements may be received. Any number and/or type of content elements may be received"]
applying the machine learning model to the obtained set of input features to predict a likelihood of the target event given a display of a candidate content item by the online system to the viewing user in the presentation context; [predicts according to received content and ranks for positioning using MLA Fig. 6 610 ¶143-145 " a relevance score may be predicted for each of the content elements received at step 605."]
generating a user interface in the presentation context that selectively includes the candidate content item based on the predicted likelihood; and [Fig. 6 620 step for generating interface accordingly ¶147 "an interface with the content elements may be output, such as the interface 300. The interface may display the content elements in their ranked order"]
sending the user interface to a device of the viewing user, the sending causing the device of the viewing user to display the user interface. [user can view and scroll interface accordingly ¶147, ¶7]
Tsoy does not specifically teach wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context.
However, Cetin teaches wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and [features are based on presentation context (query, add, user and location) ¶30 "features 308 may be derived using any information available in the context of the search, including the query, ad, user, and location"]
wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context, and [features are derived according to context information (context dependent) including unknown/missing (inapplicable) features Fig. 4, ¶30 "The predictor may have certain features or data available for calculating a click probability. For each advertisement and/or query, there may be features that are unknown and may need to be estimated for accurately determining the click probability. In one example, a new ad that has never been shown would not have any past CTR information and would have several missing features. The click-prediction features 308 may be derived using any information available in the context of the search"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy by incorporating the wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context disclosed by Cetin because both techniques address the same field of machine learning and by incorporating Cetin into Tsoy provides models with more accurate and reliable predictions in different environments [Cetin ¶10]
Tsoy and Cetin do not specifically teach wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features.
However, Sabharwal teaches wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features [trains using examples from different contexts (search ranking, item recommendations and digital components) and sources (sets) which are joined see Fig. 2 132 with common ids and data sets ¶46 " joins in features from different feature sources obtained from the database "; ¶26 "a machine learning (ML) feature data pipeline that is shared among various product areas or teams of an electronic platform (e.g. website), such as search ranking, item recommendation, and digital components (e.g. advertisements)."].
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy and Cetin by incorporating the wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features disclosed by Sabharwal because all techniques address the same field of data machine learning and by incorporating Sabharwal into Tsoy and Cetin alleviates the time and computational intensity of machine learning pipelines [Sabharwal ¶3]
As to dependent claim 10, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context comprises a search interface, and the second presentation context comprises a browsing page. [Cetin search results ¶12 browser ¶13], [Tsoy content feed Fig. 3 (browsing page) ¶97]
As to dependent claim 11, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context and the second presentation context generate different input features. [Cetin query and search context driven features ¶30, ¶35]
As to dependent claim 12, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the plurality of training examples features comprises deriving the context dependent sets of training features from historically observed data associated with presenting content in at least the first presentation context and the second presentation context. [Tsoy records of previous interactions used for training ¶105, ¶10]
As to dependent claim 13, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the plurality of training examples features comprises deriving the context-dependent sets of training features are from one or more of: user data, item data, event occurrences, and scoring metrics associated with historical orders of the online system. [Cetin item data (ad data) and records with impressions, clicks (occurrences, metrics) ¶16]
As to dependent claim 14, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the set of input features comprises masking a subset of the input features relating to data that is inapplicable to the presentation context. [Cetin unknown/missing (inapplicable) features can be imputed ¶30, ¶37 " In cases with missing data, the generative model infers a posterior distribution over the missing values, and imputes them"]
As to dependent claim 15, the rejection of claim 9 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the target event comprises a selection of the candidate content item. [Tsoy predict a score used as a likelihood of interaction (event) ¶10, ¶99 " A relevance score indicating a predicted likelihood that the user will interact with a content element may be determined for each of the content elements 301-05"], [dataset includes ranking and location contexts ¶105]
As to independent claim 17, Tsoy teaches a system, comprising: one or more processors; and a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing steps including:[system, processor, memory and instruction ¶25]
accessing a machine learning model trained to predict a likelihood of a target event given a display of a content item by an online system to a user in a presentation context of a plurality of different presentation contexts, wherein the machine learning model is trained by: [accesses an ML algorithm (model) to predict a score used as a likelihood of interaction (event) ¶10, ¶99 " A relevance score indicating a predicted likelihood that the user will interact with a content element may be determined for each of the content elements 301-05"], [dataset includes ranking and positioning presentation contexts ¶105]
obtaining a plurality of training examples, wherein each training example comprises a context-dependent set of training features associated with a previous display of a content item and a label indicating whether the target event occurred, [records of previous interactions used for training with labels including view events and attributes ¶105, ¶10 "Records of previous user interactions with interfaces displaying content elements may be used as training data for the MLA. A training dataset may be generated, where each content element in the training dataset is assigned a label."… "a number of times the content element has been viewed, a user rating corresponding to the content element, and/or any other attributes of the content element"]
updating the machine learning model, for each of the training examples, based on an error between a prediction by the machine learning model of whether the target event occurred and a label indicating whether the target event occurred; [adjusts/retrains MLA (updates) based on a difference between score and label (error, how off it was) ¶136-137 "The predicted relevance score may then be compared to the label for the content element that was determined at step 540. A difference between the predicted relevance score and the label may be determined, and the MLA may be adjusted based on the difference."]
obtaining a set of input features associated with an opportunity to present content by the online system to a viewing user in a presentation context of the opportunity; [Fig. 6 605 receives content to use as input for a user output ¶138-147 " set of content elements may be received. Any number and/or type of content elements may be received"]
applying the machine learning model to the obtained set of input features to predict a likelihood of the target event given a display of a candidate content item by the online system to the viewing user in the presentation context; [predicts according to received content and ranks for positioning using MLA Fig. 6 610 ¶143-145 " a relevance score may be predicted for each of the content elements received at step 605."]
generating a user interface in the presentation context that selectively includes the candidate content item based on the predicted likelihood; and [Fig. 6 620 step for generating interface accordingly ¶147 "an interface with the content elements may be output, such as the interface 300. The interface may display the content elements in their ranked order"]
sending the user interface to a device of the viewing user, the sending causing the device of the viewing user to display the user interface. [user can view and scroll interface accordingly ¶147, ¶7]
Tsoy does not specifically teach wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context.
However, Cetin teaches wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and [features are based on presentation context (query, add, user and location) ¶30 "features 308 may be derived using any information available in the context of the search, including the query, ad, user, and location"]
wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context, and [features are derived according to context information (context dependent) including unknown/missing (inapplicable) features Fig. 4, ¶30 "The predictor may have certain features or data available for calculating a click probability. For each advertisement and/or query, there may be features that are unknown and may need to be estimated for accurately determining the click probability. In one example, a new ad that has never been shown would not have any past CTR information and would have several missing features. The click-prediction features 308 may be derived using any information available in the context of the search"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy by incorporating the wherein the context-dependent set of training features associated with a first presentation context has at least one training feature in common with the context-dependent set of training features associated with a second presentation context, and wherein the context-dependent set of training features associated with the first presentation context has at least one training feature that is inapplicable to the context-dependent set of training features associated with the second presentation context disclosed by Cetin because both techniques address the same field of machine learning and by incorporating Cetin into Tsoy provides models with more accurate and reliable predictions in different environments [Cetin ¶10]
Tsoy and Cetin do not specifically teach wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features.
However, Sabharwal teaches wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features [trains using examples from different contexts (search ranking, item recommendations and digital components) and sources (sets) which are joined see Fig. 2 132 with common ids and data sets ¶46 " joins in features from different feature sources obtained from the database "; ¶26 "a machine learning (ML) feature data pipeline that is shared among various product areas or teams of an electronic platform (e.g. website), such as search ranking, item recommendation, and digital components (e.g. advertisements)."].
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Tsoy and Cetin by incorporating the wherein the obtained plurality of training examples comprises training examples associated with the first presentation context having a first context-dependent set of training features and training examples associated with the second presentation context having a second context-dependent set of training features different from the first context-dependent set of training features disclosed by Sabharwal because all techniques address the same field of data machine learning and by incorporating Sabharwal into Tsoy and Cetin alleviates the time and computational intensity of machine learning pipelines [Sabharwal ¶3]
As to dependent claim 18, the rejection of claim 17 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context comprises a search interface, and the second presentation context comprises a browsing page. [Cetin search results ¶12 browser ¶13], [Tsoy content feed Fig. 3 (browsing page) ¶97]
As to dependent claim 19, the rejection of claim 17 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein the first presentation context and the second presentation context generate different input features. [Cetin query and search context driven features ¶30, ¶35]
As to dependent claim 20, the rejection of claim 17 is incorporated, Tsoy, Cetin and Sabharwal further teach wherein obtaining the plurality of training examples features comprises deriving the context dependent sets of training features from historically observed data associated with presenting content in at least the first presentation context and the second presentation context. [Tsoy records of previous interactions used for training ¶105, ¶10]
Response to Arguments
Applicant's arguments filed 5/18/2026. In the remark, applicant argues that:
(1) Tsoy and Cetin fail to teach "in response to receiving the user touch inputs evaluating, while the scrollable search result area and the focus area are presented on the screen of the client device, the user touch inputs and a sequence of the user swipe gesture and the tap to determine whether the user touch inputs indicate a user selection of the second search result item of the plurality of search result items;" as recited in amended claim 1.
As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Tsoy in view of Cetin and Sabharwal
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
FOSCHINI et al. (US 20230245777 A1) teaches generating a set of masked data records by masking a subset of the data records in accordance with a pattern of natural missingness from a data record (see ¶3)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is 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 extension fee 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 date of this final action.
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