DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
2. The action is responsive to the Applicant’s Amendment filed on 5/13/2026. Claims 1-20 are pending in the application. Claims 1, 9, and 17 are amended.
The 112(b) rejection of claims 1-20 previously set forth in the Non-Final Office Action mailed 3/20/2026 is hereby withdrawn.
Applicant’s amendments to the claims integrate the processes into a practical application. The 101 rejection of claims 1-20 previously set forth in the Non-Final Office Action mailed 3/20/2026 is hereby withdrawn.
Response to Arguments
3. Applicant’s arguments with respect to the rejections previously made and the amended claims filed on 5/13/2026 have been fully considered but they are not persuasive. In view of the claim amendments, the rejections are being updated accordingly.
Further, regarding the new limitations recited in claims 1, 9, and 17, it is submitted that they are properly addressed.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for details.
Claim Rejections - 35 USC § 103
4. 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.
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al. (US 20220058503 A1) in view of Kumar et al. (US 20250131505 A1).
Regarding Claim 1, Sinha discloses a system, comprising: a processor; and non-transitory memory storing instructions that, when executed, cause the processor to ([0048]: A computer system hosting a user segmentation application, such as the content management platform 220 of FIG. 2, may be configured to perform the illustrative flow in some embodiments):
obtain a user data structure comprising at least one data element representative of a set of user features ([0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0045]: As illustrated in FIG. 2… The user interactions 218 (e.g., the online activities of the computing devices 212A-212K) can be associated with some or all of these attributes 214A-214K);
input a first subset of the set of user features to a deep neural network classifier model ([0111]: user interaction data was collected for six million sessions… Three existing system models were used… These three existing models were… A factorization machine based neural network (FNN)) and
generate a segment probability for each of a plurality of segments for the user data structure, wherein each segment probability indicates a probability that the user data structure belongs to a corresponding segment (Fig. 1; [0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0032]: As used herein, a “segment indicator” is an indicator associated with a segment and that identifies whether a user interaction (or a user or an end user device behind the user interaction) belongs to the segment; [0036]-[0038]: Each of the data points 124 includes the attributes of a corresponding user interaction (e.g., an access to a web site, a click on a link of a web page, or a surfing away) and includes or is labeled with whether the corresponding interaction belongs to the user segment or not),
wherein the deep neural network classifier model is iteratively trained to adjust corresponding parameters based on training data comprising user data structures with values for features representative of assignment to one of the plurality of segments ([0008]: For example, a deep learning system can generate user segments with relatively high accuracy through feature extractions within hidden layers of its neural network; See also Fig. 2; paras [0030], [0042]-[0043]; [This is nonfunctional descriptive material]);
However, Sinha does not explicitly teach “input the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model and generate a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment input the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model and; generate a user-application-segment score based on a weighted combination of the segment probability and the user-application segment prediction; and modify the user data structure to include an application-specific label based on the user-application-segment score; generate an interface based on the application-specific label of the user data structure; and output the interface for display.”
On the other hand, in the same field of endeavor, Kumar teaches
input the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model ([0093]-[0096]: Based on a generic input, for example, “where are the investment opportunities in real estate in the state of Florida”, the system analyzes several related and seemingly unrelated data sources to recommend, say, the top five geographies for buying land and building houses… As described above, the system builds relationships between various data inputs using AI/ML models) and
generate a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment ([0094]-[0096]: The system also presents other metrics such as economic outlook, and predictive analytics such as number of houses expected to be sold in a given period, growth in prices… A fusion of future prediction of each individual model feature… Some agents are able to predict the future coefficients for such scenarios);
input the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model ([Abstract]: The method comprises… applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources; [0096]: A combinatorial analysis of these outputs is then able to be evaluated… Similarly, the system also considers… GDP growth in a combinatorial analysis as inputs) and;
generate a user-application-segment score (Fig. 2; [0076]: In some embodiments, the correlation module 208 conducts a combinatorial analysis of the plurality of data sources… Each of these metrics is given a specific score and analyzed individually and in combination with other data)
based on a weighted combination of the segment probability and the user-application segment prediction ([0076]: For example, the intermediary data set is able to include, but is not limited to… weighted relationship amongst data segments); and
modify the user data structure to include an application-specific label based on the user-application-segment score ([0110]: The input data is able to include Quality of Life data, such as… livability score… Livability score is able to be calculated based on factors like amenities, cost of living, crime, employment, housing, schools & user ratings).
generate an interface based on the application-specific label of the user data structure; and output the interface for display (Fig. 2; [0076]: The correlation module 208 is able to take the output of each model's computation and may evaluate the combined result to generate an output; Fig. 3; The computational system receives input data 302 and generates output data 320).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sinha to incorporate the teachings of Kumar to include analyzing knowledge from a plurality of data sources and applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources.
The motivation for doing so would be to generate computational predictions associated with a real-world entity, as recognized by Kumar ([Abstract] of Kumar: A method for generating computational predictions associated with a real-world entity is disclosed. The method comprises analyzing knowledge from a plurality of data sources).
Regarding Claim 2, the combined teaching of Sinha and Kumar disclose the system of claim 1.
Sinha further teaches wherein the plurality of segments are determined based on an application context ([0029]: Accordingly, if the user segmentation is used to control a computing system, the relevant attributes associated with a group through the group's rule can be used as control parameters in the set-up of the computing system. For instance, within the context of a content management system).
Regarding Claim 3, the combined teaching of Sinha and Kumar disclose the system of claim 1.
Sinha further teaches wherein the instructions cause the processor to verify the user- application-segment score based on an output of the deep neural net classifier model, the data segmentation model, and one or more aggregated features of a corresponding user segment ([0094]-[0099]: FIG. 8 illustrates an example of a flow for selecting an optimal set of rules based on a greedy approach for computing an objective function F(R) according to certain embodiments… At operation 810, the computer system compares the score to the score threshold).
Regarding Claim 4, the combined teaching of Sinha and Kumar disclose the system of claim 1.
Kumar further teaches wherein the deep neural net classifier model is generated by a supervised learning process using a training dataset comprising ideal candidates for each of the plurality of segments ([0068]: In some embodiments, the ML models disclosed herein include appropriate classifiers and ML methodologies. Some of the ML algorithms include (1) Multilayer Perceptron, Support Vector Machines, Bayesian learning, K-Nearest Neighbor, or Naive Bayes as part of supervised learning).
Regarding Claim 5, the combined teaching of Sinha and Kumar disclose the system of claim 4.
Kumar further teaches wherein the ideal candidates are identified by an unsupervised clustering model ([0068]: Unsupervised learning utilizing Autoencoders, Gaussian Mixture and K-means clustering).
Regarding Claim 6, the combined teaching of Sinha and Kumar disclose the system of claim 1.
Sinha further teaches wherein the instructions cause the processor to generate an interface including at least a first customized interface element selected based, at least in part, on the user- application-segment score (Fig. 3; [0054]-[0055]: At operation 310, the segmentation module presents the groups. In example, the groups are presented on a user interface… At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with).
Regarding Claim 7, the combined teaching of Sinha and Kumar disclose the system of claim 6.
Sinha further teaches wherein the instructions cause the processor to: receive feedback data including user interactions with the interface including at least the first customized interface element; and modify at least one of the deep neural net classifier model, the data segmentation model, or the combinatorial model, at least in part, on the feedback data (Fig. 3; [0055]: At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with. In an example, a user interaction data point is received based on user interaction between an end user computing device and a computing resource… Hence, the user interaction data point is added to the group).
Regarding Claim 8, the combined teaching of Sinha and Kumar disclose the system of claim 1.
Kumar further teaches wherein the deep neural net classifier model generates a categorical encoding ([0068]: an artificial neural network model… a deep learning algorithm (e.g., a restricted Boltzmann machine, a deep belief network method, a convolution network method, a stacked auto-encoder method)).
Regarding Claim 9, Sinha discloses a computer-implemented method, comprising:
receiving a user data structure comprising at least one data element representative of a set of user features ([0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0045]: As illustrated in FIG. 2… The user interactions 218 (e.g., the online activities of the computing devices 212A-212K) can be associated with some or all of these attributes 214A-214K);
inputting a first subset of the set of user features to a deep neural network classifier model ([0111]: user interaction data was collected for six million sessions… Three existing system models were used… These three existing models were… A factorization machine based neural network (FNN)) and
generate a segment probability for each of a plurality of segments for the user data structure, wherein each segment probability indicates a probability that the user data structure belongs to a corresponding segment (Fig. 1; [0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0032]: As used herein, a “segment indicator” is an indicator associated with a segment and that identifies whether a user interaction (or a user or an end user device behind the user interaction) belongs to the segment; [0036]-[0038]: Each of the data points 124 includes the attributes of a corresponding user interaction (e.g., an access to a web site, a click on a link of a web page, or a surfing away) and includes or is labeled with whether the corresponding interaction belongs to the user segment or not),
wherein the deep neural network classifier model is iteratively trained to adjust corresponding parameters based on training data comprising user data structures with values for features representative of assignment to one of the plurality of segments ([0008]: For example, a deep learning system can generate user segments with relatively high accuracy through feature extractions within hidden layers of its neural network; See also Fig. 2; paras [0030], [0042]-[0043]; [This is nonfunctional descriptive material]);
However, Sinha does not explicitly teach “inputting the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model and generating a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment; inputting the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model and; generating a user-application-segment score based on a weighted combination of the segment probability and the user-application segment prediction; and modifying the user data structure to include an application-specific label based on the user-application-segment score; generating an interface based on the application-specific label of the user data structure; and output the interface for display.”
On the other hand, in the same field of endeavor, Kumar teaches
inputting the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model ([0093]-[0096]: Based on a generic input, for example, “where are the investment opportunities in real estate in the state of Florida”, the system analyzes several related and seemingly unrelated data sources to recommend, say, the top five geographies for buying land and building houses… As described above, the system builds relationships between various data inputs using AI/ML models) and
generating a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment ([0094]-[0096]: The system also presents other metrics such as economic outlook, and predictive analytics such as number of houses expected to be sold in a given period, growth in prices… A fusion of future prediction of each individual model feature… Some agents are able to predict the future coefficients for such scenarios);
inputting the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model ([Abstract]: The method comprises… applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources; [0096]: A combinatorial analysis of these outputs is then able to be evaluated… Similarly, the system also considers… GDP growth in a combinatorial analysis as inputs) and;
generating a user-application-segment score (Fig. 2; [0076]: In some embodiments, the correlation module 208 conducts a combinatorial analysis of the plurality of data sources… Each of these metrics is given a specific score and analyzed individually and in combination with other data)
based on a weighted combination of the segment probability and the user-application segment prediction ([0076]: For example, the intermediary data set is able to include, but is not limited to… weighted relationship amongst data segments); and
modifying the user data structure to include an application-specific label based on the user-application-segment score ([0110]: The input data is able to include Quality of Life data, such as… livability score… Livability score is able to be calculated based on factors like amenities, cost of living, crime, employment, housing, schools & user ratings).
generating an interface based on the application-specific label of the user data structure; and outputting the interface for display (Fig. 2; [0076]: The correlation module 208 is able to take the output of each model's computation and may evaluate the combined result to generate an output; Fig. 3; The computational system receives input data 302 and generates output data 320).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sinha to incorporate the teachings of Kumar to include analyzing knowledge from a plurality of data sources and applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources.
The motivation for doing so would be to generate computational predictions associated with a real-world entity, as recognized by Kumar ([Abstract] of Kumar: A method for generating computational predictions associated with a real-world entity is disclosed. The method comprises analyzing knowledge from a plurality of data sources).
Regarding Claim 10, Sinha discloses the computer-implemented method of claim 9.
Sinha further teaches wherein the plurality of segments are determined based on an application context ([0029]: Accordingly, if the user segmentation is used to control a computing system, the relevant attributes associated with a group through the group's rule can be used as control parameters in the set-up of the computing system. For instance, within the context of a content management system).
Regarding Claim 11, Sinha discloses the computer-implemented method of claim 9.
Sinha further teaches comprising verifying the user-application-segment score based on an output of the deep neural net classifier model, the data segmentation model, and one or more aggregated features of a corresponding user segment ([0094]-[0099]: FIG. 8 illustrates an example of a flow for selecting an optimal set of rules based on a greedy approach for computing an objective function F(R) according to certain embodiments… At operation 810, the computer system compares the score to the score threshold).
Regarding Claim 12, Sinha discloses the computer-implemented method of claim 9.
Kumar further teaches wherein the deep neural net classifier model is generated by a supervised learning process using a training dataset comprising ideal candidates for each of the plurality of segments ([0068]: In some embodiments, the ML models disclosed herein include appropriate classifiers and ML methodologies. Some of the ML algorithms include (1) Multilayer Perceptron, Support Vector Machines, Bayesian learning, K-Nearest Neighbor, or Naive Bayes as part of supervised learning).
Regarding Claim 13, Sinha discloses the computer-implemented method of claim 12.
Kumar further teaches wherein the ideal candidates are identified by a clustering model ([0068]: Unsupervised learning utilizing Autoencoders, Gaussian Mixture and K-means clustering).
Regarding Claim 14, Sinha discloses the computer-implemented method of claim 9.
Sinha further teaches comprising generating an interface including at least a first customized interface element selected based, at least in part, on the user- application-segment score (Fig. 3; [0054]-[0055]: At operation 310, the segmentation module presents the groups. In example, the groups are presented on a user interface… At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with)\).
Regarding Claim 15, Sinha discloses the computer-implemented method of claim 14.
Sinha further teaches comprising receiving feedback data including user interactions with the interface including at least the first customized interface element; and modifying at least one of the deep neural net classifier model, the data segmentation model, or the combinatorial model based, at least in part, on the feedback data (Fig. 3; [0055]: At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with. In an example, a user interaction data point is received based on user interaction between an end user computing device and a computing resource… Hence, the user interaction data point is added to the group).
Regarding Claim 16, Sinha discloses the computer-implemented method of claim 9.
Kumar further teaches wherein the deep neural net classifier model is configured to generate a categorical encoding ([0068]: an artificial neural network model… a deep learning algorithm (e.g., a restricted Boltzmann machine, a deep belief network method, a convolution network method, a stacked auto-encoder method)).
Regarding Claim 17, Sinha discloses a non-transitory computer readable medium having instructions stored thereon, wherein the instructions ([0048]: A computer system hosting a user segmentation application, such as the content management platform 220 of FIG. 2, may be configured to perform the illustrative flow in some embodiments), when executed by at least one processor, cause at least one device to perform operations comprising:
receiving a user data structure comprising at least one data element representative of a set of user features ([0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0045]: As illustrated in FIG. 2… The user interactions 218 (e.g., the online activities of the computing devices 212A-212K) can be associated with some or all of these attributes 214A-214K);
inputting a first subset of the set of user features to a deep neural network classifier model ([0111]: user interaction data was collected for six million sessions… Three existing system models were used… These three existing models were… A factorization machine based neural network (FNN)) and
generate a segment probability for each of a plurality of segments for the user data structure, wherein each segment probability indicates a probability that the user data structure belongs to a corresponding segment (Fig. 1; [0024]: In an example, user interaction data points are received. Each of the data points includes attributes of a user interaction within a computer network and a segment indicator of whether the user interaction belongs to a segment of interest; [0032]: As used herein, a “segment indicator” is an indicator associated with a segment and that identifies whether a user interaction (or a user or an end user device behind the user interaction) belongs to the segment; [0036]-[0038]: Each of the data points 124 includes the attributes of a corresponding user interaction (e.g., an access to a web site, a click on a link of a web page, or a surfing away) and includes or is labeled with whether the corresponding interaction belongs to the user segment or not),
wherein the deep neural network classifier model is iteratively trained to adjust corresponding parameters based on training data comprising user data structures with values for features representative of assignment to one of the plurality of segments ([0008]: For example, a deep learning system can generate user segments with relatively high accuracy through feature extractions within hidden layers of its neural network; See also Fig. 2; paras [0030], [0042]-[0043]; [This is nonfunctional descriptive material]);
However, Sinha does not explicitly teach “inputting the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model and generating a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment; inputting the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model and; generating a user-application-segment score based on a weighted combination of the segment probability and the user-application segment prediction; and modifying the user data structure to include an application-specific label based on the user-application-segment score; generating an interface based on the application-specific label of the user data structure; and output the interface for display.”
On the other hand, in the same field of endeavor, Kumar teaches
inputting the segment probability for each of the plurality of segments from the deep neural network classifier model and a second subset of the set of user features to a data segmentation model ([0093]-[0096]: Based on a generic input, for example, “where are the investment opportunities in real estate in the state of Florida”, the system analyzes several related and seemingly unrelated data sources to recommend, say, the top five geographies for buying land and building houses… As described above, the system builds relationships between various data inputs using AI/ML models) and
generating a user-application segment prediction for each of the plurality of segments by a data segmentation model that receives the segment probability for each of the plurality of segments, wherein each user-application segment prediction indicates a refined probability that the user data structure belongs to each corresponding segment ([0094]-[0096]: The system also presents other metrics such as economic outlook, and predictive analytics such as number of houses expected to be sold in a given period, growth in prices… A fusion of future prediction of each individual model feature… Some agents are able to predict the future coefficients for such scenarios);
inputting the segment probability for at least one of the plurality of segments and user-application segment prediction for each of the plurality of segments to a combinatorial model ([Abstract]: The method comprises… applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources; [0096]: A combinatorial analysis of these outputs is then able to be evaluated… Similarly, the system also considers… GDP growth in a combinatorial analysis as inputs) and;
generating a user-application-segment score (Fig. 2; [0076]: In some embodiments, the correlation module 208 conducts a combinatorial analysis of the plurality of data sources… Each of these metrics is given a specific score and analyzed individually and in combination with other data)
based on a weighted combination of the segment probability and the user-application segment prediction ([0076]: For example, the intermediary data set is able to include, but is not limited to… weighted relationship amongst data segments); and
modifying the user data structure to include an application-specific label based on the user-application-segment score ([0110]: The input data is able to include Quality of Life data, such as… livability score… Livability score is able to be calculated based on factors like amenities, cost of living, crime, employment, housing, schools & user ratings).
generating an interface based on the application-specific label of the user data structure; and outputting the interface for display (Fig. 2; [0076]: The correlation module 208 is able to take the output of each model's computation and may evaluate the combined result to generate an output; Fig. 3; The computational system receives input data 302 and generates output data 320).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sinha to incorporate the teachings of Kumar to include analyzing knowledge from a plurality of data sources and applying, a plurality of ensembles of machine learning models to each of the plurality of data sources to conduct a combinatorial analysis of the plurality of data sources.
The motivation for doing so would be to generate computational predictions associated with a real-world entity, as recognized by Kumar ([Abstract] of Kumar: A method for generating computational predictions associated with a real-world entity is disclosed. The method comprises analyzing knowledge from a plurality of data sources).
Regarding Claim 18, Sinha discloses the non-transitory computer readable medium of claim 17.
Sinha further teaches wherein the instructions cause the at least one device to perform operations comprising verifying the user-application-segment score based on an output of the deep neural net classifier model, the data segmentation model, and one or more aggregated features of a corresponding user segment ([0094]-[0099]: FIG. 8 illustrates an example of a flow for selecting an optimal set of rules based on a greedy approach for computing an objective function F(R) according to certain embodiments… At operation 810, the computer system compares the score to the score threshold).
Regarding Claim 19, Sinha discloses the non-transitory computer readable medium of claim 17.
Kumar further teaches wherein the deep neural net classifier model is generated by a supervised learning process using a training dataset comprising ideal candidates for each of the plurality of segments ([0068]: In some embodiments, the ML models disclosed herein include appropriate classifiers and ML methodologies. Some of the ML algorithms include (1) Multilayer Perceptron, Support Vector Machines, Bayesian learning, K-Nearest Neighbor, or Naive Bayes as part of supervised learning), and wherein the ideal candidates are identified by a clustering model ([0068]: Unsupervised learning utilizing Autoencoders, Gaussian Mixture and K-means clustering).
Regarding Claim 20, Sinha discloses the non-transitory computer readable medium of claim 17.
Sinha further teaches wherein the instructions cause the at least one device to perform operations comprising:
generating an interface including at least a first customized interface element selected based, at least in part, on the user-application-segment score (Fig. 3; [0054]-[0055]: At operation 310, the segmentation module presents the groups. In example, the groups are presented on a user interface… At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with);
receiving feedback data including user interactions with the interface including at least the first customized interface element; and modifying at least one of the deep neural net classifier model, data segmentation model, or combinatorial model based, at least in part, on the feedback data (Fig. 3; [0055]: At operation 312, a customization module of the computer system customizes online activity of a client based on a group that the client is associated with. In an example, a user interaction data point is received based on user interaction between an end user computing device and a computing resource… Hence, the user interaction data point is added to the group).
Conclusion
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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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