CTNF 17/380,738 CTNF 100458 DETAILED ACTION This action is in response to the amendment filed 04/02/2026. Claims 1, 2-8, 10-15, and 18-23 are pending and have been examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1, 3-8, 10-15, and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Claim 1 : Subject Matter Eligibility Analysis Step 1: Claim 1 recites a system… comprising a processor and is thus an article of manufacture, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 1 recites classifying mouse dynamics data of a session (this limitation could encompass a human mentally classifying mouse dynamics data and is thus a judgement.) providing a decision regarding legitimacy of the session (this limitation could encompass a human mentally providing a decision and is thus a judgement.) process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (this limitation could encompass a human mentally partitioning a website into URL categories and is thus an evaluation.); map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (this limitation could encompass a human mentally mapping each URL to a category and is thus an evaluation.); group the mouse dynamics data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (this limitation could encompass a human mentally grouping mouse data by the type of URL the mouse was on and is thus an evaluation.), separately extract, for each of the plurality of URL categories, a distinct set of mouse dynamics features, wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories ( t his limitation could encompass a human mentally extracting features from each group and is thus an evaluation.), and specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold, wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (this limitation is a mental process as it could encompass a human mentally specifying a decision regarding legitimacy and is thus a judgment.) . Claim 1 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 1 further recites additional elements of using a trained classification model (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) a processor (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) receive mouse dynamics data of the session to be analyzed; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) receive an output score from the trained classification model. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) wherein the output score represents a legitimacy of the session (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, Claim 1 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of Claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination, because using a trained classification model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). a processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). receive mouse dynamics data of a session to be analyzed is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). receive an output score from the trained classification model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). wherein the output score represents a legitimacy of the session specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, Claim 1 is subject-matter ineligible . Regarding Claim 3: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 3 recites the same abstract idea as Claim 1 and therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 3 further recites the additional element wherein the trained classification model is trained using the mouse dynamics features extracted from a plurality of training sessions (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))), Therefore, claim 3 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: This additional element also does not provide significantly more than the abstract idea itself, taken alone and in combination, because wherein the trained classification model is trained using the mouse dynamics features extracted from a plurality of training sessions using a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore, claim 3 is subject-matter ineligible . Regarding Claim 4: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 4 recites the URL category mapping comprises the plurality of URLs, wherein the plurality of URLs are mapped to a unique URL category ( This limitation could encompass a human mentally mapping the URLs to specific and unique categories and is thus an evaluation.) Claim 4 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 4 has no additional elements that would integrate the abstract idea into a practical application, and therefore, Claim 4 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements to provide significantly more than the abstract idea itself, taken alone and in combination, Claim 4 is subject-matter ineligible. Regarding Claim 5: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 5 recites wherein the URL category mapping comprises a predetermined mapping ( This limitation could encompass a human mentally mapping the URLs to specific and unique categories before applying it to mouse data and is thus an evaluation.) Claim 5 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 5 has no additional elements that would integrate the abstract idea into a practical application, and therefore, Claim 5 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements to provide significantly more than the abstract idea itself, taken alone and in combination, Claim 5 is subject-matter ineligible. Regarding Claim 6: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 6 recites the URL category mapping is…generated based on data collected from an application ( This limitation could encompass a human mentally creating a mapping based on data provided and is thus an evaluation.) Claim 6 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 6 recites an additional element of automatically generating the URL category mapping (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) Therefore, claim 6 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: This additional element also does not provide significantly more than the abstract idea itself, taken alone and in combination, because automatically generating the URL category mapping uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)), and therefore Claim 6 is subject-matter ineligible . Regarding Claim 7: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 7 recites the URL category mapping is ... generated using a ... clustering on the mouse dynamics data (This limitation could encompass a human mentally creating a mapping based on clustering mouse dynamics data and is thus an evaluation.) Claim 7 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 7 further recites the additional elements automatically generated (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) using…machine learning . (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) Therefore, Claim 7 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: These additional elements also do not provide significantly more than the abstract idea itself, taken alone and in combination, because automatically generated uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)) using…machine learning . uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)) Therefore claim 7 is subject-matter ineligible . Regarding Claim 8 : Subject Matter Eligibility Analysis Step 1: Claim 8 recites method and is thus a process, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 8 recites classifying mouse dynamics data of a session (this limitation could encompass a human mentally classifying mouse dynamics data and is thus a judgment.) providing a decision regarding legitimacy of the session (this limitation could encompass a human mentally providing a decision and is thus a judgment.) processing … the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (this limitation could encompass a human mentally partitioning a website into URL categories and is thus an evaluation.); mapping … each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (this limitation could encompass a human mentally mapping each URL to a category and is thus an evaluation.); grouping … the mouse dynamics data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (this limitation could encompass a human mentally grouping mouse data by the type of URL the mouse was on and is thus an evaluation.), separately extracting, … for each of the plurality of URL categories, a distinct set of mouse dynamics features, wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories ( t his limitation could encompass a human mentally extracting features from each group and is thus an evaluation.), and specifying, … using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold, wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (this limitation is a mental process as it could encompass a human mentally specifying a decision regarding legitimacy and is thus a judgement.) . Claim 8 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 8 further recites additional elements of using a trained classification model (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) receiving, via a processor, mouse dynamics data of the session to be analyzed; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) via the processor (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) inputting, via the processor, the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) receiving, via the processor, an output score from the trained classification model. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) wherein the output score represents a legitimacy of the session (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, Claim 1 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of Claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination, because using a trained classification model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). Receiving, via a processor, mouse dynamics data of a session to be analyzed is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Via the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). inputting, via the processor, the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Receiving, via the processor, an output score from the trained classification model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). wherein the output score represents a legitimacy of the session specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, Claim 8 is subject-matter ineligible . Regarding Claim 10: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 10 recites merging… all of the mouse dynamics features (This limitation could encompass a human mentally merging all the groups into one group and is thus an evaluation.) Claim 10 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 10 further recites additional elements of training a classification model to generate the trained classification model. ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) receiving…the mouse dynamics data for a plurality of sessions and the URL category mapping, ( This additional elements do not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) the processor, ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) training, via the processor, the classification model based on the merged groups of mouse dynamics features. ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) Therefore, Claim 10 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: These additional elements also do not provide significantly more than the abstract idea itself, taken alone and in combination, because training a classification model to generate the trained classification model uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). receiving…the mouse dynamics data for a plurality of sessions and the URL category mapping is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), the processor uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). training, via the processor, the classification model based on the merged groups of mouse dynamics features uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore, Claim 10 is subject-matter ineligible . Regarding Claim 11: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 11 recites the same abstract ideas as claim 8. Claim 11 therefore recites an abstract idea . Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 11 has additional elements of training a plurality of machine learning models to generate the trained classification model. ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) receiving…the mouse dynamics data for a plurality of sessions ( This additional elements do not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) the processor, ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) training, via the processor, a machine learning model for each of the mouse dynamics features, wherein the trained classification model comprises an ensemble of the trained machine learning models ( This additional elements do not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, Claim 11 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: These additional elements also do not provide significantly more than the abstract idea itself, taken alone and in combination, because training a plurality of machine learning models to generate the trained classification model. uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). receiving…the mouse dynamics data for a plurality of sessions and the URL category mapping is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). the processor, uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). training, via the processor, a machine learning model for each of the mouse dynamics features, wherein the trained classification model comprises an ensemble of the trained machine learning models uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore, Claim 11 is subject-matter ineligible . Regarding Claim 12, claim 12 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis. Regarding Claim 13, claim 13 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis Regarding Claim 14: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 14 recites adjusting a threshold ( This limitation could encompass a human mentally adjusting the threshold to a desirable number and is thus an evaluation.) generate a decision. ( This limitation could encompass a human mentally making a decision based on the threshold and is thus a judgment.) finding a limit on a false positive rate. (This limitation is considered a mathematical concept because a limit is a mathematical concept and is thus an evaluation.) Claim 14 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 14 recites an additional element of during a training phase of the trained classification model . (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)),) Therefore, claim 14 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B This additional element also does not provide significantly more than the abstract idea itself, taken alone and in combination, because a training phase of the trained classification model uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore Claim 14 is subject-matter ineligible . Regarding Claim 15 : Subject Matter Eligibility Analysis Step 1: Claim 15 recites computer program product and is thus an article of manufacture, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 15 recites classifying mouse dynamics data of a session (this limitation could encompass a human mentally classifying mouse dynamics data and is thus a judgment.) providing a decision regarding legitimacy of the session (this limitation could encompass a human mentally providing a decision and is thus a judgment.) process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (this limitation could encompass a human mentally partitioning a website into URL categories and is thus an evaluation.); map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (this limitation could encompass a human mentally mapping each URL to a category and is thus an evaluation.); group the mouse dynamics data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (this limitation could encompass a human mentally grouping mouse data by the type of URL the mouse was on and is thus an evaluation.), separately extract, for each of the plurality of URL categories, a distinct set of mouse dynamics features, wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories ( t his limitation could encompass a human mentally extracting features from each group and is thus an evaluation.), and specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold, wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (this limitation is a mental process as it could encompass a human mentally specifying a decision regarding legitimacy and is thus a judgment.) . Claim 15 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 15 further recites additional elements of the computer program product comprising a computer-readable storage medium having program code embodied therewith, wherein the computer-readable storage medium is not a transitory signal per se, the program code executable by a processor (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) using a trained classification model (This element does not integrate the abstract idea into a practical application because it a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).) receive mouse dynamics data of the session to be analyzed; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) receive an output score from the trained classification model. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) wherein the output score represents a legitimacy of the session (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, Claim 15 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of Claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination, because the computer program product comprising a computer-readable storage medium having program code embodied therewith, wherein the computer-readable storage medium is not a transitory signal per se, the program code executable by a processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). using a trained classification model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). receive mouse dynamics data of a session to be analyzed is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of URL categories is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). receive an output score from the trained classification model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). wherein the output score represents a legitimacy of the session specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, Claim 15 is subject-matter ineligible . Regarding Claim 17: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 17 recites the same mental processes as Claim 15 and therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 17 further recites the additional element program code executable by the processor to train the classification model based on merged groups of mouse dynamics features. (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f))) Therefore, claim 17 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: This additional element also does not provide significantly more than the abstract idea itself, taken alone and in combination, because program code executable by the processor to train the classification model based on merged groups of mouse dynamics features uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore Claim 17 is subject-matter ineligible . Regarding Claim 18: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 18 recites the same mental processes as Claim 15 and therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 18 further recites additional elements of program code executable by the processor to train the classification model based on a machine learning model for each of the mouse dynamics features (This additional element does not integrate the abstract idea into a practical application because they recite a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) wherein the classification model comprises an ensemble classifier. (This additional element does not integrate the abstract idea into a practical application because they recite a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, Claim 18 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: These additional elements also do not provide significantly more than the abstract idea itself, taken alone and in combination, because program code executable by the processor to train the classification model based on a machine learning model for each of the mouse dynamics features uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Wherein the classification model comprises an ensemble classifier uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore Claim 18 is subject-matter ineligible . Regarding Claim 19: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 19 recites generate the URL category mapping based on data collected from an application ( This limitation could encompass a human mentally creating a mapping based on data provided and is thus an evaluation.) Claim 19 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 19 further recites an additional element of program code executable by the processor to automatically generate the URL category mapping. (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, claim 19 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: This additional element also does not provide significantly more than the abstract idea itself, taken alone and in combination, because program code executable by the processor to automatically generate the URL category mapping uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)), Therefore Claim 19 is subject-matter ineligible . Regarding Claim 20: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 20 recites generate the URL category mapping using a ... clustering on the mouse dynamics data. (This limitation could encompass a human mentally creating a mapping based on clustering mouse dynamics data and is thus an evaluation.) Claim 20 therefore recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 20 further recites the additional elements program code executable by the processor to automatically generate a mapping (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) using… machine learning . (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, Claim 20 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: These additional elements also do not provide significantly more than the abstract idea itself, taken alone and in combination, because program code executable by the processor to automatically generate a mapping uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Using… machine learning uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)). Therefore Claim 20 is subject-matter ineligible . Regarding Claim 21: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 21 recites mark the session as not legitimate in response to the output score not satisfying a predefined threshold (This limitation is a mental process as it encompasses a human mentally noting that the session is not legitimate and is thus a judgment.) wherein the policy includes specifications comprising automatically block the session, manually inspect the session, and automatically inspect the session (This limitation is a mental process as further describes the mental process of specifying, using a policy, the decision regarding legitimacy from claim 1 and is thus a judgment.) Therefore, claim 21 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 21 does not further recite any additional elements. Therefore, claim 21 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 21 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 21 is subject-matter ineligible. Regarding Claim 22, claim 22 recites substantially similar limitations to claim 21, and is therefore rejected under the same analysis. Regarding Claim 23, claim 23 recites substantially similar limitations to claim 21, and is therefore rejected under the same analysis. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1, 3, 8, 10-13, 15, and 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Benkreira et al. (US 20210319527) ("Benkreira"), in view of Smith and Ng, "Web Page Clustering Using a Self-Organizing Map of User Navigation Patterns" (“Smith”) . Regarding Claim 1 , Benkreira teaches A system for classifying mouse dynamics data of a session using a trained classification model and providing a decision regarding legitimacy of the session, comprising a processor to (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025) where “the system (e.g., using computing resource 414 processor 520, memory 530, storage component 540, input component 550, output component 560, communication interface 570, and or the like) may transit an indication of a recommended action to be performed by the server device with respect to the application form and the client device based on the fraud score, as described above”.): receive mouse dynamics data of the session to be analyzed (Benkreira Fig. 6, 620, Receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device wherein “the behavior information may indicate at least one of:…mouse dynamics used to input the data into the one or more fields or to navigate between fields of the application form, a technique used to navigate between fields of the application form, a technique used to scroll between different portions of the application form on the client device.” (Benkreira, page 22, paragraph 0087)), group the mouse dynamics data into a plurality of groups (Benkreira, page 19, paragraph 0055, “the trained machine learning model may classify (e.g. cluster) the new observation in a cluster [group] as shown by reference number 320” wherein “the set of observations may include data gathered from user interacting with and/or user input” (Benkreira, page 16, paragraph 0038)), separately extract, for each of the plurality of… categories, a distinct set of mouse dynamics, (Benkreira, Paragraph 0039, "As shown by reference number 210, a feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature….In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from a server device, such as by extracting or generating a name for a column… and/or the like" where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) wherein the features are clustered (Benkreira page 19, paragraph 0055), then the features are extracted from each cluster for each feature set. Examiner further notes that the observations are the mouse dynamics.). input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of …categories (Benkreira, Paragraph 0003, "provide the device information and the behavior information as a feature set that is input to a machine learning model”); receive an output score from the trained classification model (Benkreira, Paragraph 0003, “receive output from the machine learning model”), wherein the output score represents a legitimacy of the session (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315.”where “the fraud platform may determine a fraud score based on the device information and the behavior information. A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score.); specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation, such as to send another authentication challenge to verify identity. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action) such as to send a more difficult authentication challenge” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score. Examiner further notes that by exceeding the threshold, the fraud score is classified as having a high likelihood of fraud. Examiner notes that classifying as not legitimate is having a likelihood of fraud.). Benkreira does not teach, but Smith does teach process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to aa plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes mapping each URL visited in the session to at least one URL category is clustering the transactions into similarity groups. Examiner further notes that the clusters are the URL categories and the category mapping is the SOM.); group the…data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph).Examiner notes that the SOM is being considered as a category mapping) the plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to a plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns. The K-means algorithm is outlined in Fig. 2. Within each processed transaction group, we use the column totals as the activity of the transaction group. This is illustrated in Table 2, where the cluster depicted contains 2679 transactions, including transaction numbers 5, 6, and 8012. Instead of having transactions as features, we now have transaction groups as features. The URLs are now described by the relative interests or activities of each of the transaction groups.” (Smith, page 250, 2 nd -3 rd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph). Examiner notes that the transaction groups are features.); Benkreira and Smith are considered analogous because they are in the same field of machine learning where they both collect and organize data to be trained. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Benkreira to use a URL category mapping to group the behavior data. Doing so would “successfully reduce[] the number of dimensions of the data” (Smith, page 250, paragraph 6) and “add more value to information retriev[ed]” (Smith, page 246, fifth paragraph). Regarding Claim 3 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira further teaches wherein the trained classification model is trained using the mouse dynamics features extracted from a plurality of training sessions (Benkreira, Paragraph 0038 "a machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from historical data, such as data gathered during one or more processes described herein” where "a device may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:…provide the device information and the behavior information as a feature set that is input to a machine learning model” (Benkreira, Paragraph 0003) wherein the “processes” correspond to training sessions and the “observations” correspond to the mouse dynamics.). Regarding Claim 8 , Benkreira teaches A computer-implemented method for classifying mouse dynamics data of a session using a trained classification model and providing a decision regarding legitimacy of the session, comprising (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025) where “the system (e.g., using computing resource 414 processor 520, memory 530, storage component 540, input component 550, output component 560, communication interface 570, and or the like) may transit an indication of a recommended action to be performed by the server device with respect to the application form and the client device based on the fraud score, as described above”.): Receiving… mouse dynamics data of the session to be analyzed (Benkreira Fig. 6, 620, Receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device wherein “the behavior information may indicate at least one of:…mouse dynamics used to input the data into the one or more fields or to navigate between fields of the application form, a technique used to navigate between fields of the application form, a technique used to scroll between different portions of the application form on the client device.” (Benkreira, page 22, paragraph 0087)), Via a processor (Benkreira, page 21, paragraph 0078, “Device 500 may perform one or more processes described herein. Device 500 may perform these processes based on processor 520 executing software instructions stored by a non-transitory computer readable medium, such as memory 530 and/or storage component 540.”) Grouping… the mouse dynamics data into a plurality of groups (Benkreira, page 19, paragraph 0055, “the trained machine learning model may classify (e.g. cluster) the new observation in a cluster [group] as shown by reference number 320” wherein “the set of observations may include data gathered from user interacting with and/or user input” (Benkreira, page 16, paragraph 0038)), separately extracting…, for each of the plurality of… categories, a distinct set of mouse dynamics, (Benkreira, Paragraph 0039, "As shown by reference number 210, a feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature….In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from a server device, such as by extracting or generating a name for a column… and/or the like" where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) wherein the features are clustered (Benkreira page 19, paragraph 0055), then the features are extracted from each cluster for each feature set. Examiner further notes that the observations are the mouse dynamics.). inputting… the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of …categories (Benkreira, Paragraph 0003, "provide the device information and the behavior information as a feature set that is input to a machine learning model”); Receiving… an output score from the trained classification model (Benkreira, Paragraph 0003, “receive output from the machine learning model”), wherein the output score represents a legitimacy of the session (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315.”where “the fraud platform may determine a fraud score based on the device information and the behavior information. A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score.); specifying… using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation, such as to send another authentication challenge to verify identity. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action) such as to send a more difficult authentication challenge” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score. Examiner further notes that by exceeding the threshold, the fraud score is classified as having a high likelihood of fraud. Examiner notes that classifying as not legitimate is having a likelihood of fraud.). Benkreira does not teach, but Smith does teach processing… the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to aa plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); mapping… each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes mapping each URL visited in the session to at least one URL category is clustering the transactions into similarity groups. Examiner further notes that the clusters are the URL categories and the category mapping is the SOM.); grouping… the…data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph).Examiner notes that the SOM is being considered as a category mapping) the plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to a plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns. The K-means algorithm is outlined in Fig. 2. Within each processed transaction group, we use the column totals as the activity of the transaction group. This is illustrated in Table 2, where the cluster depicted contains 2679 transactions, including transaction numbers 5, 6, and 8012. Instead of having transactions as features, we now have transaction groups as features. The URLs are now described by the relative interests or activities of each of the transaction groups.” (Smith, page 250, 2 nd -3 rd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph). Examiner notes that the transaction groups are features.); Benkreira and Smith are considered analogous because they are in the same field of machine learning where they both collect and organize data to be trained. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Benkreira to use a URL category mapping to group the behavior data. Doing so would “successfully reduce[] the number of dimensions of the data” (Smith, page 250, paragraph 6) and “add more value to information retriev[ed]” (Smith, page 246, fifth paragraph). Regarding Claim 10 , Benkreira in view of Smith teaches the computer-implemented method of claim 8 (and thus the rejection of claim 8 is incorporated). Benkreira further teaches further comprising training a classification model to generate the trained classification model (Benkreira, page 16, paragraph 0037, “FIG. 2 is a diagram illustrating an example 200 of training a machine learning model. The machine learning model described herein may be performed using a machine learning system”) receiving…mouse dynamics data for a plurality of sessions (Benkreira Fig. 6, 620, Receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device wherein “the behavior information may indicate at least one of:…mouse dynamics used to input the data into the one or more fields or to navigate between fields of the application form, a technique used to navigate between fields of the application form, a technique used to scroll between different portions of the application form on the client device.” (Benkreira, page 22, paragraph 0087) and “process [ session ] 600 may include additional implementations, such as any single implementation or any combination of implementations described below and/or in connection with one or more other processes [ sessions] described elsewhere herein” (Benkreira, page 22, paragraph 0086)) merging, via the processor, all of the mouse dynamics features (Benkreira, page 16, paragraph 0040, "the machine learning system may pre-process and/or perform dimensionality reduction to reduce the feature set and/or combine features of the feature set to a minimum feature set” wherein “as shown by reference number 210, a feature set may be derived from the set of observations [clusters]”(Benkreira, page 16, paragraph 0038) where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) and there are multiple clusters (Benkreira, Fig 3, 320).), and training, via the processor, the classification model based on the merged groups of mouse dynamics features (Benkreira, page 16, paragraph 0040 "a machine learning model may be trained on the minimum feature set" where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040)). Benkreira does not teach, but Smith does teach receiving…the URL category mapping (Smith, page 249, second paragraph, "we want to map the web documents into a two-dimensional space, where the locations will indicate the similarity between documents, as indicated by the navigation patterns"). Benkreira and Smith are considered analogous because they are in the same field of machine learning where they both collect and organize data to be trained. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Benkreira to use a URL category mapping to group the behavior data. Doing so would “successfully reduce[] the number of dimensions of the data” (Smith, page 250, paragraph 6) and “add more value to information retriev[ed]” (Smith, page 246, fifth paragraph). Regarding Claim 11 , Benkreira in view of Smith teaches the computer-implemented method of claim 8 (and thus the rejection of claim 8 is incorporated). Benkreira further teaches receiving…mouse dynamics data for a plurality of sessions (Benkreira Fig. 6, 620, Receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device wherein “the behavior information may indicate at least one of:…mouse dynamics used to input the data into the one or more fields or to navigate between fields of the application form, a technique used to navigate between fields of the application form, a technique used to scroll between different portions of the application form on the client device.” (Benkreira, page 22, paragraph 0087) and “process [ session ] 600 may include additional implementations, such as any single implementation or any combination of implementations described below and/or in connection with one or more other processes [ sessions] described elsewhere herein” (Benkreira, page 22, paragraph 0086)) and training, via the processor, a machine learning model for each of the mouse dynamics features (Benkreira page 18, paragraph 0048, “In some implementations, the machine learning system may independently train the machine learning model k times, with each individual group being used as a hold-out group once and being used as a training group k-1 times” wherein “observations in the training set 220 may be split into k groups (e.g., in order or at random)” (Benkreira page 18, paragraph 0048) where "a device may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:…provide the device information and the behavior information as a feature set that is input to a machine learning model” (Benkreira, Paragraph 0003) and “the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) ) , wherein the trained classification model comprises an ensemble of the trained machine learning models (Benkreira, page 17, paragraph 0047, "the machine learning system may train multiple machine learning model to generate a set of model parameters for each machine learning model, where each machine learning model corresponds to a different combination of a machine learning algorithm and a hyperparameter set 240 for that machine learning algorithm"). Regarding Claim 12, claim 12 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis. Regarding Claim 13, claim 13 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis Regarding Claim 15 , Benkreira teaches A computer program product for classifying mouse dynamics data of a session using a trained classification model and providing a decision regarding legitimacy of the session, the computer program product comprising a computer-readable storage medium having program code embodied therewith, wherein the computer-readable storage medium is not a transitory signal per se, the program code executable by a processor (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025) where “the system (e.g., using computing resource 414 processor 520, memory 530, storage component 540, input component 550, output component 560, communication interface 570, and or the like) may transit an indication of a recommended action to be performed by the server device with respect to the application form and the client device based on the fraud score, as described above” where “Device 500 may perform one or more processes described herein. Device 500 may perform these processes based on processor 520 executing software instructions stored by a non-transitory computer-readable medium, such as memory 530 and/or storage component 540” (Benkreira, page 21, paragraph 0078).) receive mouse dynamics data of the session to be analyzed (Benkreira Fig. 6, 620, Receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device wherein “the behavior information may indicate at least one of:…mouse dynamics used to input the data into the one or more fields or to navigate between fields of the application form, a technique used to navigate between fields of the application form, a technique used to scroll between different portions of the application form on the client device.” (Benkreira, page 22, paragraph 0087)), group the mouse dynamics data into a plurality of groups (Benkreira, page 19, paragraph 0055, “the trained machine learning model may classify (e.g. cluster) the new observation in a cluster [group] as shown by reference number 320” wherein “the set of observations may include data gathered from user interacting with and/or user input” (Benkreira, page 16, paragraph 0038)), separately extract, for each of the plurality of… categories, a distinct set of mouse dynamics, (Benkreira, Paragraph 0039, "As shown by reference number 210, a feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature….In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from a server device, such as by extracting or generating a name for a column… and/or the like" where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) wherein the features are clustered (Benkreira page 19, paragraph 0055), then the features are extracted from each cluster for each feature set. Examiner further notes that the observations are the mouse dynamics.). input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of …categories (Benkreira, Paragraph 0003, "provide the device information and the behavior information as a feature set that is input to a machine learning model”); receive an output score from the trained classification model (Benkreira, Paragraph 0003, “receive output from the machine learning model”), wherein the output score represents a legitimacy of the session (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315.”where “the fraud platform may determine a fraud score based on the device information and the behavior information. A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score.); specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold wherein the decision is a classification of the session as legitimate, not legitimate or an outlier (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation, such as to send another authentication challenge to verify identity. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action) such as to send a more difficult authentication challenge” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025). Examiner notes that the fraud score is the output score. Examiner further notes that by exceeding the threshold, the fraud score is classified as having a high likelihood of fraud. Examiner notes that classifying as not legitimate is having a likelihood of fraud.). Benkreira does not teach, but Smith does teach process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to aa plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes mapping each URL visited in the session to at least one URL category is clustering the transactions into similarity groups. Examiner further notes that the clusters are the URL categories and the category mapping is the SOM.); group the…data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph).Examiner notes that the SOM is being considered as a category mapping) the plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to a plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns. The K-means algorithm is outlined in Fig. 2. Within each processed transaction group, we use the column totals as the activity of the transaction group. This is illustrated in Table 2, where the cluster depicted contains 2679 transactions, including transaction numbers 5, 6, and 8012. Instead of having transactions as features, we now have transaction groups as features. The URLs are now described by the relative interests or activities of each of the transaction groups.” (Smith, page 250, 2 nd -3 rd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph). Examiner notes that the transaction groups are features.); Benkreira and Smith are considered analogous because they are in the same field of machine learning where they both collect and organize data to be trained. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Benkreira to use a URL category mapping to group the behavior data. Doing so would “successfully reduce[] the number of dimensions of the data” (Smith, page 250, paragraph 6) and “add more value to information retriev[ed]” (Smith, page 246, fifth paragraph). Claim 17 recites the computer program product of claim 15 (and thus the rejection of claim 15 is incorporated). Benkreira further teaches train a classification model based on the merged groups of mouse dynamics feature (Benkreira, page 16, paragraph 0040 "a machine learning model may be trained on the minimum feature set" wherein “as shown by reference number 210, a feature set may be derived from the set of observations [clusters]”(Benkreira, page 16, paragraph 0038) and “the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) and there are multiple clusters (Benkreira, Fig 3, 320).)). Regarding Claim 18 , Benkreira in view of Smith teaches the computer program product of claim 15 (and thus the rejection of claim 15 is incorporated). Benkreira further teaches train a classification model based on a machine learning model (Benkreira, page 16, paragraph 0037, “FIG. 2 is a diagram illustrating an example 200 of training a machine learning model. The machine learning model described herein may be performed using a machine learning system”) for each of the plurality of groups of features (Benkreira page 18, paragraph 0048, “In some implementations, the machine learning system may independently train the machine learning model k times, with each individual group being used as a hold-out group once and being used as a training group k-1 times” wherein “observations in the training set 220 may be split into k groups (e.g., in order or at random)” (Benkreira page 18, paragraph 0048) where "a device may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:…provide the device information and the behavior information as a feature set that is input to a machine learning model” (Benkreira, Paragraph 0003) and “the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) ) wherein the classification model comprises an ensemble classifier (page 17, paragraph 0046, "the machine learning algorithm may include a decision tree algorithm, which may include a tree ensemble algorithm (e.g., generated using bagging and/or boosting)). Regarding Claim 19, claim 19 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis. Regarding Claim 20, claim 20 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis. Regarding Claim 21 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira further teaches mark the session as not legitimate in response to the output score not satisfying a predefined threshold (Benkreira, page 19, paragraph 0054, “the trained machine learning model 305 may predict a value of 90 for the target variable of “fraud score” for the new observation, as shown by reference number 315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation” where “A fraud score may indicate a likelihood that the user who is associated with the behavior information or the device information is committing fraud (e.g., a high fraud score indicates a high likelihood of fraud, a low fraud score indicates a low likelihood of fraud, and/or the like)”( Benkreira, page 14, paragraph 0025).); wherein the policy includes specifications comprising of automatically block the session, manually inspect the session, and automatically inspect the session (Benkreira, page 15, paragraph 0028, “Recommended actions may include approving an application associated with the application form, rejecting the application associated with the application form, requesting additional information from the client device, sending an authentication challenge ( e.g., a knowledge-based authentication (KBA) question, a video review action, a biometric step-up action, and/or the like), and/or the like. The recommended action may be used to obtain additional information on whether to authenticate the user and/or gain more information on whether the transaction is fraudulent” where “ the client device may transmit, to an operator device, the video review based on the unsuccessful completion of the KBA challenge. The operator device may determine whether the video review is sufficient to authenticate the user and/or accept the application” (Benkreira, page 16, paragraph 0035) and “Operator device 440 includes one or more devices capable of receiving, generating, storing, processing, and/or providing information, such as information described herein. For example, operator device 440 may include a laptop computer, a tablet computer, a desktop computer, a server device, a group of server devices, or a similar type of device, associated with a merchant, a financial institution, and/or the like. In some implementations, operator device 440 may receive information from and/or transmit information to server device 430 and/or fraud platform 410” (Benkreira, page 20, paragraph 0070). Examiner notes that rejecting the application is automatically blocking the session. Examiner further notes that sending an authentication challenge and/or the like is automatically inspecting the session. Additionally, Examiner notes that requesting additional information from a client device is manually inspecting because the client device transmits information to an operator device which is used by a human to further inspect a session.). Regarding Claim 22, claim 22 recites substantially similar limitations to claim 21, and is therefore rejected under the same analysis. Regarding Claim 23, claim 23 recites substantially similar limitations to claim 21, and is therefore rejected under the same analysis . 07-22-aia AIA Claim 4 , 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Benkreira in view of Smith as applied to claim s 1-3, 8-13, and 15-20 above, and further in view of Morichetta et al. (Morichetta), "CLUE: Clustering for Mining Web URLs." Regarding Claim 4 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira in view of Smith does not teach, but Morichetta does teach, wherein the URL category mapping comprises the plurality of URLs, wherein the plurality of URLs are mapped to a unique URL category (Morichetta, page 286, paragraph 5, "URLs are grouped into well-separated and cohesive clusters" wherein “clusters clearly pinpoint specific services [ categories ]” (Morichetta, page 293, TABLE V)). Benkreira in view of Smith and Morichetta are considered analogous because both relate to grouping behavior information via machine learning. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Benkreira in view of Smith to incorporate the teachings of Morichetta and group the URLs into separate and unique groups. Doing so would “strengthen[] the potential to support the mining of URLs and of web traffic with applications to security and privacy protection fields” (Morichetta, page 287, first paragraph). Regarding Claim 6 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira in view of Smith does not teach, but Morichetta further teaches wherein the URL category mapping is automatically generated (Morichetta, page 286, col 2, second paragraph, “we focus on the problem of automatically analyzing web traffic leveraging URLs. We design an unsupervised methodology that groups URLs in clusters according to a similarity metric”) based on data collected from an application (Morichetta, page 290, col 2, first paragraph “We let Tstat collect URLs for an entire day, generating more than 100GB of data”). Benkreira in view of Smith and Morichetta are analogous because they are in the same field of machine learning where they both collect and organize URLs in clusters. It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Benkreira in view of Smith to automatically generate the URL category mapping based on the mouse data collected. Doing so would “avoid the overhead introduced by web crawling techniques” (Morichetta, page 286, col 2, third paragraph). Regarding Claim 7 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira in view of Smith further teaches using a machine learning clustering on the mouse dynamics data corresponding to a plurality of sessions of various users of an application (Benkreira, Paragraph 0043 "the machine learning model may learn patterns from the set of observations without labeling or supervision, and may provide output that indicates such patterns such as by using clustering and/or association to identify related groups of items within the set of observations" wherein “the set of observations may be obtained and/or input from historical data, such as data gathered during one or more processes [ sessions ]” (Benkreira, page 16, paragraph 0038)). Benkreira in view of Smith does not teach, but Morichetta does teach wherein the URL category mapping is automatically generated using a machine learning clustering (Morichetta, page 286, col 2, second paragraph, “we focus on the problem of automatically analyzing web traffic leveraging URLs. We design an unsupervised methodology that groups URLs in clusters according to a similarity metric”). Benkreira in view of Smith and Morichetta are analogous because they are in the same field of machine learning where they both collect and organize URLs in clusters, 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 Benkreira in view of Smith to automatically generate the URL category mapping based on the mouse data collected. Doing so would “avoid the overhead introduced by web crawling techniques” (Morichetta, page 286, col 2, third paragraph) . 07-22-aia AIA Claim s 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Benkreira in view of Smith as applied to claim s 1-3, 8-13, and 15-20 above, and further in view of Luo et al. (Luo) (US 2020/0034752 A1) Regarding Claim 5 , Benkreira in view of Smith teaches the system of claim 1 (and thus the rejection of claim 1 is incorporated). Benkreira in view of Smith does not teach, but Luo does teach wherein the URL category mapping comprises a predetermined mapping (Luo, page 30, paragraph 0111, “Email classifier 114 classifies emails, such as the email 104 shown in FIG. 1, into one or more of a number of predetermined categories such as good, spam, bulk, phishing, or malware” where “initial labels may come from …URLs” (Luo, page 21, paragraph 0005)). Benkreira in view of Smith and Luo are analogous because they are in the same field of machine learning where they both collect and organize data into clusters. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Benkreira in view of Smith to use a predetermined mapping. Doing so would “creat[e] a labeled, training dataset…without use of confidential information or PII” (Luo, page 21, paragraph 0004). Regarding Claim 14 , Benkreira in view of Smith teaches the computer-implemented method of claim 8 (and thus the rejection of claim 8 is incorporated). Benkreira further teaches a threshold used to generate a decision (Benkreira, page 22, paragraph 0091, “process 600 may include determining that the fraud score satisfies a threshold, and where the recommended action may include a video review action, that requires submission of a video before a completed application form can be submitted to the server device, based on determining that the fraud score satisfies the threshold”). Benkreira in view of Smith does not teach, but Luo does teach adjusting …during a training phase of the trained classification model (page 30, paragraph 0112, “The email classifier uses a MLM to identify a classification… the current model is run with the training dataset 118 and produces a result which is then compared with the target, for each input vector in the training dataset 118. Based on the result of the comparison and the specific learning technology being used, the parameters of the MLM are adjusted”). generate a decision by finding a limit on the false positive rate (page 27, paragraph 0080, “the confidence degrading ratio [ false positive rate ] of clustering edges (dotted lines) may decrease confidence by half by applying a confidence degrading ratio of 0.5 [ finding a limit ]. Thus, if the email 904 is labeled with 100% confidence that it is a “good” email, then the clustering edge 924 reduces that confidence level by half [ generate a decision ]” wherein “the edges represent inference logic that is specific to the category label (“good”) of the expansion graph 900. Thus, for the label “good” if the fingerprint 906 of the email 904 is known to be good then the email itself may be inferred to be a good email based on the edge… However, just because an email includes a good URL 910 that does not necessarily indicate the email itself is good… spam and bulk email may include URLs that are identified as good [spam and bulk emails are falsely labeled as good making the confidence degrading ratio degrade due to false positives]” (Luo, page 26, paragraph 0077)). Benkreira in view of Smith and Luo are analogous because they are in the same field of machine learning where they both collect and organize data into clusters. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Benkreira in view of Smith to fine-tune a threshold by finding a limit on a false positive rate. Doing so would “save network resources such as bandwidth, storage, and processor cycles.” (Luo, page 23, paragraph 0046) . Response to Arguments On page 9, the Applicant argues: One of skill in the art would recognize that the steps in the independent claims, such as claim 1, regarding a system for classifying mouse dynamics data of a session using a trained classification model and providing a decision regarding legitimacy of the session are done by a computer and not a human mind. Therefore, the claims do not involve an abstract idea that can be performed in the human mind. Regarding the Applicant’s arguments that claim 1 does not recite a judicial exception, the Examiner respectfully disagrees. Specifically, the Examiner respectfully notes that claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C). Examiner further notes that classifying mouse dynamics data of a session encompasses a human mentally classifying mouse dynamics data and providing a decision regarding legitimacy of the session encompasses a human mentally providing a decision. On page 9-10, the Applicant argues: The application as filed provides how the subject of the application is an improvement over prior technology that advantageously provides classification of mouse dynamics data using uniform resource locator category mapping. The application provides, at paragraph 15, as follows: Embodiments of the present disclosure enable an improved mouse dynamics analysis (MDA) that takes into account the webpage where the examination happens. Specifically, the embodiments can take into account all possible webpages (or URLs) composing the website, which are grouped into meaningful categories, referred to herein as URL categories. The output of MDA is a set of features containing information about the way the user interacts with every URL category. The extracted features can thus serve as input to a machine learning (ML) model for execution of various tasks such as classification and anomaly detection. In this manner, the embodiments herein enable improved classification and anomaly detection. In particular, the embodiments provide better classification performance by relating the extracted features to each URL category individually, thus increasing the extracted features' statistical power. It is respectfully submitted that claim 1 is directed to a specific implementation of a solution to a technical problem in the technical field of machine learning models. In particular, the claim is directed to a specific implementation that advantageously classifies mouse dynamics data using a trained machine learning model based on URL category mapping, on a computer with improved performance. The claim reflects some improvement in the functioning of a computer and another device, as well as some improvement in another technology or technological field, i.e., in the technological field of machine learning models. In regards to the Applicant’s argument that claim 1 is directed to a specific implementation of a solution that advantageously classifies mouse dynamics data using a trained machine learning model based on URL category mapping, on a computer with improved performance, the Examiner respectfully disagrees. While the Applicant argues that the Specification details a specific implementation of a solution to the technical problem, the examiner respectfully notes that the specific implementation is not described within the claims. As such, the 101 rejections of claims have not been overcome. Examiner recommends amending the claims to include the specific implementations of the improvements in the functioning of the computer to potentially overcome the 101 rejection above. (“In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.” MPEP 2106.05(f)). On page 10-11, the Applicant argues: Similar to example 39, the independent claims do not recite a judicial exception. For example, the independent claims ( claim 1 being used to represent the independent claims) recite, " ... receive mouse dynamics data of the session to be analyzed; process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories; map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping; group the mouse dynamics data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category; separately extract, for each of the plurality of URL categories, a distinct set of mouse dynamics features, wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories; input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of the plurality of URL categories; receive an output score from the trained classification model, wherein the output score represents a legitimacy of the session; and specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold the decision is a classification of the session as legitimate, not legitimate or an outlier." These limitations "may involve or rely upon mathematical concepts" but "the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols" (Reminders Memo, Pg. 3). Therefore, the independent claims are directed to patent-eligible subject matter for at least the same reasons as example 39 and in light of the Reminders Memo. Regarding the Applicant’s argument that the claim does not recite a judicial exception, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that the August 4 Memo does not change process of examining claims under 101. Examiner further notes that “classifying mouse dynamics data of a session” encompasses a human mentally classifying mouse dynamics data and is thus a judgment, “providing a decision regarding legitimacy of the session” encompasses a human mentally providing a decision and is thus a judgment, “process the mouse dynamics data by partitioning a website corresponding to a plurality of uniform resource locators (URLs) included in the mouse dynamics data into a plurality of URL categories” encompasses a human mentally partitioning a website into URL categories and is thus an evaluation, “map each URL visited in the session to at least one URL category of the plurality of URL categories in order to generate a URL category mapping” encompasses a human mentally mapping each URL to a category and is thus an evaluation, “group the mouse dynamics data into a plurality of groups using the URL category mapping, wherein each of the plurality of groups relates to one of the at least one URL category” encompasses a human mentally grouping mouse data by the type of URL the mouse was on and is thus an evaluation, “separately extract, for each of the plurality of URL categories, a distinct set of mouse dynamics features, wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories;” encompasses a human mentally extracting features from each group and is thus an evaluation, and “specify, using a policy, the decision regarding legitimacy based on the output score of the session and whether the output score exceeds a threshold, wherein the decision is a classification of the session as legitimate, not legitimate or an outlier” encompasses a human mentally specifying a decision regarding legitimacy and is thus a judgment. Thus claim 1 recites a judicial exception. On page 11-12, Applicant argues: The Reminders Memo also states, "[t]he examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement" (Reminders Memo, Pg. 4). As provided in the Specification, paragraph 15: " ... the embodiments provide better classification performance by relating the extracted features to each URL category individually, thus increasing the extracted features' statistical power." Applicant's claim 1 is integrated into a practical application because it provides a technical solution that improves a field of technology. For this additional reason, the independent claims are directed toward patent-eligible subject matter. The Reminders Memo further discusses the "Apply It" consideration and states, "[c]laims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself' (Reminders Memo, Pg. 5). As discussed in the Ex Parte Desjardins et al. Rehearing Decision, 2024-000567, Sept. 26, 2025: "Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that' [ m ]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.' 822 F.3d at 1339. Moreover, because' [s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,' the Federal Circuit held that the eligibility determination should turn on whether 'the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.' Id. At 1336." (Desjardins, p. 8). The independent claims clearly apply any judicial exception to realize an improvement in technology for at least the reason that the disclosure describes: " ... embodiments of the present disclosure enable an improved mouse dynamics analysis (MDA) that takes into account the webpage where the examination happens." (Para. 15). The independent claims, such as claim 1, reflects the improvements as well. For example the claim recites: "[a] system for classifying mouse dynamics data of a session using a trained classification model and providing a decision regarding legitimacy of the session .... " For this additional reason, the independent claims are directed toward patent-eligible subject matter. Regarding the Applicant’s argument that the claim integrates the judicial exception into a practical application, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that the August 4 memo does not change process of examining claims under 101. Examiner further respectfully notes that specification sets forth an improvement in a conclusory manner and therefore cannot integrate the exception into a practical solution (MPEP 2104(d)(1)). On page 12, Applicant argues: The dependent claims depend on one of the independent claims (namely, claims 1, 8 or 15) discussed above, and are therefore believed to be patent-eligible for at least the same reasons discussed above that claims 1, 8 and 15 are patent-eligible. Each dependent claim also recites additional features which help provide technological improvement and further justification for a determination of patent-eligibility under 35 U.S.C. § 101. Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above. On page 13, Applicant argues: Applicant respectfully submits the features added to claim 1 by amendment have not been addressed by the Office Action and that Benkreira and Smith do not together teach or suggest the added features. Thus, Applicant asserts that amended claim 1 is allowable and respectfully requests that the 3 5 USC § 103 rejection of claim 1 be withdrawn. Regarding the Applicant’s argument that the features added to claim 1 are not taught by Benkreira and Smith, the Examiner respectfully disagrees. Specifically, a combination of Benkreira and Smith teach all of claim 1. Benkreira and Smith teach the newly amended limitations of: separately extract, for each of the plurality of… categories, a distinct set of mouse dynamics, (Benkreira, Paragraph 0039, "As shown by reference number 210, a feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature….In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from a server device, such as by extracting or generating a name for a column… and/or the like" where “For example, the feature set may include one or more of the following features: …mouse dynamics used to input data into one or more fields or to navigate between field of the application form” (Benkreira, page 16, paragraph 0040) wherein the features are clustered (Benkreira page 19, paragraph 0055), then the features are extracted from each cluster for each feature set. Examiner further notes that the observations are the mouse dynamics.). input the mouse dynamics features into the trained classification model, wherein the trained classification is configured to operate on the mouse dynamics features of each of the plurality of …categories (Benkreira, Paragraph 0003, "provide the device information and the behavior information as a feature set that is input to a machine learning model”); the plurality of URL categories (Smith, page 246, last paragraph – page 247 first paragraph, “in this paper we present LOGSOM, a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns rather than according to the web content [4, 5, 8]. Instead of organizing the web-pages according to the words contained in the webpages, we keep track of the interest of the web-users, and organize the web-pages according to their interest” where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns” (Smith, page 250, 2 nd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph) and where “As the web users visit the Business Systems website http://www.bs.monash.edu.au – including all of its linked web pages), they leave some footprints behind. Like many other servers, that of Business Systems saves the footprints as web server logs, which we have reformatted as shown in Fig. 1” (Smith, page 247, 2 nd column, 2 nd paragraph). Examiner notes that the mouse dynamics data is the user navigation patterns. The website corresponding to a plurality of URLs is the Business Systems website with linked web pages. Examiner further notes that the URL categories are the clusters based on the transactions of users.); wherein the mouse dynamics features contain information about how a user interacts with each of the plurality of URL categories (Smith, page 246, last paragraph, "a prototype system that organizes web pages on a self-organizing map (SOM) according to user navigation patterns" where “By using the K-means cluster algorithm [3], we cluster the transactions into nine groups. The number K=9 is chosen arbitrarily. In fact, we can choose any number as long as it is small enough for the data to carry sufficient information to produce a meaningful outcome. In our experiment, each 235-dimensional binary transaction vector is treated as an input vector and clustered into K=9 groups. These are essentially similarity groups, that is collections of transactions that involve similar web page access patterns. The K-means algorithm is outlined in Fig. 2. Within each processed transaction group, we use the column totals as the activity of the transaction group. This is illustrated in Table 2, where the cluster depicted contains 2679 transactions, including transaction numbers 5, 6, and 8012. Instead of having transactions as features, we now have transaction groups as features. The URLs are now described by the relative interests or activities of each of the transaction groups.” (Smith, page 250, 2 nd -3 rd paragraph) and “we define a transaction as a set of web pages requested by a user in a particular session (Smith, page 247, last paragraph). Examiner notes that the transaction groups are features.); Therefore, the prior art teaches claim 1. Examiner further points the Applicant to the above 103 rejections. On page 13, Applicant argues: Claims 8 and 15 have been amended to include substantially similar features as amended claim 1. Therefore, Applicant respectfully submits that claims 8 and 15 are allowable for the same reasons that claim 1 is allowable and respectfully requests that the 35 USC§ 103 rejection of claims 8 and 15 be withdrawn. Because claims 2-3, 10-13 and 17-20 depend upon and incorporate the limitations of claims 1, 8, and 15, Applicant contends that claims 2-3, 10-13 and 17-20 are allowable for the same reasons that claims 1, 8, and 15 are allowable. Reconsideration and withdrawal of the rejection are respectfully requested. Regarding the Applicant’s argument that claims 8 and 15 overcome the prior art, the Examiner respectfully disagrees. Specifically, claims 8 and 15 recite substantially similar limitations to claim 1, and are therefore rejected under the same analysis. Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above. On page 13, Applicant argues: Claims 4, 6 and 7 depend upon and incorporate the limitations of claim 1. Applicant contends that claims 4, 6 and 7 are allowable over Benkreira in view of Smith for the same reasons that claim 1 is allowable. Morichetta does not remedy the shortcomings of Benkreira and Smith, and claims 4, 6 and 7 are allowable over the combination of references. Reconsideration and withdrawal of the rejection are respectfully requested. Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above. On page 14, Applicant argues: Claims 5 and 14 depend upon and incorporate the limitations of claim 1 or claim 8. Applicant contends that claims 5 and 14 are allowable over Benkreira in view of Smith for the same reasons that claims 1 and 8 are allowable. Luo does not remedy the shortcomings of Benkreira and Smith and claims 5 and 14 are allowable over the combination of references. Reconsideration and withdrawal of the rejection are respectfully requested. Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Solano et al., (US 2021/0264003 A1) also describes a method of receiving mouse data and training it via a machine learning model in order to detect suspicious behavior . Burns et al. (US 8,341,724 B1) also performs further inspection on suspicious sessions and either blocks the confirmed suspicious sessions or sends the session to a security management module for further analysis (Burns et al., page 13, column 7, lines 23-46). The management module provides a user interface for a human to further inspect a session (Burns et al., page 14, column 9 line 65 – column 10, line 17) Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 7:15 am - 5:15 pm EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /K.R.L./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148 Application/Control Number: 17/380,738 Page 2 Art Unit: 2148 Application/Control Number: 17/380,738 Page 3 Art Unit: 2148 Application/Control Number: 17/380,738 Page 4 Art Unit: 2148 Application/Control Number: 17/380,738 Page 5 Art Unit: 2148 Application/Control Number: 17/380,738 Page 6 Art Unit: 2148 Application/Control Number: 17/380,738 Page 7 Art Unit: 2148 Application/Control Number: 17/380,738 Page 8 Art Unit: 2148 Application/Control Number: 17/380,738 Page 9 Art Unit: 2148 Application/Control Number: 17/380,738 Page 10 Art Unit: 2148 Application/Control Number: 17/380,738 Page 11 Art Unit: 2148 Application/Control Number: 17/380,738 Page 12 Art Unit: 2148 Application/Control Number: 17/380,738 Page 13 Art Unit: 2148 Application/Control Number: 17/380,738 Page 14 Art Unit: 2148 Application/Control Number: 17/380,738 Page 15 Art Unit: 2148 Application/Control Number: 17/380,738 Page 16 Art Unit: 2148 Application/Control Number: 17/380,738 Page 17 Art Unit: 2148 Application/Control Number: 17/380,738 Page 18 Art Unit: 2148 Application/Control Number: 17/380,738 Page 19 Art Unit: 2148 Application/Control Number: 17/380,738 Page 20 Art Unit: 2148 Application/Control Number: 17/380,738 Page 21 Art Unit: 2148 Application/Control Number: 17/380,738 Page 22 Art Unit: 2148 Application/Control Number: 17/380,738 Page 23 Art Unit: 2148 Application/Control Number: 17/380,738 Page 24 Art Unit: 2148 Application/Control Number: 17/380,738 Page 25 Art Unit: 2148 Application/Control Number: 17/380,738 Page 26 Art Unit: 2148 Application/Control Number: 17/380,738 Page 27 Art Unit: 2148 Application/Control Number: 17/380,738 Page 28 Art Unit: 2148 Application/Control Number: 17/380,738 Page 29 Art Unit: 2148 Application/Control Number: 17/380,738 Page 30 Art Unit: 2148 Application/Control Number: 17/380,738 Page 31 Art Unit: 2148 Application/Control Number: 17/380,738 Page 32 Art Unit: 2148 Application/Control Number: 17/380,738 Page 33 Art Unit: 2148 Application/Control Number: 17/380,738 Page 34 Art Unit: 2148 Application/Control Number: 17/380,738 Page 35 Art Unit: 2148 Application/Control Number: 17/380,738 Page 36 Art Unit: 2148 Application/Control Number: 17/380,738 Page 37 Art Unit: 2148 Application/Control Number: 17/380,738 Page 38 Art Unit: 2148 Application/Control Number: 17/380,738 Page 39 Art Unit: 2148 Application/Control Number: 17/380,738 Page 40 Art Unit: 2148 Application/Control Number: 17/380,738 Page 41 Art Unit: 2148 Application/Control Number: 17/380,738 Page 42 Art Unit: 2148 Application/Control Number: 17/380,738 Page 43 Art Unit: 2148 Application/Control Number: 17/380,738 Page 44 Art Unit: 2148 Application/Control Number: 17/380,738 Page 45 Art Unit: 2148 Application/Control Number: 17/380,738 Page 46 Art Unit: 2148 Application/Control Number: 17/380,738 Page 47 Art Unit: 2148 Application/Control Number: 17/380,738 Page 48 Art Unit: 2148 Application/Control Number: 17/380,738 Page 49 Art Unit: 2148 Application/Control Number: 17/380,738 Page 50 Art Unit: 2148 Application/Control Number: 17/380,738 Page 51 Art Unit: 2148 Application/Control Number: 17/380,738 Page 52 Art Unit: 2148 Application/Control Number: 17/380,738 Page 53 Art Unit: 2148 Application/Control Number: 17/380,738 Page 54 Art Unit: 2148 Application/Control Number: 17/380,738 Page 55 Art Unit: 2148 Application/Control Number: 17/380,738 Page 56 Art Unit: 2148 Application/Control Number: 17/380,738 Page 57 Art Unit: 2148 Application/Control Number: 17/380,738 Page 58 Art Unit: 2148 Application/Control Number: 17/380,738 Page 59 Art Unit: 2148 Application/Control Number: 17/380,738 Page 60 Art Unit: 2148 Application/Control Number: 17/380,738 Page 61 Art Unit: 2148 Application/Control Number: 17/380,738 Page 62 Art Unit: 2148 Application/Control Number: 17/380,738 Page 63 Art Unit: 2148 Application/Control Number: 17/380,738 Page 64 Art Unit: 2148 Application/Control Number: 17/380,738 Page 65 Art Unit: 2148 Application/Control Number: 17/380,738 Page 66 Art Unit: 2148 Application/Control Number: 17/380,738 Page 67 Art Unit: 2148 Application/Control Number: 17/380,738 Page 68 Art Unit: 2148 Application/Control Number: 17/380,738 Page 69 Art Unit: 2148 Application/Control Number: 17/380,738 Page 70 Art Unit: 2148 Application/Control Number: 17/380,738 Page 71 Art Unit: 2148 Application/Control Number: 17/380,738 Page 72 Art Unit: 2148 Application/Control Number: 17/380,738 Page 73 Art Unit: 2148
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