DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/1/2026 has been entered.
The claim Objections regarding to claims 1 and 12 is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 12, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73 in view of Fernandez et al. (Fernandez) US 2023/0281278 and Xue et al. (Xue) US 2013/0219505
In regard to claim 1, Roehm disclose A computer implemented method for analyzing an usage of an application provided by a software vendor, wherein the method, that by way of a computer comprising one or more hardware processors and memory coupled to the one or more hardware processors, wherein the memory includes computer instructions which when executed by the one or more hardware processors causes the one or more hardware processors to perform the method, comprises the steps of: (see abstract and page 465, section ''A. Motivating Example", computer implemented method for analyzing the usage of an application, e.g, an online banking application)
instrumenting the application and an environment in which the application runs to generate execution traces at use-case reference points; (p. 465~466, “A. Motivating Example"-section “C. General Framework", instrumentation of the application source code in the application's environment, e.g. using a sensor monitoring log files at use case steps, see Fig. 1);
capturing said execution traces during user interaction with the application during a use- case scenario; (page 465, section "A Motivating Example", monitoring of user interaction during a banking use case with user interactions, see Fig.1)
applying a classification model to uncorrelated execution traces to report a use of unseen use-case scenarios; (p. 466, section "D. Detection of the Current Use Case", application of a machine teaming model to detect use case scenarios; p. 466, section "F. Exploitation of Detected Differences'', reporting of undocumented use-case scenarios)
But Roehm fail to explicitly disclose “wherein the generated event traces of the application due to the binary rewriting comprise metadata about the license account executing the application, and detect activities of the application with respect to authentication services for single sign- on (SSO) and authorization services to address the metadata and access privileges; collecting the metadata associated with said application and events during user interaction with the application during said use-case scenario; including said metadata within said learning of said classification model, wherein said metadata comprises information about said software license; and detecting and reporting a software license issue with said application during said use case scenario for said events and said unseen use-case scenarios, wherein said metadata comprises information about said software license consisting of at least one among timestamps, access permissions, and number of runtime allowances; thereby providing to an Independent Service Vendor (ISV) associated with the application, a measure of value that the user receives for said software license from using the application with said authentication and authorization services.”
Fernandez disclose wherein the generated event traces of the application due to the user license checking process comprise metadata about the license account executing the application, ([0004]-[0006] [0038]-[0042][0066]-[0068] [0074]-[0079] ][0093]-[0098] [0116]-[0122] the events with the user activities, such as account creation, sign-on, user account suspension, etc. which are information about the license account in the validating process)
and detect activities of the application with respect to authentication services for single sign- on (SSO) and authorization services to address the metadata and access privileges; ([0066]-[0068] [0074]-[0078][0093]-[0098] [0116]-[0122] track usage of the application by the user, such as SSO and detecting the access and use of saved passwords, etc. information or a user with authority to access, etc. information )
collecting the metadata associated with said application and events during user interaction with the application during said use-case scenario; ([0066]-[0068] [0073]-[0078][0093]-[0098] [0116]-[0122] gather usage data regarding software licenses by track usage of the application by the user, such as what type of activity the user performs, determine time the user access the application, how long each access session, detect a use of SSO, etc. during the application session and the data is stored)
including said metadata within said learning of said classification model, wherein said metadata comprises information about said software license; ([0066]-[0068] [0077]-[0078][0091]-[0098] [0114]-[0122] [0136]-[0137] include data or information about the software license in the ML model) and
detecting and reporting a software license issue with said application during said use case scenario for said events and said unseen use-case scenarios; ([0066]-[0068] [0077]-[0082][0093]-[0098] [0114]-[0122] reporting issues with the software application
when detecting user activities such as the user login frequency, etc. during application sessions)
wherein said metadata comprises information about said software license consisting of at least one among timestamps, access permissions, and number of runtime allowances, ([0026]-[0028][0077]-[0078][0093]-[0097] [0114]-[0122] [0150] the data include every time user access the application, access permission, user privileges, etc. )
thereby providing to an Independent Service Vendor (ISV) associated with the application, a measure of value that the user receives for said software license from using the application with said authentication and authorization services. ([0051]-[0054] [0104]-[0122] [0140]-[0150] provide to the vendor associated with the application, a calculated value that the user receives for the software licensing from using the application with the authentication and authorization services)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Fernandez’s software license management platform into Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Fernandez’s software license management based on user activity detection would help to provide more user software license usage information into Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user software license usage information would help to improve user behavior identification.
But Roehm and Fernandez fail to explicitly disclose “instrumenting the application via binary rewriting to add automatic license verification to the software of the application and the environment, due to the binary rewriting.”
Xue disclose instrumenting the application via binary rewriting to add automatic license verification to the software of the application and the environment ([0022] [0029]-[0041] using the existing APIs and made the application to mount a page blob as a drive letter, and create an ID that instance can possess, etc. which involve binary instructions rewriting and to validating the license for the application, the ID is based on a unique name associated with a cloud-based object within the cloud computing env. Note: please further define how the automatic license verification is added base on what criteria, it appears the ML is used, may be dependent claim 5-7 helps, call to discuss if necessary)
due to the binary rewriting ([0022] [0029]-[0041] in response to a validation event, using the existing APIs and made the application to mount a page blob as a drive letter, and create an ID that instance can possess, etc. which involve binary instructions rewriting and to validating the license for the application, the ID is based on a unique name associated with a cloud-based object within the cloud computing env. Here it discloses an event trigger)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Xue’s method of license validating into Fernandez and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Xue’s method of license validating based on event detection would help to provide more license validating method into Fernandez and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing license validating based on event detection would facilitate license validations.
In regard to claim 2, Roehm and Fernandez, Xue disclose The method of claim 1,
Roehm disclose further comprising:
presenting the user with a reference use-case scenario to follow by way of the application; (p 464, abstract. “I. Introduction” present the user with use case specification and description to follow for the application)
correlating said execution traces to a sequence of interaction steps of said use-case scenario; (page 465-466, section "A. Motivating Example" and “E. Comparison of Use Cases and Monitored User Actions” user events and device events are captured and correlated to user interaction steps of use case scenarios, use sequential pattern mining to detect patterns in traces of monitored user actions, etc.)
repeating said steps of presenting and capturing for multiple use-case scenarios; (p 464-466, abstract. “I. Introduction” present the user with use case specification and description to follow for the application and page 465, section "A Motivating Example", monitoring of user interaction during a banking use case with user interactions, see Fig.1 and present, capture and compare behaviors of users to online banking activities to end-users)
producing reference event traces with reference usage scenarios for input to a training process of said classification model to map presented reference use-case scenarios to known use-case scenarios; (page 465-466, abstract. “I. Introduction”, “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” detect a current use case of a user using ML, and compare use case steps to monitored user actions with the use case descriptions and produce event traces with a use case as input to train the ML model) and
updating a learning of said classification model for said execution traces from said repeating and producing. (page 465-466, abstract. “I. Introduction”, “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” detect a current use case of a user using ML, and compare use case steps to monitored user actions with the use case descriptions and the steps are iterated and produce event traces with a use case as input to train the ML model and training program is revised or refined base on knowledge about application usage)
In regard to claim 3, Roehm and Fernandez, Xue disclose The method of claim 2,
Roehm disclose further comprising:
detecting an event during a use-case among those of a user event, a device event or environmental event; (page 465, section "A Motivating Example", monitoring of user interaction during a banking use case with user interactions, see Fig.1 and user events and device events are captured)
generating execution traces responsive to said detection of said event; capturing and correlating said event to a user interaction step of said use-case scenario; (page 465-466, section "A. Motivating Example" and “E. Comparison of Use Cases and Monitored User Actions” user events and device events are captured and correlated to user interaction steps of use case scenarios, use sequential pattern mining to detect patterns in traces of monitored user actions is generated, detecting and correlated user actions to user interaction steps of use case scenarios Fig. 1) and
updating said learning on said use of said use-case in view of said event. (page 465-466, abstract. “I. Introduction”, “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” training program is revised or refined base on knowledge about application usage in view of the user actions observed)
In regard to claim 12, Roehm disclose A computer implemented method for analyzing an usage of an application by a user, said application provided by a software vendor, wherein the method, that by way of a computer comprising one or more hardware processors and memory coupled to the one or more hardware processors, wherein the memory includes computer instructions which when executed by the one or more hardware processors causes the one or more hardware processors to perform the method, (see abstract and page 465, section ''A. Motivating Example", computer implemented method for analyzing the usage of an application, e.g, an online banking application) comprises the steps of:
instrumenting both the application and an environment in which the application runs to generate execution traces at use-case reference points; (p. 465~466, “A. Motivating Example"-section “C. General Framework", instrumentation of the application source code in the application's environment, e.g. using a sensor monitoring log files at use case steps, see Fig. 1);
presenting the user with a reference use-case scenario to follow byway of the application; (p 464, abstract. “I. Introduction” present the user with use case specification and description to follow for the application)
capturing said execution traces during user interaction with the application and in the environment during said reference use-case scenario; (page 465, section "A Motivating Example", monitoring of user interaction during a banking use case with user interactions, see Fig.1)
correlating said execution traces to a sequence of interaction steps of said reference use-case scenario; (page 465-466, section "A. Motivating Example" and “E. Comparison of Use Cases and Monitored User Actions” user events and device events are captured and correlated to user interaction steps of use case scenarios, use sequential pattern mining to detect patterns in traces of monitored user actions, etc.)
repeating said steps of presenting, capturing and correlating for multiple reference use- case scenarios; (p 464-466, abstract. “I. Introduction” present the user with use case specification and description to follow for the application and page 465, section "A Motivating Example", and “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” monitoring of user interaction during a banking use case with user interactions, see Fig.1 and present, capture and use case steps to monitored user actions with the use case descriptions and the steps are iterated)
producing reference event traces with reference use-case scenarios for input to a training process of said classification model that maps presented reference use-case scenarios to known use-case scenarios; (page 465-466, abstract. “I. Introduction”, “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” detect a current use case of a user using ML, and compare use case steps to monitored user actions with the use case descriptions and produce event traces with a use case as input to train the ML model)
learning a classification model for said execution traces from said repeating;
and thereafter said learning, (page 465-466, abstract. “I. Introduction”, “D. Detection of the Current Use Case” and “E. Comparison of Use Cases and Monitored User Actions” detect a current use case of a user using ML, and compare use case steps to monitored user actions with the use case descriptions and the steps are iterated and produce event traces with a use case as input to train the ML model and training program is revised or refined base on knowledge about application usage)
applying said classification model to uncorrelated execution traces to report a use of unseen use-case scenarios; (p. 466, section "D. Detection of the Current Use Case", application of a machine teaming model to detect use case scenarios; p. 466, section "F. Exploitation of Detected Differences'', reporting of undocumented use-case scenarios)
But Roehm fail to explicitly disclose “wherein the generated event traces of the application comprise metadata about the license account executing the application, to detect activities of the application with respect to authentication services for single sign- on (SSO) and authorization services to address the metadata and access privileges;
collecting the metadata associated with said application and events during user interaction with the application during said reference use-case scenario; and including said metadata within said learning of said classification model, wherein said metadata comprises information about said software license; and detecting and reporting a software license issue with said application during said reference use case scenario for said events and said unseen use-case scenarios, thereby providing to an Independent Service Vendor (ISV) associated with the application, a measure of value that the user receives for said software license from using the application with said authentication and authorization services,”
Fernandez disclose wherein the generated event traces of the application comprise metadata about the license account executing the application, ([0004]-[0006] [0038]-[0042][0066]-[0068][0093]-[0098] [0116]-[0121] detecting user inputs and determining a software license or subscription associated with the application user account and user status of the software license based on data associated with the application user account, etc.)
to detect activities of the application with respect to authentication services for single sign- on (SSO) and authorization services to address the metadata and access privileges; ([0066]-[0068] [0077]-[0078][0093]-[0098] [0116]-[0121] track usage of the application by the user, such as SSO and detecting the access and use of saved passwords, etc. information or a user with authority to access, etc. information )
collecting the metadata associated with said application and events during user interaction with the application during said reference use-case scenario; ([0066]-[0068] [0073]-[0078][0093]-[0098] [0116]-[0121] gather usage data regarding software licenses by track usage of the application by the user, such as what type of activity the user performs, determine time the user access the application, how long each access session, detect a use of SSO, etc. during the application session and the data is stored)
and including said metadata within said learning of said classification model, wherein said metadata comprises information about said software license; ([0066]-[0068] [0077]-[0078][0091]-[0098] [0114]-[0121] [0136]-[0137] include data or information about the software license in the ML model) and
detecting and reporting a software license issue with said application during said reference use case scenario for said events and said unseen use-case scenarios. ([0066]-[0068] [0077]-[0082][0093]-[0098] [0114]-[0122] reporting issues with the software application when detecting user activities such as the user login frequency, etc. during application sessions)
thereby providing to an Independent Service Vendor (ISV) associated with the application, a measure of value that the user receives for said software license from using the application with said authentication and authorization services. ([0051]-[0054] [0104]-[0122] [0140]-[0150] provide to the vendor associated with the application, a calculated value that the user receives for the software licensing from using the application with the authentication and authorization services)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Fernandez’s software license management platform into Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Fernandez’s software license management based on user activity detection would help to provide more user software license usage information into Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user software license usage information would help to improve user behavior identification.
But Roehm and Fernandez fail to explicitly disclose “instrumenting both the application via binary rewriting to add automatic license verification to the software of the application and the environment,”
Xue disclose instrumenting both the application via binary rewriting to add automatic license verification to the software of the application and the environment, ([0022] [0029]-[0041] using the existing APIs and made the application to mount a page blob as a drive letter, and create an ID that instance can possess, etc. which involve binary instructions rewriting and to validating the license for the application, the ID is based on a unique name associated with a cloud-based object within the cloud computing env.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Xue’s method of license validating into Fernandez and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Xue’s method of license validating based on event detection would help to provide more license validating method into Fernandez and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing license validating based on event detection would facilitate license validations.
In regard to claim 13, claim 13 is a method claim corresponding to the method claim 3 above and, therefore, is rejected for the same reasons set forth in the rejections of claim
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73 and Fernandez et al. (Fernandez) US 2023/0281278, and Xue et al. (Xue) US 2013/0219505
as applied to claim 1, further in view of Cranfill et al. (Cranfill) US 2019/0347181
In regard to claim 4, Roehm and Fernandez, Xue disclose The method of claim 3,
Roehm disclose further comprising:
detecting multiple events during a same user interaction step; (page 465-466, section "A Motivating Example", and “E. Comparison of Use Cases and Monitored User Actions” monitoring of user interaction during a banking use case with user interactions, see Fig.1 and one or more user action types are monitored corresponding to the use case step) and
But Roehm and Fernandez, Xue fail to explicitly disclose “differentiating between said multiple events, wherein a user event comprises at least one among a user interface action or touch, a device event comprises at least one among an abrupt movement or abrupt orientation, an environmental event comprises one among a sound, a voice, a location, or image.”
Cranfill disclose differentiating between said multiple events, wherein a user event comprises at least one among a user interface action or touch, a device event comprises at least one among an abrupt movement or abrupt orientation, an environmental event comprises one among a sound, a voice, a location, or image. ([0037]-[0039] [0055]-[0059] [0126] [0234]-[0235] [00487] a different type of operation, a click on the user interface such as playing a sound with a speaker associated with the device, location and rotation of the device, receive light form the env. etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Cranfill‘s method of presenting device usage on a device into Xue, Fernandez and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Cranfill‘s presenting device usage on a device based on the different events detected would help to provide more user usage information into Xue, Fernandez and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user usage information would help to improve user behavior identification.
In regard to claim 5, Roehm, Fernandez and Cranfill, Xue disclose The method of claim 4,
Roehm disclose further comprising sending said execution traces and said user interaction steps to a back-end server for storage and for performing said learning by way of a machine learning algorithm; (page 465-466, section "C. General Framework” and “D. Detection of the Current Use Case” client component processes and aggregates the user actions and corresponding use case steps and send them to server component for store them in the database and for learning by ML algorithm) and
matching sets of execution traces automatically with reference use-case scenarios in a training process by said machine learning. (page 465-466, “I. Introduction”, section "C. General Framework” and “D. Detection of the Current Use Case” comparing user case steps and user actions in the training process)
In regard to claim 6, Roehm, Fernandez and Cranfill, Xue disclose The method of claim 5,
Roehm disclose wherein the machine learning associates an execution of the application with at least one use-case scenario where a classification error is below a pre- established threshold. (page 465-466, “D. Detection of the Current Use Case” and E. Comparison of Use Cases and Monitored User Actions” the training program associated with the application execution with the use case where the similarity exceed a threshold parameter (corresponding to a classification error below the threshold), the user action maps to the use case)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73, Fernandez et al. (Fernandez) US 2023/0281278, and Xue et al. (Xue) US 2013/0219505 and Cranfill et al. (Cranfill) US 2019/0347181 as applied to claim 6, further in view of Singh et al. (Singh) US 2015/0262107
In regard to claim 7, Roehm, Fernandez and Cranfill, Xue disclose The method of claim 6,
But Roehm, Fernandez and Cranfill, Xue fail to explicitly disclose “further comprising updating a sensitivity analysis model configured to determine a weight of each execution trace in the reference use-case scenarios, wherein the sensitivity analysis model classifies each execution trace, or group of execution traces, associated to a reference use-case scenario as a main or an auxiliary event trace, or group thereof, according to their weight.”
Singh disclose further comprising updating a sensitivity analysis model configured to determine a weight of each execution trace in the reference use-case scenarios, wherein the sensitivity analysis model classifies each execution trace, or group of execution traces, associated to a reference use-case scenario as a main or an auxiliary event trace, or group thereof, according to their weight. ([0007]-[0013] [0032]-[0037] [0052]-[0064] determine a weighted average of metrics of the user experiences with a total score and classify the metrics into the core or secondary metrics based on the impact which corresponding to their weight.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Singh‘s customer experience measurement system into Xue, Fernandez, Cranfill and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Singh‘s method of customer experience measurement would help to provide more user usage information into Xue, Fernandez, Cranfill and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user usage information to classify the usage would help to improve user behavior identification.
Claims 10, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73, Fernandez et al. (Fernandez) US 2023/0281278, Xue et al. (Xue) US 2013/0219505 and Cranfill et al. (Cranfill) US 2019/0347181 as applied to claim 6, further in view of Stickle et al. (Stickle) US 2014/0258506
In regard to claim 10, Roehm, Fernandez and Cranfill, Xue disclose The method of claim 6,
Roehm disclose comprising algorithms of a machine learning classification model; (page 465-466, section "C. General Framework” and “D. Detection of the Current Use Case” client component processes and aggregates the user actions and corresponding use case steps and send them to server component for store them in the database and for learning by ML algorithm)
But Roehm, Fernandez and Cranfill, Xue fail to explicitly disclose “comprising: deploying the application with a package, and reporting the use of the use-case scenario continually in real-time,”
Stickle disclose comprising: deploying the application with a package, and reporting the use of the use-case scenario continually in real-time, ([0026] [0037]-[0038] employ the machine instance with a software library through which usage of the application is reported and the frequency can be configured, )
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Stickle‘s tracking application usage into Xue, Fernandez, Cranfill and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Stickle‘s tracking application usage with a software library would help to provide more user usage package into Xue, Fernandez, Cranfill and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user usage package would facilitate the execution of the usage application.
In regard to claim 15, Roehm and Stickle, Xue disclose The method of claim 12,
Roehm disclose instrumented to expose data relevant to use-case information and detect activities of the application with respect to. (page 465-466, section "A Motivating Example", “C. General Framework” monitoring of user interaction during a banking use case with user interactions, see Fig.1 and user events and device events are captured and aggregated)
But Roehm, Fernandez and Stickle, Xue fail to explicitly disclose “wherein the cloud load balancer is further configured by way of: Identify and Access Management (IAM) Service Components: authentication services including one among multi- factor authentication (MFA), session and token management; authorization services to address roles, rules, and attributes; directory services including one among an identity store, directory federation, metadata synchronization, and virtual directory; and user management to provide provisioning and release of services, self-service, and delegation of actions and responsibilities.”
Cranfill disclose wherein the cloud load balancer is further configured by way of:
identify and Access Management (IAM) Service Components: authentication services including one among multi- factor authentication (MFA), session and token management; authorization services to address roles, rules and attributes; directory services including one among an identity store, directory federation, metadata synchronization, and virtual directory; and user management to provide provisioning and release of services, self-service, and delegation of actions and responsibilities. ([0060]-[0064] [0216]-[0230] [0265]-[0266] [0312] [0365] [0399]-[0403] [0521] [0576]-[0579] identify service components, authentication with sign on with password and biometrics. etc. indication of the duration of the longest usage session, session restriction, address role, rules, access restriction, attributes, metadata, etc. and storage with user identifies, records, etc. delegation of actions and responsibilities such as (parent and child), provide the app. etc. all those limitations are common practice in the technical field and are well known to the people with skill in the art and not an invention)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Cranfill‘s method of presenting device usage on a device into Xue, Fernandez, Stickle and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Cranfill‘s presenting device usage on a device based on the different events detected with authentication would help to detect usage with security into Xue, Fernandez, Stickle and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that detecting usage with security would help to improve the communication security and therefore improve user experience using the device
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73, Fernandez et al. (Fernandez) US 2023/0281278, Xue et al. (Xue) US 2013/0219505, Cranfill et al. (Cranfill) US 2019/0347181 and Stickle et al. (Stickle) US 2014/0258506 as applied to claim 10, further in view of Price et al. (Price) US 2019/0236394
In regard to claim 11, Roehm, Fernandez, Cranfill, Stickle, Xue disclose The method of claim 10,
Roehm disclose learning temporal dependencies and sequential user interaction steps of said use-case scenario from said training patterns. (page 465-466, section "C. General Framework” and “E. Comparison of Use Cases and Monitored User Actions” user events and device events are captured and correlated to user interaction steps of use case scenarios, use sequential pattern (which inherently teach time dependencies of the user actions) mining to detect patterns in traces of monitored user actions to train the ML)
But Roehm, Fernandez, Stickle, Cranfill, Xue fail to explicitly disclose “wherein the machine learning is by way of a convolutional neural network configured for RGB image processing that receives as training patterns: execution trace vectors, use-case vectors, and event vectors for respective R,G, B components; and a recurrent neural network.”
Price disclose wherein the machine learning is by way of a convolutional neural network configured for RGB image processing that receives as training patterns: execution trace vectors, use-case vectors, and event vectors for respective R,G, B components; and a recurrent neural network. (Fig. 2A, [0049][0074]-[0094] using CNN and RNN to learn and receive pattern, corresponding to use interaction, use case, and trace vectors for respective R, G, B object in the image)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Price‘s using deep learning to select objects in digital visual media into Xue, Fernandez, Stickle, Cranfill and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Price‘s using deep learning to select objects in digital visual media would help to provide more user usage information into Xue, Stickle, Fernandez, Cranfill and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more user usage information would help to improve user behavior identification.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Roehm et al. (Roehm) “Towards Identification of Software Improvements and Specification Updates By Comparing Monitored and Specified End-User Behavior.” 2013 IEEE International Conference on Software Maintenance, Date of Conference: 22-28 September 2013, DOI: 10.1109/ICSM.2013.73, Fernandez et al. (Fernandez) US 2023/0281278, and Xue et al. (Xue) US 2013/0219505 as applied to claim 12, further in view of Stickle et al. (Stickle) US 2014/0258506
In regard to claim 14, Roehm and Fernandez, Xue disclose The method of claim 12,
But Roehm and Fernandez, Xue fail to explicitly disclose “wherein the environment includes a cloud load balancer, instrumented to expose data relevant to use-case information and detect activities of the application with respect to: distributing user traffic and tasks across multiple instances of applications, reducing risk that the application experiences limited performance issues, and optimize network response time and avoid uneven overloading.”
Stickle disclose wherein the environment includes a cloud load balancer, instrumented to expose data relevant to use-case information and detect activities of the application with respect to: distributing user traffic and tasks across multiple instances of applications, reducing risk that the application experiences limited performance issues, and optimize network response time and avoid uneven overloading. ([0016] [0056] [0057] perform load balancing to distribute the user traffic and tasks to increase performance)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Stickle‘s tracking application usage into Xue, Fernandez and Roehm’s invention as they are related to the same field endeavor of identifying end user behavior. The motivation to combine these arts, as proposed above, at least because Stickle‘s tracking application usage with load balancing would provide mechanism to control the traffic flow into Xue, Fernandez and Roehm’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that controlling the traffic flow by using load balancing would help to improve the network performance and therefore improve user experience using the device.
Response to Arguments
Applicant’s arguments with respect to claims 1-7, 9-15 filed on 3/1/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20150101059 A1 2015-04-09 Galpin et al.
Application License Verification
Galpin et al. disclose An application may provide an application identifier to a client as part of a validation request. The client may request a validation token from a server using the application identifier and a user token provided by the client. The server may send a validation token to the client which, in turn, may send the validation token to the application. The application may establish a secure connection to the server and present the validation token to the server as part of a validation request. The server may validate the application in response to the validation request. The server's response may indicate that the application or content contained in the application is licensed… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143