7Notice 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted by applicant dated 03/23/2026 and 08/25/2026 have been considered by the examiner.
Response to Amendment
Applicants’ arguments have been fully considered. However, upon further consideration, a new ground(s) of rejection is made in view of GANTMAN (US 20160285897 A1).
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.
Claim(s) 1, 2, 6, 12, 16, 17, 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1).
Regarding claim 1, FAN teaches a method comprising:
determining, by a computing system and based on application policy information for an application, one or more application policies for the application (Para [n0051]: automatically analyze the privacy policy of application software.);
monitoring, by the computing system, execution of the application to determine a set of application behaviors (Claim 1: Step S105: reversely compiling the software S to be detected; re-packing the application program and running on the device; obtaining the user interface component of the software S to be detected; understanding the data type collected by the component.);
comparing, by the computing system, the set of application behaviors to the one or more application policies (Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106.); and
outputting, by the computing system, an indication of whether one or more application behaviors from the set of application behaviors are consistent with the one or more application policies (Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106, if the action in ACP is not included in the DCP, judging that the APP data related behaviour is inconsistent with the privacy policy description.).
Fan does not explicitly disclose wherein monitoring the execution of the application comprises: sending, by the computing system and to at least one virtual device in communication with the computing system, at least one command to cause the at least one virtual device to execute the application; and receiving, by the computing system and from the at least one virtual device, information indicative of the set of application behaviors.
GANTMAN teaches wherein monitoring the execution of the application comprises (Para [0122]):
sending, by the computing system and to at least one virtual device in communication with the computing system, at least one command to cause the at least one virtual device to execute the application (Para [0122]: the computing device may emulate the execution environment of the client computing device and/or the behavior monitoring and analysis system of the client computing device, execute the software application in the emulated execution environment, perform behavior-based analysis operations to identify all the operations performed or behaviors exhibited by the software application, and generate the behavior information structure.); and
receiving, by the computing system and from the at least one virtual device, information indicative of the set of application behaviors (Para [0122]: the computing device may emulate the execution environment of the client computing device and/or the behavior monitoring and analysis system of the client computing device, execute the software application in the emulated execution environment, perform behavior-based analysis operations to identify all the operations performed or behaviors exhibited by the software application, and generate the behavior information structure.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN with the teachings of GANTMAN to include wherein monitoring the execution of the application comprises: sending, by the computing system and to at least one virtual device in communication with the computing system, at least one command to cause the at least one virtual device to execute the application; and receiving, by the computing system and from the at least one virtual device, information indicative of the set of application behaviors in order to perform automated threat mitigation without risking the safety of the system.
Regarding claim 2, FAN IN VIEW OF GANTMAN teaches the method of claim 1, wherein determining the one or more application policies comprises:
applying, by the computing system, a set of natural language processing classifiers to the application policy information for the application to generate the one or more application policies (Para [n0053]. Claim 1: combining the natural language processing technology in the text analysis and the code analysis in the software, safety is the application of emerging artificial intelligence technology in software safety from step S101-S104 according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S. NLP is the technology that powers the analysis of the written privacy policy to extract the stated data collection practices (DCP).)
Regarding claim 6, FAN IN VIEW OF GANTMAN teaches the method of any of claim 1, further comprising, prior to determining the one or more application policies:
monitoring, by the computing system, execution of the application to determine an initial set of application behaviors (Claim 1: Step S105: reversely compiling the software S to be detected; writing and obtaining the activity of the user interface component; re-packing the application program and running on the device; obtaining the user interface component of the software S to be detected; understanding the data type collected by the component.); and
determining, based on the initial set of application behaviors, proposed application policy information (Claim 1: Step S106: using data stream analysis method, identifying and obtaining the control of the specific data and the transmission object of the inspection data, associating the component with the data stream, constructing data related control set ACP.).
As per claim 12, the claim claiming a computing system essentially corresponding to the method claim 1 above, and they are rejected, at least for the same reasons.
As per claim 16, the claim claiming a computing system essentially corresponding to the method claim 5 above, and they are rejected, at least for the same reasons.
As per claim 17, the claim claiming a computing system essentially corresponding to the method claim 6 above, and they are rejected, at least for the same reasons.
As per claim 19, the claim claiming a non-transitory computer-readable storage medium essentially corresponding to the method claim 1 above, and they are rejected, at least for the same reasons
Claim(s) 3, 4, 15, 20, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of Cho (US 12130852 B1).
Regarding claim 3, FAN IN VIEW OF GANTMAN teaches the method of claim 2, further comprising:
training, using the first set of natural language processing classifiers, a second set of natural language processing classifiers as multi-label classification models (Claim 1: Step S101: for the to-be-detected software S and the data set D of the category to be detected, obtaining their privacy policy; removing the non-character content part, converting each sentence into feature vector according to the composition condition of the word; Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers.),
wherein applying the set of natural language processing classifiers to the application policy information includes applying the multi-label classification models to the application policy information (Claim 1: Step S101: for the to-be-detected software S and the data set D of the category to be detected, obtaining their privacy policy; removing the non-character content part, converting each sentence into feature vector according to the composition condition of the word; Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S.), and
wherein the one or more application policies include a respective policy label for one or more segments of the application policy information (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete.).
FAN IN VIEW OF GANTMAN does not disclose training a first set of natural language processing classifiers as binary classification models.
Cho teaches training a first set of natural language processing classifiers as binary classification models (Col 8 lines 15-32: Each model 330-1 and 330-2 undergoes a learning or training phase 602 evolve to a production phase 604 for binary and multiclass classification.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Cho to include training a first set of natural language processing classifiers as binary classification models in order to resulting the classification probability to have a higher accuracy (Cho Col 2).
Regarding claim 4, FAN IN VIEW OF GANTMAN teaches the method of claim 3, wherein training the first set of natural language processing classifiers comprises:
indexing a series of application policies (FAN Para [n0051]: automatically analyze the privacy policy of different types of application software.);
receiving a query for a particular type of data (FAN Claim 1: Step 101: for the to-be-detected software S and the data set D.);
responsive to receiving the query, outputting a set of query results that includes application policy information of the particular type of data (FAN Claim 1: S102, classifying the privacy policy P of the to-be-detected software.);
generating, based on the set of query results, an initial training data set (FAN Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers.); and
training the first set of natural language processing classifiers using the initial training data set (FAN Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers.).
Regarding claim 15, FAN IN VIEW OF GANTMAN teaches the computing system of wherein the one or more processors are configured to determine the one or more application policies by at least being configured to apply a set of natural language processing classifiers to the application policy information for the application to generate the one or more application policies (Claim 1. Para [n0051]: automatically analyze the privacy policy of application software.);
wherein the one or more processors are further configured to:
index a series of application policies Para [n0051]: automatically analyze the privacy policy of application software.);
receive a query for a particular type of data (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step 101: for the to-be-detected software S and the data set D.);
responsive to receiving the query, output a set of query results that includes application policy information of the particular type of data (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step 101: for the to-be-detected software S and the data set D. converting the privacy policy P in the data set D into feature vector st);
generate, based on the set of query results, an initial training data set (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step 101: for the to-be-detected software S and the data set D. converting the privacy policy P in the data set D into feature vector st.);
train, using the first set of natural language processing classifiers, a second set of natural language processing classifiers as multi-label classification models (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete), wherein applying the set of natural language processing classifiers to the application policy information includes applying the multi-label classification models to the application policy information (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete.), and wherein the one or more application policies include a respective policy label for one or more segments of the application policy information (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete.).
FAN IN VIEW OF GANTMAN does not disclose training a first set of natural language processing classifiers as binary classification models.
Cho teaches training a first set of natural language processing classifiers as binary classification models (Col 8 lines 15-32: Each model 330-1 and 330-2 undergoes a learning or training phase 602 evolve to a production phase 604 for binary and multiclass classification.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Cho to include training a first set of natural language processing classifiers as binary classification models in order to resulting the classification probability to have a higher accuracy (Cho Col 2).
Regarding claim 20, FAN IN VIEW OF GANTMAN teaches the non-transitory computer-readable storage medium of wherein, the one or more processors are configured to determine the one or more application policies by at least being configured to apply a set of natural language processing classifiers to the application policy information for the application to generate the one or more application policies (Claim 1. Para [n0051]: automatically analyze the privacy policy of application software.);
wherein the one or more processors are further configured to:
index a series of application policies Para [n0051]: automatically analyze the privacy policy of application software.);
receive a query for a particular type of data (Claim 1: Step 101: for the to-be-detected software S and the data set D.);
responsive to receiving the query, output a set of query results that includes application policy information of the particular type of data (Claim 1: Step 101: for the to-be-detected software S and the data set D.);
generate, based on the set of query results, an initial training data set (Claim 1: Step 101: for the to-be-detected software S and the data set D.);
train, using the first set of natural language processing classifiers, a second set of natural language processing classifiers as multi-label classification models models (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete), wherein applying the set of natural language processing classifiers to the application policy information includes applying the multi-label classification models to the application policy information (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete), and wherein the one or more application policies include a respective policy label for one or more segments of the application policy information (Claim 1: Step S102: according to the step S101, converting the privacy policy P in the data set D into feature vector st, classifying each sentence according to different types specified by the privacy policy specification, then using machine learning method, constructing different classifiers; ; Step S103: according to the classifier obtained in step S102, classifying the privacy policy P of the to-be-detected software S; if the type owned by the P does not contain the specified all types, then judging that the privacy policy P is not complete).
FAN IN VIEW OF GANTMAN does not disclose training a first set of natural language processing classifiers as binary classification models.
Cho teaches training a first set of natural language processing classifiers as binary classification models (Col 8 lines 15-32: Each model 330-1 and 330-2 undergoes a learning or training phase 602 evolve to a production phase 604 for binary and multiclass classification.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Cho to include training a first set of natural language processing classifiers as binary classification models in order to resulting the classification probability to have a higher accuracy (Cho Col 2).
Regarding claim 21, FAN IN VIEW OF GANTMAN teaches the non-transitory computer-readable storage medium of claim 19, wherein the instructions cause the one or more processors to, prior to determining the one or more application policies:
monitoring, by the computing system, execution of the application to determine an initial set of application behaviors (Claim 1: Step S105: reversely compiling the software S to be detected; writing and obtaining the activity of the user interface component; re-packing the application program and running on the device; obtaining the user interface component of the software S to be detected; understanding the data type collected by the component.); and
determining, based on the initial set of application behaviors, proposed application policy information (Claim 1: Step S106: using data stream analysis method, identifying and obtaining the control of the specific data and the transmission object of the inspection data, associating the component with the data stream, constructing data related control set ACP.).
Claim(s) 5, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of Bhattathiri (US 20200092332 A1).
Regarding claim 5, FAN IN VIEW OF GANTMAN teaches the method of claim 1.
FAN IN VIEW OF GANTMAN does not explicitly disclose wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information.
Bhattathiri teaches wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information (Para [0076]: the management service 120 can also modify an application-specific policy 131 to be user specific based on the user account.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Bhattathiri to include wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information in order to the policies can be modified so that administrative users have a certain feature enabled (Cho Col 2).
Regarding claim 16, FAN IN VIEW OF GANTMAN teaches the method of claim 12.
FAN IN VIEW OF GANTMAN does not explicitly disclose wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information.
Bhattathiri teaches wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information (Para [0076]: The management service 120 can also modify an application-specific policy 131 to be user specific based on the user account.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Bhattathiri to include wherein the application policy information includes a set of user specified application policy information or a set of third party specified application policy information in order to the policies can be modified so that administrative users have a certain feature enabled (Cho Col 2).
Claim(s) 7, 18, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of Kapoor (US 20140189783 A1).
Regarding claim 7, FAN IN VIEW OF GANTMAN teaches the method of claim 1, wherein determining the one or more application policies, and comparing the set of application behaviors to the one or more application policies are performed (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106, if the action in ACP is not included in the DCP, judging that the APP data related behaviour is inconsistent with the privacy policy description.).
FAN in view of GANTMAN does not disclose (perform an action) by an application store provider in response to determining that an update to the application was submitted to the application store provider.
Kapoor teaches (perform an action) by an application store provider in response to determining that an update to the application was submitted to the application store provider (Para [0095]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Kapoor to include (perform an action) by an application store provider in response to determining that an update to the application was submitted to the application store provider in order to updates the locally stored application policy descriptor to ensures that all local instances adhere current set of application standards.
Regarding claim 18, FAN IN VIEW OF GANTMAN teaches the computing system of claim 12, wherein the one or more processors are one or more processors for an application store provider, and wherein the one or more processors are further configured to determine the one or more application policies; and comparing the set of application behaviors to the one or more applications policies are performed (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106, if the action in ACP is not included in the DCP, judging that the APP data related behaviour is inconsistent with the privacy policy description.).
FAN in view of GANTMAN does not disclose provider in response to determining that an update to the application was submitted to the application store provider.
Kapoor teaches provider in response to determining that an update to the application was submitted to the application store provider (Para [0095]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Kapoor to include provider in response to determining that an update to the application was submitted to the application store provider in order to updates the locally stored application policy descriptor to ensures that all local instances adhere current set of application standards.
Regarding claim 22, FAN IN VIEW OF GANTMAN teaches the non-transitory computer-readable storage medium of claim 19, wherein the one or more processors are one or more processors for an application store provider, and wherein the instructions cause the one or more processors to determine the one or more application policies, and compare the set of application behaviors to the one or more application policies (Para [n0051]: automatically analyze the privacy policy of application software. Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106, if the action in ACP is not included in the DCP, judging that the APP data related behaviour is inconsistent with the privacy policy description.).
FAN in view of GANTMAN does not disclose in response to determining that an update to the application was submitted to the application store provider.
Kapoor teaches in response to determining that an update to the application was submitted to the application store provider (Para [0095]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Kapoor to include in response to determining that an update to the application was submitted to the application store provider in order to updates the locally stored application policy descriptor to ensures that all local instances adhere current set of application standards.
Claim(s) 8, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of MANJUNATH (US 20130091536 A1).
Regarding claim 8, FAN IN VIEW OF GANTMAN teaches the method of claim 1.
FAN IN VIEW OF GANTMAN does not explicitly disclose wherein the application is a web application.
MANJUNATH does disclose wherein the application is a web application (Para [0017]: when a web application is executed on a user's device.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN of perform mentoring application behavior with the teachings of MANJUNATH to include the well-known technique of wherein the application is a web application because the results would have been predictable and resulted in perform mentoring application behavior for web application.
Regarding claim 9, FAN IN VIEW OF GANTMAN teaches the method of claim 1, wherein outputting the indication of whether the one or more application behaviors from the set of application behaviors are consistent with the one or more application policies (Claim 1: Step S107: comparing the DCP obtained in step S104 with ACP obtained in step S106, if the action in ACP is not included in the DCP, judging that the APP data related behaviour is inconsistent with the privacy policy description.).
FAN IN VIEW OF GANTMAN does not explicitly disclose includes , wherein the computing system is an end user computing system, wherein the application is installed and executed as the end user computing system, outputting, for display by a display device of the computing system, a web page including information about one or more application behaviors that are inconsistent with at least one of the one or more application policies.
MANJUNATH does disclose includes , wherein the computing system is an end user computing system, wherein the application is installed and executed as the end user computing system, outputting, for display by a display device of the computing system, a web page including information about one or more application behaviors that are inconsistent with at least one of the one or more application policies (Para [0017]. Para [0027]: when a web application is executed on a user's device. The script file creation or execution process may, in some embodiments, be terminated if instructions 50 and parameters 52 violate one or more policy criteria 80. An error message may, for example, be displayed to the user to notify the user that web application 10 may not be created or executed.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of MANJUNATH to include includes wherein the computing system is an end user computing system, wherein the application is installed and executed as the end user computing system, outputting, for display by a display device of the computing system, a web page including information about one or more application behaviors that are inconsistent with at least one of the one or more application policies in order to notify the user that web application may not be executed (MANJUNATH [0027]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of MANJUNATH (US 20130091536 A1) in view of Zhang (US 20170366562 A1).
Regarding claim 10, FAN IN VIEW OF GANTMAN in view of MANJUNATH teaches the method of claim 9.
FAN IN VIEW OF GANTMAN in view of MANJUNATH does not explicitly disclose further comprising:
receiving, by the computing system, a request to uninstall the application; and
uninstalling, by the computing system, the application in response to receiving the request.
Zhang does explicitly disclose further comprising:
receiving, by the computing system, a request to uninstall the application (Para [0039]); and
uninstalling, by the computing system, the application in response to receiving the request (Para [0039]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN in view of MANJUNATH with the teachings of Zhang to include further comprising: receiving, by the computing system, a request to uninstall the application; and uninstalling, by the computing system, the application in response to receiving the request in order to uninstall the application to enhance security.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over FAN (CN 112068844 B) in view of GANTMAN (US 20160285897 A1) in view of Sawhney (US 20180121659 A1).
Regarding claim 11, FAN IN VIEW OF GANTMAN teaches the method of any of claim 1.
FAN IN VIEW OF GANTMAN does not explicitly disclose wherein outputting the indication of whether the one or more application behaviors from the set of application behaviors are consistent with the one or more application policies includes sending a report to a developer associated with the application.
Sawhney teaches wherein outputting the indication of whether the one or more application behaviors from the set of application behaviors are consistent with the one or more application policies includes sending a report to a developer associated with the application (Para [0031]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of FAN IN VIEW OF GANTMAN with the teachings of Sawhney to include wherein outputting the indication of whether the one or more application behaviors from the set of application behaviors are consistent with the one or more application policies includes sending a report to a developer associated with the application in order to notify the developer about any application behaviour (Sawhney [0031]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JUDY BAZNA/ Examiner, Art Unit 2495
/FARID HOMAYOUNMEHR/ Supervisory Patent Examiner, Art Unit 2495
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