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 .
Election/Restrictions
NO restrictions warranted at applicant’s time of filing for CONtinuation.
Priority
Applicant claims domestic priority under 35 USC 120 to non – provisional application # 18/478302 [i.e. parent application], filed on 09/29/2023, now US PAT # 12413606.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/16/2025, 11/21/2025, the submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
Applicant’s drawings filed on 07/16/2025 have been inspected and are in compliance with MPEP 608.02.
Specification
Applicant’s specification filed on 07/16/2025 has been inspected and is in compliance with MPEP 608.01.
Claim Objections
NO claim objections warranted at applicant’s time of filing for CONtinuation.
Claim Interpretation – 35 USC 112th f
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Such claim limitation(s) is/are:
As per claim 1. A method for detecting attacks against a software service authentication system configured to authorize access to software services, the method comprising:
using at least one processor to perform:
accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system; and
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system.”
As per clam 19. A system for detecting cyberattacks against a software service authentication system configured to authorize access to software services, the system comprising:
at least one processor, and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor “to:
access a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitor computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system; and
determine, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system.”
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may:
(1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or
(2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Appropriate action required.
Claim Rejections - 35 USC § 112
NO claim rejections warranted at applicant’s time of filing for CONtinuation.
Claim Rejections - 35 USC § 101
NO claim rejections warranted at applicant’s time of filing for CONtinuation.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim[s] 1 – 20 are rejected on the ground of non-statutory double patenting as being unpatentable over claim[s] 2 - 20 of U.S. Patent No. 12413606.
Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter of the pending application and the patent are the same or similar in scope and are not distinct in the following manner:
Monitoring cyberattacks against a software service authentication system that validates access to software services. A user activity profile is obtained and specifies values of parameters indicating a user’s pattern of requesting access to unique software services. The activity of the user is monitored over a time period to obtain software request data indicating requests made by the user to access software services during the time period. Determining, using the software service request data and the user activity profile, whether computing activity of the user during the time period is anomalous.
Also, see the table below for claim-by-claim comparison.
Pending US Application # 19/271704
US PAT # 12413606
1. A method for detecting attacks against a software service authentication system configured to authorize access to software services, the method comprising:
using at least one processor to perform:
accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system; and
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system.
18.(Currently amended) A method for detecting attacks against a software service authentication system configured to authorize access to software services
using Kerberos authentication, the method comprising further comprises:
using at least one processor to perform:
accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system;
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system; and
detecting a Kerberoasting attack when it is determined that the computing activity of the first user during the first time period is anomalous.
2. The method of claim 1, wherein the values of parameters indicating the first user's pattern of requesting access to one or more unique software services indicate a threshold number of unique software service requests.
2. (Currently amended) The method of claim 18, wherein the values of parameters indicating the first user's pattern of requesting access to one or more unique software services indicate a threshold number of unique software service requests.
3. The method of claim 2, wherein determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system comprises:
determining a number of unique software service requests of the one or more requests by the first user during the first time period; and
determining whether the number of unique software service requests exceeds the threshold number of unique software service requests indicated by the first user activity profile.
3. (Original) The method of claim 2, wherein determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system comprises:
determining a number of unique software service requests of the one or more requests by the first user during the first time period; and
determining whether the number of unique software service requests exceeds the threshold number of unique software service requests indicated by the first user activity profile.
5. The method of claim 4, wherein determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system comprises:
determining a number of unique software service requests after a first authentication of the first user in the first time period;
determining an inverse of the number of unique software service requests after the first authentication of the first user in the first time period; and
determining whether the inverse of the number of unique software service requests is less than the threshold ratio of authentications to unique software service requests.
5. (Original) The method of claim 4, wherein determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system comprises:
determining a number of unique software service requests after a first authentication of the first user in the first time period;
determining an inverse of the number of unique software service requests after the first authentication of the first user in the first time period; and
determining whether the inverse of the number of unique software service requests is less than the threshold ratio of authentications to unique software service requests.
6. The method of claim 4, further comprising determining the threshold ratio of authentications to unique software service requests by:
determining, for each of a plurality of authentications of the user in a time period preceding the first time period, an inverse of a number of unique software service requests after the authentication to obtain a plurality of ratios of authentications to unique software service requests; and
determining the threshold ratio of authentications to unique software service requests using the plurality of ratios of authentications to unique software service requests.
6. (Original) The method of claim 4, further comprising determining the threshold ratio of authentications to unique software service requests by:
determining, for each of a plurality of authentications of the user in a time period preceding the first time period, an inverse of a number of unique software service requests after the authentication to obtain a plurality of ratios of authentications to unique software service requests; and
determining the threshold ratio of authentications to unique software service requests using the plurality of ratios of authentications to unique software service requests.
7. The method of claim 1, wherein accessing the first user activity profile comprises:
accessing user software service request data indicating requests by the first user to access software services during a time period preceding the first time period; and
generating the first user activity profile using the user service request data at least in part by determining the values of the parameters.
7. (Currently amended) The method of claim 18, wherein accessing the first user activity profile comprises:
accessing user software service request data indicating requests by the first user to access software services during a time period preceding the first time period; and
generating the first user activity profile using the user service request data at least in part by determining the values of the parameters.
8. The method of claim 6, wherein the software service request data comprises:
an indication of a plurality of authentications of the first user in the time period preceding the first time period, the plurality of authentications associated with respective ones of a plurality of sessions; and
an indication of software service requests in the plurality of sessions.
8. (Original) The method of claim 6, wherein the software service request data comprises:
an indication of a plurality of authentications of the first user in the time period preceding the first time period, the plurality of authentications associated with respective ones of a plurality of sessions; and
an indication of software service requests in the plurality of sessions.
9. The method of claim 6, wherein the time period preceding the first time period ends at least a threshold amount of time prior to a start of the first time period.
9. (Original) The method of claim 6, wherein the time period preceding the first time period ends at least a threshold amount of time prior to a start of the first time period.
10. The method of claim 9, wherein the threshold amount of time is 12 hours.
10. (Original) The method of claim 9, wherein the threshold amount of time is 12 hours.
11. The method of claim 1, wherein the values of the parameters indicating the first user's pattern of requesting access to one or more unique software services through the software service authentication system indicate one or more software services that the first user previously requested to access in a time period preceding the first time period.
11. (Currently amended) The method of claim 18, wherein the values of the parameters indicating the first user's pattern of requesting access to one or more unique software services through the software service authentication system indicate one or more software services that the first user previously requested to access in a time period preceding the first time period.
13. The method of claim 1, further comprising:
transmitting, to at least one device, an indication of a detected attack by the first user when it is determined that the computing activity of the first user during the first time period is anomalous.
13. (Currently amended) The method of claim 18, further comprising:
transmitting, to at least one device, an indication of a detected attack by the first user when it is determined that the computing activity of the first user during the first time period is anomalous.
14. The method of claim 13, further comprising:
preventing the first user from being authorized to access one or more software services through the software service authentication system when it is determined that the computing activity of the first user during the first time period is anomalous.
14. (Original) The method of claim 13, further comprising:
preventing the first user from being authorized to access one or more software services through the software service authentication system when it is determined that the computing activity of the first user during the first time period is anomalous.
15. The method of claim 1, wherein monitoring computing activity of the first user during the first time period to obtain the software service request data indicating the one or more requests by the first user during the first time period to access the one or more software services through the software service authentication system comprises:
storing an indication of one or more requests for one or more software service tickets to access the one or more software services.
15. (Currently amended) The method of claim 18, wherein monitoring computing activity of the first user during the first time period to obtain the software service request data indicating the one or more requests by the first user during the first time period to access the one or more software services through the software service authentication system comprises:
storing an indication of one or more requests for one or more software service tickets to access the one or more software services.
18. The method of claim 1, wherein the software service authentication system is configured to authorize access to the software services using Kerberos authentication, and the method further comprises:
detecting a Kerberoasting attack when it is determined that the computing activity of the first user during the first time period is anomalous.
18. (Currently amended) A method for detecting attacks against a software service authentication system configured to authorize access to software services of claim 1, wherein the software service authentication system is configured to authorize access to the software services using Kerberos authentication, the method comprising further comprises:
using at least one processor to perform: accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system;
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system; and
detecting a Kerberoasting attack when it is determined that the computing activity of the first user during the first time period is anomalous.
19. A system for detecting cyberattacks against a software service authentication system configured to authorize access to software services, the system comprising:
at least one processor, and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to:
access a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitor computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system; and
determine, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system.
19. (Currently amended) A system for detecting cyberattacks against a software service authentication system configured to authorize access to software services using Kerberos authentication, the system comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to:
access a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitor computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system;
determine, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system; and
detect a Kerberoasting attack when it is determined that the computing activity of the first user during the first time period is anomalous.
20. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method for detecting attacks against a software service authentication system configured to authorize access to software services, the method comprising:
accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system; and
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system.
20. (Currently amended) A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method for detecting attacks against a software service authentication system configured to authorize access to software services using Kerberos authentication, the method comprising:
accessing a first user activity profile specifying values of parameters indicating a first user's pattern of requesting access to one or more unique software services through the software service authentication system;
monitoring computing activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system;
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous, the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user's pattern of requesting access to one or more unique software services through the software service authentication system; and
detecting a Kerberoasting attack when it is determined that the computing activity of the first user during the first time period is anomalous.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 7, 8, 11, 13, 14, 19, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being taught by Kirti et al. [US PAT # 10701094].
As per claim 1. Kirti does teach a method for detecting attacks against a software service authentication system configured to authorize access to software services [Col. 1, lines 15 – 23, Cloud service providers provide various services in the “cloud;” that is, over a network, such as the public Internet, and remotely accessible to any network-connected client device. Examples of the service models used by cloud service providers (also referred to herein as “cloud providers” or “providers”) include infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and network as a service (NaaS).
Then further of col. 2, lines 14 – 27, In various implementations, provided are systems and methods for a cloud security system that can identify users that have privileged capabilities with respect to an application or service provided by a cloud services provider………it may be desirable to monitor privileged users with a higher degree of scrutiny, and to determine quickly whether a privileged user account has become compromised.], the method comprising:
using at least one processor [col. 4, lines 46 – 57, one or more processing units] to perform:
accessing a first user activity profile specifying values of parameters indicating a first user’s pattern of requesting access to one or more unique software services through the software service authentication system [col. 2, lines 34 – 50, ….a security management system [i.e. applicant’s service authentication system] to identity the privileged users of a cloud service. In various implementations, the security management system can include techniques for identifying privileged users of a cloud service, where the techniques include performing various steps. The steps can include obtaining activity data [i.e. applicant’s…first user activity profile specifying values of parameters…….first user’s pattern] from a service provider system. The activity data can describe actions performed during use of a cloud service. The actions can be performed by one or more users associated with a tenant, where the service provider system provides the tenant with a tenant account. The tenant account enables the one or more users to access the cloud service. The steps can further include identifying, in the activity data, one or more actions that are privileged with respect to the cloud service. The steps can further include identifying, using the activity data, a set of users who performed the one or more actions. The set of users can be determined from the one or more users associated with the tenant……..The one or more instructions can cause the security control to be changed with respect to the user, where access to the cloud service by the user is modified due to the change to the security control.];
monitoring computer activity of the first user during a first time period to obtain software service request data indicating one or more requests by the first user during the first time period to access one or more software services through the software service authentication system [Figure # 1, and col. 6, lines 21 – 25, For example, the security management and control system can use supervised learning techniques and unsupervised learning techniques to develop a model that describes the manner in which the organization's users use the cloud service. In this and other examples, the security management and control system can use the actions in the activity log to identify privileged users. Once the privileged users are identified, the security management and control system can monitor the privileged users with a higher degree of scrutiny.]; and
determining, using the software service request data and the first user activity profile, whether the computing activity of the first user during the first time period is anomalous [Figure # 1, and col. 10, lines 34 – 44, In some implementations, the security monitoring and control system 102 can further suggestion remediation actions, and/or can automatically perform remediation actions to isolate or stop the threat. In some examples, analysis performed by the security monitoring and control system 102 can include determining models of normal and/or abnormal behavior in user activity, and using the models to detect patterns of suspicious activity. In some examples, the security monitoring and control system 102 can simultaneously analyze data from different services and/or from different services providers], the determining comprising:
determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system [Figure # 1, and col. 18, lines 18 – 31, In various implementations, the security monitoring and control system 102 [ie..applicant’s software service authentication system] can include a learning system 178. The learning system 178 can apply various machine learning algorithms to data collected by the security monitoring and control system 102 [i.e. applicant’s….first user’s pattern of requesting access…software services]. The information learned about the data can then be used, for example, by the data analysis system 136 to make determinations about user activities in using services provided by the service provider 110. For example, the learning system 178 can learn patterns of normal or common behaviors of users of an organization. In these and other examples, the learning system 178 can generate models that capture patterns that the learning system 178 has learned, which can be stored in the storage 122 along with other data for an organization.]; and
determining that the computing activity of the first user during the first time period is anomalous when it is determined that the one or more requests by the first user during the first time period do not match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system [Figure # 3, and col. 27 – 40 – 60, FIG. 3 illustrates a block diagram of an example analytics engine 300 of a security management and control system. In various examples, the analytics engine 300 can analyze various data sources to identify network threats for an organization whose users are using cloud services. In various examples, the operations of the analytics engine 300 can be used to detect and/or address various threat scenarios.
One example of a threat scenario is IP hopping. In an IP hopping scenario, an attacker may use one or more proxy servers to hide the attacker's true location or machine identity before mounting an attack. Detection of this type of scenario can involve geographic resolution (e.g., identifying or looking up a geographic location associated with an IP address) of each IP connection used to connect to a cloud application. Detection can further include detecting anomalous characteristics in the spatial data, and predicting a threat from this information. Metrics used for detection can include, for example, a count of the number of unique IP addresses used by a user per day and/or a velocity, which can refer to the time difference between the use of different IP addresses and the/or duration that each IP address used.
Another example of a threat scenario is an unusual geolocation scenario. An unusual geolocation scenario may refer to activities being originated in locations that are unexpected or outside of an established pattern [i.e. applicant’s…the first user during the first time period]. This scenario may include activities such as, but not limited to, successful logins or file upload/download from unusual geolocations].
As per claim 2. Kirti does teach the method of claim 1, wherein the values of parameters indicating the first user’s pattern of requesting access to one or more unique software services indicate a threshold number of unique software service requests [Kirti, col. 24, lines 30 – 55, In various examples, activity data can include various types of information about the user of the service provider's services. For example, activity data associated with user accounts can include information relating to the use of, and/or actions taken with, a user account for a service. In this example, the activity data can include sources of information such as user logs and/or audit trails. More specific types of activity data can include, for example, login and logout statistics (including attempts and successes), file operations, access metrics, network download/upload metrics, application metrics (e.g., use, operations, functions, etc.), IP addresses used to access a service, devices used to access service, and/or cloud resources that were accessible (such as, for example, files and folders in a file management cloud application [such as Box], employees and contractors in a human resource cloud application [such as Workday], and/or contacts and accounts in a customer relationship management cloud application [such as Salesforce]). In various examples, activity data can include the user account or other user identifier for the user associated with the events or statistics. In various examples activity data can include information about system status or activity of a cloud system such as, for example, server activity, server reboots, security keys used by a server, and system credentials, where this information is visible or accessible to a system using authorized credentials.].
As per claim 3. Kirti does teach the method of claim 2, wherein determining, using the values of the parameters specified by the first user activity profile, whether the one or more requests by the first user during the first time period match the first user’s pattern of requesting access to one or more unique software services through the software service authentication system comprises:
determining a number of unique software service requests of the one or more requests by the first user during the first time period [Kirti, col. 24, lines 30 – 55, In various examples, activity data can include various types of information about the user of the service provider's services. For example, activity data associated with user accounts can include information relating to the use of, and/or actions taken with, a user account for a service. In this example, the activity data can include sources of information such as user logs and/or audit trails. More specific types of activity data can include, for example, login and logout statistics (including attempts and successes), file operations, access metrics, network download/upload metrics, application metrics (e.g., use, operations, functions, etc.), IP addresses used to access a service, devices used to access service, and/or cloud resources that were accessible (such as, for example, files and folders in a file management cloud application [such as Box], employees and contractors in a human resource cloud application [such as Workday], and/or contacts and accounts in a customer relationship management cloud application [such as Salesforce]). In various examples, activity data can include the user account or other user identifier for the user associated with the events or statistics. ]; and
determining whether the number of unique software service requests exceeds the threshold number of unique software service requests indicated by the first user activity profile [Kirti, Figure # 6, and col. 47, lines 19 – 27, At step 612, the process 600 includes determining that a risk score for user in the set of users is greater than a threshold. In various examples, the threshold can indicate activity that, when the threshold is exceeded, constitutes a security risk for the tenant. In various examples, the threshold can be associated with a particular tenant, a particular user, a group of users, a particular service or service provider, a time of day or day of the week, another factor, or a combination of factors.].
As per claim 7. Kirti does teach the method of claim 1, wherein accessing the first user activity profile comprises:
accessing user software service request data indicating requests by the first user to access software services during a time period preceding the first time period; and generating the first user activity profile using the user service request data at least in part by determining the values of the parameters [Kirti, col. 26, lines 23 – 27, In various implementations, the user identity repository 209 can also be used to facilitate tracking of user activity and generation of profiles, where a profile can describe a particular user's use of a cloud service or of multiple cloud services. In some examples, the cloud security system 200 can use the profile of a user to take actions that affect multiple cloud services.].
As per claim 8. Kirti does teach the method of claim 6, wherein the software service request data comprises:
an indication of a plurality of authentications of the first user in the time period preceding the first time period, the plurality of authentications associated with respective ones of a plurality of sessions [Kirti, Table # 5, and col. 32, lines 50 – 57, Table 5 below lists example values for several possible daily aggregation matrix vectors. The example vectors illustrated here include a count of logins per day for one day (“logcntday_1dy”), a count of failed logins per day for one day (“logfailcntday_1dy”), a count per day of IP addresses from which failed logins occurred over one day (“logfailipdisday_1dy”), and a count per day of IP addresses used to log in over one day (“logipdisday_1dy”).]; and
an indication of software service requests in the plurality of sessions [Kirti, col. 34, lines 50 – 67, Algorithm 3 provides an example of an algorithm that can be used for analytics of multiple application behavior. In algorithm 3, user IP addresses associated with various cloud service activities (such as logging in) are resolved to geolocation coordinates IP1 (Latitude 1, Longitude 1), IP2 (Latitude 2, Longitude 2), IP3 (Latitude 3, Longitude 3), etc. If a user has different usernames with different cloud services, the various usernames associated with that user can be mapped to a unique user specific identity that identifies the user across the services.].
As per claim 11. Kirti does teach the method of claim 1, wherein the values of the parameters indicating the first user's pattern of requesting access to one or more unique software services through the software service authentication system indicate one or more software services that the first user previously requested to access in a time period preceding the first time period [Kirti, col. 2, lines 34 – 50, ….a security management system [i.e. applicant’s service authentication system] to identity the privileged users of a cloud service. In various implementations, the security management system can include techniques for identifying privileged users of a cloud service, where the techniques include performing various steps. The steps can include obtaining activity data [i.e. applicant’s…first user activity profile specifying values of parameters…….first user’s pattern] from a service provider system. The activity data can describe actions performed during use of a cloud service.].
As per claim 13. Kirti does teach the method of claim 1, further comprising: transmitting, to at least one device, an indication of a detected attack by the first user when it is determined that the computing activity of the first user during the first time period is anomalous [Kirti, col. 34, lines 40 – 49, In various examples, anomalous activity that is detected for a user of one cloud service can be used by the threat detection engine 302 to calculate or re-calculate the likelihood of a threat in the use of another cloud service. In this way, new events occurring during the use of one cloud service can be screened proactively to detect and/or predict threats in the use of another cloud service. In various examples, multiple data points across different cloud services can be correlated to increase the accuracy of a threat score.].
As per claim 14. Kirti does teach the method of claim 13, further comprising: preventing the first user from being authorized to access one or more software services through the software service authentication system when it is determined that the computing activity of the first user during the first time period is anomalous [Kirti, col. 34, lines 40 – 49, In various examples, anomalous activity that is detected for a user of one cloud service can be used by the threat detection engine 302 to calculate or re-calculate the likelihood of a threat in the use of another cloud service. In this way, new events occurring during the use of one cloud service can be screened proactively to detect and/or predict threats in the use of another cloud service.].
As per system claim 19 that includes the same or similar claim limitations as method claim # 1, and is similarly rejected.
***The examiner notes that applicant’s recited: “at least one processor,” “at least one non-transitory storage medium storing instructions,” is taught by the prior art of Kirti et al. at col. 4, lines 25 – 57.
As per non – transitory computer – readable storage medium claim 20, that includes the same or similar claim limitations as method claim # 1, and is similarly rejected.
***The examiner notes that applicant’s recited: “at least one processor,” “at least one non-transitory storage medium storing instructions,” is taught by the prior art of Kirti et al. at col. 4, lines 25 – 57.
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.
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 non-obviousness.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirti et al. [US PAT # 10701094] in view of Hardinger et al. [US PAT # 8225281]
As per claim 15. Kirti does teach what is taught in the rejection of claim # 1 above.
Kirti does clearly teach the method of claim 1, wherein monitoring computing activity of the first user during the first time period to obtain the software service request data indicating the one or more requests by the first user during the first time period to access the one or more software services through the software service authentication system comprises:
storing an indication of one or more requests for one or more software service tickets to access the one or more software services.
However, Hardinger does teach the method of claim 1, wherein monitoring computing activity of the first user during the first time period to obtain the software service request data indicating the one or more requests by the first user during the first time period to access the one or more software services through the software service authentication system comprises:
storing an indication of one or more requests for one or more software service tickets to access the one or more software services [Figure # 1, and col. 8, lines 54 – 67 and col. 9, lines 1 – 2, The source code repository 140 is a secure server or servers that store the source software components 142, 144, 146 used by the baseline build component 122 to fulfill application build requests received by the baseline build component 122 from service requester devices 180. The source code repository 140 also stores tasks, tickets, and documentation that remain a continuing part of source software components 142. The source code repository 140 may alternately store pointers to those components wherein those components reside on a server or in a database. When a source software component 142 is drawn from the source code repository 140 by the baseline build component 122 to be included in the process of building a baseline application product 162, the tasks, tickets, and documentation or pointers thereto associated with the source software component 142 are included in the build process along with the source software component 142.].
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Kirti and Hardinger in order for the monitoring of the user activities of the cloud services by the threat detection engine of a security management system of Kirti to include monitoring for specific types of activities of Hardinger. This would allow for the detection of specific types of anomalies that may be present when the user interacting with the cloud services. See col. 1, lines 28 – 33 of Hardinger.
Allowable Subject Matter
Claim[s] 4 – 6, 9, 10, 12, 16 – 18 contain allowable subject matter, but as allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
***The examiner notes that a reason for allowance can be written in the next subsequent office action, once all identified formal requirements above have been overcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kulkarni, who does teach parameters for a set of permissions are determined based at least in part on previous requests to access a set of resources by a principal or user. The set of permissions are updated based at least in part on the set of parameters such that the set of parameters cause different requests to have different authentication requirements. The updated set of permissions is enforced to control access to computing resources such as the set of resources.
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/DANT B SHAIFER HARRIMAN/ Primary Examiner, Art Unit 2434
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