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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/01/2025 was filed is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed on 10/31/2025 has been accepted and considered in this office action. Claims 1-4, 9-10 and 12-19 have been amended. Claim 20 has been cancelled. Claim 21 has been newly added.
Response to Arguments
Applicant's arguments filed 10/31/2025 have been fully considered but they are not persuasive.
Applicant argues that Ahn’s system 730 A is not a “network core”. Applicant says, “The actual network core of Ahn appears to be the security information and event management (SIEM) system 702”. Therefore, Ahn fails to teach a network core that performs the claimed obtaining, storing, and improving steps of the independent claim. However, Applicant’s assertion is not persuasive.
Applicant’s specification (para 50) defines the network core functionally as performing operations such as obtaining curated telemetry data, storing activity logs, improving a security profile of the logs, and performing validation using a security questionnaire. Under applicant’s own definition, a network core is not limited to a specific named component such as a SIEM, but rather encompasses centralized system that perform telemetry collection, logging, retention management, and security validation across distributed devices. Applicant’s specification (para 37) states “Network core 102 may provide all, or a portion of, the computer-implemented services.”
Ahn’s system 730 A centrally manages telemetry collection, logging, retention, and rule enforcement across distributed network devices. Anh, para 119 discloses that FIGS. 8A and 8B show an example of how the system 730A may act to collect, filter, and forward (or discard without forwarding) packets captured by the PSGs 110. This demonstrates that the system 730A centrally controls how telemetry data is handled across multiple PSG devices.
Ahn further discloses that the system 730 A stores and manages instructions governing telemetry and retention. Anh, para 123 discloses that in event 813, the system 730A may update a list of instructions to store the instruction received in event 812.
Ahn discloses that the system 730 A enforces retention and deletion policies without human intervention. The automated deletion based on stored instruction is a form of policy-driven management. (para 123)
Anh further discloses (para 130) that the system 730 A updates the retrieved logs on the received packets to create an updated log following C, D based criteria. This constitutes attempting to improve a security profile of the activity log to obtain an updated activity log.
Applicant does not proffer any definition for the phrase “network core”. The examiner finds that the broadest reasonable interpretation of the term includes any device that’s not directly interfacing to a (non-administrator) end user. Thus, Ahn’s 730A is within the scope of a “network core”.
Hence Applicant’s argument is not persuasive.
Applicant asserts that Verma is silent with regards to a data processing system that is able to automatically complete a security questionnaire without a user. Applicant’s argument is not persuasive.
Verma discloses security questionnaires for authentication purposes, and explicitly differentiates between “device questions” (i.e., questions answered by the literal device and not the user), and “user questions” (i.e., questions that are answered via required user input). Verma explicitly contains an example without any “user questions”. Verma (Col 21, lines 9-30) discloses an authentication flow for a laptop, if the authentication level is regular, the system identifies device questions to be asked regardless of the UI type by using at least one question keyword extracted from device data present in at least one of a device log, historic data, or a device record for a smart phone. The system presents the device question to the smart phone (device) through an API and receives answers for the device question. This constitutes the device receiving the questions based on device log history and receiving answer via the device without human intervention. Further, claim 14 and 20, Verma further discloses that the device questions are generated based on device’s log history and response is received from the device without human intervention for authentication.
Hence, the applicant’s argument is ultimately not persuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The limitation of claim 1 recites “... manages … without human intervention..” and “and performing validation of the data processing system …without the human intervention..” The specification fails to provide support for the concept of “without human intervention”, as claimed. In particular, the limitation is unsupported for two main reasons, referred below as (A) and (B). The first (A) is that the specification discloses a concept of “without user intervention” in limited contexts, and not “without human intervention.” And, while a human may be a user, the user may not be a human. See, for example, general computing and operating system terminology, where the “user” is often a process or software-based endpoint or representation. Second (B), applicant’s specification fails to disclose “without human intervention” or “without user intervention” with respect to the functionality claimed. For the first function (“manages”), nad as an initial matter, it’s unclear whether the language “manages, without human intervention” has a broadest reasonable interpretation that either i) requires at least one function, which can be construed as “manages” or a “managing function”, be performed without human intervention or ii) requires all functions that would be construed as “manages” or a “managing function” be performed without human intervention. Interpretation i) makes the most sense as the BRI, however, every computer in existence since the 1940s does at least one managing function without human intervention, such as execute a discrete instruction. In this scenario, applicant’s amended language (“manages, without human intervention …”) doesn’t practically limit anything (other than it be implemented by a computer) and, likewise, the prior art discloses this concept of performing at least one “manages” function without human intervention, such as executing an instruction on a CPU. Interpretation ii) would result in a 112b indefiniteness rejection because the set of functions that are construed as “manages” is unknown. Further, applicant’s specification lacks any support whatsoever for requiring some unknown set of “manages” functions to be performed without human intervention (or without user intervention).
Lastly, applicant’s own specification affirmatively requires user involvement at least for initial establishment of the session and authentication. For example, the specification para 15 recites “To authenticate data processing systems throughout a distributed environment without user intervention, the system may include a network core. The network core may initially establish a root of trust with a data processing system of the distributed environment via user intervention (by a user, for example, entering a password, pin, fingerprint scan, etc.)” However, the claim phrase “without human intervention” is too narrow representation of a user in computing. Huang [US 20030110397 A1] para 11, discloses a user may mean a human user, a software agent, a group of users, a member of a group, a device and/or application. The disclosure is not commensurate with the scope of “without human intervention,” which is not described and is not supported by the specification’s “user intervention” disclosure.
The limitation in claim 12 recites “..was originally manually established by a user ..” The specification does not provide support for “.. was originally manually established by a user” The specification para 15, discloses establishing a root of trust via user intervention (e.g., password/PIN/fingerprint scan), but does not disclose or define “manual” establishment. It is unclear whether “manually” is intended to be narrower than “user intervention” (e.g., physical interaction vs software interaction). This term lacks a clear written description.
For the reasons stated above, the amended claim language lacks written description support under 35 U.S.C 112 (a) and constitutes new matter. The applicant is advised to review their originally filed disclosure and claims to ensure alignment with the proposed claim amendments.
Dependent claims 2-19 are rejected based on their claim dependencies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 10, 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1).
Regarding Claim 1, Anh teaches:
the method comprising: by a network core of the distributed environment that manages, without human intervention, data processing systems of the distributed environment that includes the data processing system: (Anh, para 116, 119 discloses the telemetry collection, log storage, analysis, updating, and retention control are performed by 730 A based on filtering rules and predefined criteria implies this whole process is automated without user intervention)
obtaining curated telemetry data from the data processing system, the curated telemetry data comprising data points that meet data security criteria indicated by certain rules; (Anh, para 124, discloses system 730A collects packet level telemetry data based on D-based criteria or rules from PSG device);
storing the curated telemetry data in a first activity log; (Anh, para 126, discloses system 730 A stores the logs and captured packets based on criteria, rules associated with capturing and storing those packets.);
attempting to improve a security profile of the first activity log based on the certain rules to obtain a new log; (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
wherein the new log comprises updated first activity log (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
Anh does not explicitly teach; However, Verma teaches:
and performing a validation of the data processing system by providing a security questionnaire to the data processing system (Verma (Col 21, lines 9-30) discloses an authentication flow for a laptop, if the authentication level is regular, the system identifies device questions to be asked regardless of the UI type by using by using at least one question keyword extracted from device data present in at least one of a device log, historic data, or a device record for a smart phone. The system presents the device question to the smart phone (device) through an API);
and causing the data processing system to automatically complete the security questionnaire without the human intervention and return a completed one of the security questionnaire to the network core (Verma (Col 21, lines 9-30) discloses the system presents the device question to the smart phone (device) through an API and receives answers for the device question. This constitutes the device receiving the questions based on device log history and receiving answer via the device without human intervention. Further, claim 14 and 20, Verma further discloses that the device questions are generated based on device’s log history and response is receive from the device without human intervention for authentication);
the security questionnaire comprising security questions based on the new log (Verma, Col 5, lines 44-67; Col 6 line 1, discloses the questions are generated based on user and device data such as device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags etc to validate or authenticate user and the device.)
Wherein the first activity log being maintained identically to a second activity log hosted by the data processing system based on certain rule; (Verma, Col 5, lines 60-65, discloses device data which includes device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags are stored in the device 104 and the storage unit 108.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Verma’s technique of maintaining identical copies of log in two separate units and performing validation of the device using the security questions based on updated logs. One would be motivated to perform such modification on Anh’s system to improve the authentication in Internet of Things (IoT) environments implementing context-aware, multi-dimensional, device and user-based question and score system for seamless and adaptive authentication. (Verma, Col 1, 2)
Verma does not explicitly teach; However, Ramaswamy teaches:
Wherein certain rules comprise data retention policy; (Ramaswamy, para 34, discloses the retention policy administrator 144 defining a particular retention rule/policy.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Ramaswamy’s technique of implementing retention rule/policy in their system. One would be motivated to perform such modification on Anh’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Regarding Claim 10, Anh, Verma and Ramaswamy teaches the method of claim 1:
Ahn and Ramaswamy does not explicitly teach; However, Verma teaches:
identifying an occurrence of an event indicating that the data processing system is to be authenticated (Verma, Col 12, lines 1-21, discloses that the authentication for device is triggered);
obtaining a security questionnaire, based on the occurrence of the event, using the first activity log and a security risk level of the data processing system (Verma, Col 5, lines 44 – 67;
Col 6, line 1; discloses the questions are generated based on user and device data such as device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags etc to validate or authenticate user and the device);
providing the security questionnaire to the data processing system (Verma, Col 9, lines 44-59, discloses the questions can be presented to the corresponding devices in the IoT environment, wherein the questions are presented through the API of the device);
obtaining a response, the response comprising answers to the security questions in the security questionnaire (Verma, Col 9, lines 60-67, discloses that at operation 308, the method includes generating a score based on the response received from the atleast one of the users and the corresponding one or more devices 104 present in the IoT environment);
making a determination regarding whether each answer of the answers matches a pre-determined answer from a set of possible answers (Verma, Fig. 7, discloses evaluating the correctness of the answers by comparing the response with distance metrics, comparing score against the threshold);
in an instance of the determination in which each answer of the answers matches the pre-determined answer: concluding that the data processing system is authentic (Verma, Fig. 18, discloses at the end, that once the device response evaluation is successful, the device is authenticated);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Verma’s technique of performing validation of the device using the security questions based on updated logs. One would be motivated to perform such modification on Anh’s system to improve the authentication in Internet of Things (IoT) environments implementing context-aware, multi-dimensional, device and user-based question and score system for seamless and adaptive authentication. (Verma, Col 1, 2)
Regarding Claim 13, Anh teaches:
the operations comprising: by the network core of the distributed environment that manages, without human intervention, data processing systems of the distributed environment that includes the data processing system: (Anh, para 116, 119 discloses the telemetry collection, log storage, analysis, updating, and retention control are performed by 730 A based on filtering rules and predefined criteria implies this whole process is automated without user intervention)
obtaining curated telemetry data from the data processing system, the curated telemetry data comprising data points that meet data security criteria indicated by certain rules; (Anh, para 124, discloses system 730A collects packet level telemetry data based on D-based criteria or rules from PSG device);
storing the curated telemetry data in a first activity log; (Anh, para 126, discloses system 730 A stores the logs and captured packets based on criteria, rules associated with capturing and storing those packets.);
attempting to improve a security profile of the first activity log based on the certain rules to obtain a new log; (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
wherein the new log comprises updated first activity log (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
Anh does not explicitly teach; However, Verma teaches:
performing a validation of the data processing system by providing a security questionnaire to the data processing system (Verma (Col 21, lines 9-30) discloses an authentication flow for a laptop, if the authentication level is regular, the system identifies device questions to be asked regardless of the UI type by using by using at least one question keyword extracted from device data present in at least one of a device log, historic data, or a device record for a smart phone. The system presents the device question to the smart phone (device) through an API);
and causing the data processing system to automatically complete the security questionnaire without the human intervention and return a completed one of the security questionnaire to the network core (Verma (Col 21, lines 9-30) discloses the system presents the device question to the smart phone (device) through an API and receives answers for the device question. This constitutes the device receiving the questions based on device log history and receiving answer via the device without human intervention. Further, claim 14 and 20, Verma further discloses that the device questions are generated based on device’s log history and response is receive from the device without human intervention for authentication);
the security questionnaire comprising security questions based on the new log (Verma, Col 5, lines 44-67; Col 6 line 1, discloses the questions are generated based on user and device data such as device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags etc to validate or authenticate user and the device.)
Wherein the first activity log being maintained identically to a second activity log hosted by the data processing system based on certain rule; (Verma, Col 5, lines 60-65, discloses device data which includes device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags are stored in the device 104 and the storage unit 108.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Verma’s technique of maintaining identical copies of log in two separate units and performing validation of the device using the security questions based on updated logs. One would be motivated to perform such modification on Anh’s system to improve the authentication in Internet of Things (IoT) environments implementing context-aware, multi-dimensional, device and user-based question and score system for seamless and adaptive authentication. (Verma, Col 1, 2)
Verma does not explicitly teach; However, Ramaswamy teaches:
Wherein certain rules comprise data retention policy; (Ramaswamy, para 34, discloses the retention policy administrator 144 defining a particular retention rule/policy.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Ramaswamy’s technique of implementing retention rule/policy in their system. One would be motivated to perform such modification on Anh’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Regarding Claim 17, Anh teaches:
obtaining curated telemetry data from the data processing system, the curated telemetry data comprising data points that meet data security criteria indicated by certain rules; (Anh, para 124, discloses system 730A collects packet level telemetry data based on D-based criteria or rules from PSG device);
storing the curated telemetry data in a first activity log; (Anh, para 126, discloses system 730 A stores the logs and captured packets based on criteria, rules associated with capturing and storing those packets.);
attempting to improve a security profile of the first activity log based on the certain rules to obtain a new log; (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
wherein the new log comprises updated first activity log (Anh, para 130, discloses the system 730A updates the retrieved logs on the retrieved packets to create an updated log following C, D based criteria.)
Anh does not explicitly teach; However, Verma teaches:
and performing a validation of the data processing system by providing a security questionnaire to the data processing system (Verma (Col 21, lines 9-30) discloses an authentication flow for a laptop, if the authentication level is regular, the system identifies device questions to be asked regardless of the UI type by using by using at least one question keyword extracted from device data present in at least one of a device log, historic data, or a device record for a smart phone. The system presents the device question to the smart phone (device) through an API);
and causing the data processing system to automatically complete the security questionnaire without the human intervention and return a completed one of the security questionnaire to the network core (Verma (Col 21, lines 9-30) discloses the system presents the device question to the smart phone (device) through an API and receives answers for the device question. This constitutes the device receiving the questions based on device log history and receiving answer via the device without human intervention. Further, claim 14 and 20, Verma further discloses that the device questions are generated based on device’s log history and response is receive from the device without human intervention for authentication);
the security questionnaire comprising security questions based on the new log (Verma, Col 5, lines 44-67; Col 6 line 1, discloses the questions are generated based on user and device data such as device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags etc to validate or authenticate user and the device.)
Wherein the first activity log being maintained identically to a second activity log hosted by the data processing system based on certain rule; (Verma, Col 5, lines 60-65, discloses device data which includes device activity logs, time of connectivity, data downloaded or uploaded, application updates, logs uploaded, global positioning system (GPS) coordinates, websites, media tags are stored in the device 104 and the storage unit 108.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Verma’s technique of maintaining identical copies of log in two separate units and performing validation of the device using the security questions based on updated logs. One would be motivated to perform such modification on Anh’s system to improve the authentication in Internet of Things (IoT) environments implementing context-aware, multi-dimensional, device and user-based question and score system for seamless and adaptive authentication. (Verma, Col 1, 2)
Verma does not explicitly teach; However, Ramaswamy teaches:
Wherein certain rules comprise data retention policy; (Ramaswamy, para 34, discloses the retention policy administrator 144 defining a particular retention rule/policy.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh’s system to incorporate the teaching of Ramaswamy’s technique of implementing retention rule/policy in their system. One would be motivated to perform such modification on Anh’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Claims 2, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Pender (US 20170330191 A1).
Regarding Claim 2, Anh/Verma/Ramaswamy teach the method of claim 1,
Anh/Verma does not explicitly teach:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system, wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system; and deploying the data retention policy to the data processing system and the network core.
However, Ramaswamy teaches:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system; (Ramaswamy, para 28, 33, 34, discloses defining retention policy/rule which includes system's account data (file types, tags, date etc.).)
and deploying the data retention policy to the data processing system and the network core (Ramaswamy, para 39, discloses that based on instructions generated by the retention policy administrator 144 via the RPMP 112, the system 100 may deploy the unified retention policy 110 across one or more of the productivity platforms 104.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma’s system to incorporate the teaching of Ramaswamy’s technique of obtaining retention policy based on device’s data and deploying the retention policy to one or more platform. One would be motivated to perform such modification on Anh/Verma’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Ramaswamy does not explicitly teach; However, Pender teaches:
wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system (Pender, para 22, discloses generating security questions based on historical
information associated with the user account. Historical information such as login history information, usage history information which implicitly involves filtering and selection of relevant log data.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy’s system to incorporate the teaching of Pender’s technique of generating security questions for authentication based on selection of relevant log data. One would be motivated to perform such modification on Anh/Verma/Ramaswamy’s system to improve authentication across multiple interaction channels and enhance overall security and user experience. (Pender, Page 1)
Regarding Claim 14, Anh/Verma/Ramaswamy teach the non-transitory machine readable medium of claim 13,
Anh/Verma does not explicitly teach:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system, wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system; and deploying the data retention policy to the data processing system and the network core.
However, Ramaswamy teaches:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system; (Ramaswamy, para 28, 33, 34, discloses defining retention policy/rule which includes system's account data (file types, tags, date etc.).)
and deploying the data retention policy to the data processing system and the network core (Ramaswamy, para 39, discloses that based on instructions generated by the retention policy administrator 144 via the RPMP 112, the system 100 may deploy the unified retention policy 110 across one or more of the productivity platforms 104.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma’s system to incorporate the teaching of Ramaswamy’s technique of obtaining retention policy based on device’s data and deploying the retention policy to one or more platform. One would be motivated to perform such modification on Anh/Verma’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Ramaswamy does not explicitly teach; However, Pender teaches:
wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system (Pender, para 22, discloses generating security questions based on historical
information associated with the user account. Historical information such as login history information, usage history information which implicitly involves filtering and selection of relevant log data.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy’s system to incorporate the teaching of Pender’s technique of generating security questions for authentication based on selection of relevant log data. One would be motivated to perform such modification on Anh/Verma/Ramaswamy’s system to improve authentication across multiple interaction channels and enhance overall security and user experience. (Pender, Page 1)
Regarding Claim 18, Anh/Verma/Ramaswamy teach the data processing system of claim 17,
Anh/Verma does not explicitly teach:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system, wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system; and deploying the data retention policy to the data processing system and the network core.
However, Ramaswamy teaches:
prior to obtaining the curated telemetry data: obtaining the data retention policy based on a data profile of the data processing system; (Ramaswamy, para 28, 33, 34, discloses defining retention policy/rule which includes system's account data (file types, tags, date etc.).)
and deploying the data retention policy to the data processing system and the network core (Ramaswamy, para 39, discloses that based on instructions generated by the retention policy administrator 144 via the RPMP 112, the system 100 may deploy the unified retention policy 110 across one or more of the productivity platforms 104.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma’s system to incorporate the teaching of Ramaswamy’s technique of obtaining retention policy based on device’s data and deploying the retention policy to one or more platform. One would be motivated to perform such modification on Anh/Verma’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Ramaswamy does not explicitly teach; However, Pender teaches:
wherein the data points that meet the data security criteria indicated by the data retention policy are candidate data points for generation of security questions to authenticate the data processing system (Pender, para 22, discloses generating security questions based on historical
information associated with the user account. Historical information such as login history information, usage history information which implicitly involves filtering and selection of relevant log data.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy’s system to incorporate the teaching of Pender’s technique of generating security questions for authentication based on selection of relevant log data. One would be motivated to perform such modification on Anh/Verma/Ramaswamy’s system to improve authentication across multiple interaction channels and enhance overall security and user experience. (Pender, Page 1)
Claims 3, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Pender (US 20170330191 A1) further in view of Sanchez (US 10956560 B1).
Regarding Claim 3, Anh/Verma/Ramaswamy/Pender teach the method of claim 2,
Ramaswamy/Pender does not explicitly teach; However, Anh teaches:
establishing a secure connection to the data processing system (Anh, para 35, discloses cyber threat analysis system 130 and hosts (120.n-1 through 120.n-n) connected vis PSGs (policy and security gateways) as well as a network 125 and firewall (FW 153). The presence of Policy and security gaetways and firewall implies the establishment and maintenance of secure connections for data transfer. Further cyber threat detection inherently requires secure communication channels to prevent tampering or interception of threat intelligence and collected data.);
obtaining raw telemetry data from the data processing system, the raw telemetry data not being previously curated by the data processing system (Anh, para 36, discloses that the cyber threat analysis system 130 receives log data or captured packets from PSGs);
performing an analysis of the raw telemetry data to obtain a result (Anh, Fig. 3B, para 58, discloses that on step 345 the cyber threat analysis system 130 analyzes the retrieved logs and/or pcaps based on changes and produces a result which indicates whether the packet is considered suspicious or relevant to a known threat.);
obtaining the data security criteria based on the result and the data profile of the data processing system (Anh, para 120-121, discloses that the cyber threat intelligence data (CTI data) is used to create filtering-based criteria based on past measurements and known threats.);
and obtaining the data retention policy using, at least in part, the data security criteria (Anh, para 122, discloses that logs are generated and retained for predetermined amount of time (30 days, 60 days, 90 days, etc.) based on whether packets meet threat detection criteria.);
Anh does not explicitly teach; However, Sanchez teaches:
wherein the result indicating a degree of difficulty of predicting a measurement associated with each data point of the raw telemetry data by an adversary (Sanchez, Col 14, lines 25-30, discloses the addition of a fry value to the password prior to hashing is seen to cause tremendous computational intractability for attackers, while still allowing the operator or proprietor of a service using password-based authentication to quickly authenticate a user. This clearly indicates that the result (hash) reflects how difficult it would be for an attacker to predict or reverse the underlying value.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender’s system to incorporate the teaching of Sanchez’s technique of generating an output or result which would be difficult for an attacker to predict or reverse the underlying value. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender’s system for protecting password information so that the information is resistant to misuse and/or exploitation, even if the information leaks. (Sanchez, Column 1)
Regarding Claim 15, Anh/Verma/Ramaswamy/Pender teach the non-transitory machine-readable medium of claim 14,
Ramaswamy/Pender does not explicitly teach; However, Anh teaches:
establishing a secure connection to the data processing system (Anh, para 35, discloses cyber threat analysis system 130 and hosts (120.n-1 through 120.n-n) connected vis PSGs (policy and security gateways) as well as a network 125 and firewall (FW 153). The presence of Policy and security gaetways and firewall implies the establishment and maintenance of secure connections for data transfer. Further cyber threat detection inherently requires secure communication channels to prevent tampering or interception of threat intelligence and collected data.);
obtaining raw telemetry data from the data processing system, the raw telemetry data not being previously curated by the data processing system (Anh, para 36, discloses that the cyber threat analysis system 130 receives log data or captured packets from PSGs);
performing an analysis of the raw telemetry data to obtain a result (Anh, Fig. 3B, para 58, discloses that on step 345 the cyber threat analysis system 130 analyzes the retrieved logs and/or pcaps based on changes and produces a result which indicates whether the packet is considered suspicious or relevant to a known threat.);
obtaining the data security criteria based on the result and the data profile of the data processing system (Anh, para 120-121, discloses that the cyber threat intelligence data (CTI data) is used to create filtering-based criteria based on past measurements and known threats.);
and obtaining the data retention policy using, at least in part, the data security criteria (Anh, para 122, discloses that logs are generated and retained for predetermined amount of time (30 days, 60 days, 90 days, etc.) based on whether packets meet threat detection criteria.);
Anh does not explicitly teach; However, Sanchez teaches:
wherein the result indicating a degree of difficulty of predicting a measurement associated with each data point of the raw telemetry data by an adversary (Sanchez, Col 14, lines 25-30, discloses the addition of a fry value to the password prior to hashing is seen to cause tremendous computational intractability for attackers, while still allowing the operator or proprietor of a service using password-based authentication to quickly authenticate a user. This clearly indicates that the result (hash) reflects how difficult it would be for an attacker to predict or reverse the underlying value.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender’s system to incorporate the teaching of Sanchez’s technique of generating an output or result which would be difficult for an attacker to predict or reverse the underlying value. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender’s system for protecting password information so that the information is resistant to misuse and/or exploitation, even if the information leaks. (Sanchez, Column 1)
Regarding Claim 19, Anh/Verma/Ramaswamy/Pender teach the data processing system of claim 18,
Ramaswamy/Pender does not explicitly teach; However, Anh teaches:
establishing a secure connection to the data processing system (Anh, para 35, discloses cyber threat analysis system 130 and hosts (120.n-1 through 120.n-n) connected vis PSGs (policy and security gateways) as well as a network 125 and firewall (FW 153). The presence of Policy and security gaetways and firewall implies the establishment and maintenance of secure connections for data transfer. Further cyber threat detection inherently requires secure communication channels to prevent tampering or interception of threat intelligence and collected data.);
obtaining raw telemetry data from the data processing system, the raw telemetry data not being previously curated by the data processing system (Anh, para 36, discloses that the cyber threat analysis system 130 receives log data or captured packets from PSGs);
performing an analysis of the raw telemetry data to obtain a result (Anh, Fig. 3B, para 58, discloses that on step 345 the cyber threat analysis system 130 analyzes the retrieved logs and/or pcaps based on changes and produces a result which indicates whether the packet is considered suspicious or relevant to a known threat.);
obtaining the data security criteria based on the result and the data profile of the data processing system (Anh, para 120-121, discloses that the cyber threat intelligence data (CTI data) is used to create filtering-based criteria based on past measurements and known threats.);
and obtaining the data retention policy using, at least in part, the data security criteria (Anh, para 122, discloses that logs are generated and retained for predetermined amount of time (30 days, 60 days, 90 days, etc.) based on whether packets meet threat detection criteria.);
Anh does not explicitly teach; However, Sanchez teaches:
wherein the result indicating a degree of difficulty of predicting a measurement associated with each data point of the raw telemetry data by an adversary (Sanchez, Col 14, lines 25-30, discloses the addition of a fry value to the password prior to hashing is seen to cause tremendous computational intractability for attackers, while still allowing the operator or proprietor of a service using password-based authentication to quickly authenticate a user. This clearly indicates that the result (hash) reflects how difficult it would be for an attacker to predict or reverse the underlying value.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender’s system to incorporate the teaching of Sanchez’s technique of generating an output or result which would be difficult for an attacker to predict or reverse the underlying value. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender’s system for protecting password information so that the information is resistant to misuse and/or exploitation, even if the information leaks. (Sanchez, Column 1)
Claims 4, 5, 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Pender (US 20170330191 A1) further in view of Sanchez (US 10956560 B1) further in view of Chen (US 20240281522 A1).
Regarding Claim 4, Anh/Verma/Ramaswamy/Pender/Sanchez teach the method of claim 3,
Anh/Verma/Ramaswamy/Pender/Sanchez does not explicitly teach; However, Chen teaches:
obtaining a security score associated with each data point of the raw telemetry data (Chen, Para 44, discloses calculating a model anomaly risk score for each of the plurality of asset using machine learning model that analyzes asset event data related to activity in the computing environment.);
wherein obtaining the security score comprises: obtaining an inference as output from an inference model trained to generate security scores, the inference comprising the security score. (Chen, Para 53-55, discloses that the anomaly detection machine learning model is trained on behavioral attributes, and outputs a model anomaly risk score.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender/Sanchez’s system to incorporate the teaching of Chen’s technique of obtaining security scores by using machine learning model using past measurements. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender/Sanchez’s system to address a structured false positive, misidentification of any asset affected by such structured false positive as an anomaly asset may be avoided during subsequent model application. (Chen, Page 2)
Regarding Claim 5, Anh/Verma/Ramaswamy/Pender/Sanchez/Chen teach the method of claim 4,
Anh/Verma/Ramaswamy/Pender/Sanchez does not explicitly teach; However, Chen teaches:
wherein the inference model performs anomaly detection, and the security score indicates a degree of anomalousness of each data point of the raw telemetry data (Chen, Para 44, 46, discloses the model performs anomaly detection and the model anomaly risk score reflects the degree of anomaly of the asset's behavior.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender/Sanchez’s system to incorporate the teaching of Chen’s technique of obtaining security scores by using machine learning model using past measurements which indicates anomalousness of data. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender/Sanchez’s system to address a structured false positive, misidentification of any asset affected by such structured false positive as an anomaly asset may be avoided during subsequent model application. (Chen, Page 2)
Regarding Claim 6, Anh/Verma/Ramaswamy/Pender/Sanchez/Chen teach the method of claim 4,
Anh/Verma/Ramaswamy/Pender/Sanchez does not explicitly teach; However, Chen teaches:
wherein the inference model performs a variability analysis of the raw telemetry data (Chen, Para 55, 57, discloses the model performs behavioral analysis including plurality of event data as well as [0057] time series patterns.);
and the security score indicates a degree of variability associated with each feature of the raw telemetry data (Chen, Para 44, 46, discloses the model performs anomaly detection and the
model anomaly risk score reflects the degree of anomaly of the asset's behavior.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender/Sanchez’s system to incorporate the teaching of Chen’s technique of obtaining security scores by using machine learning model using past measurements which indicates anomalousness of data. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender/Sanchez’s system to address a structured false positive, misidentification of any asset affected by such structured false positive as an anomaly asset may be avoided during subsequent model application. (Chen, Page 2)
Regarding Claim 16, Anh/Verma/Ramaswamy/Pender/Sanchez teach the non-transitory machine-readable medium of claim 15,
Anh/Verma/Ramaswamy/Pender/Sanchez does not explicitly teach; However, Chen teaches:
obtaining a security score associated with each data point of the raw telemetry data (Chen, Para 44, discloses calculating a model anomaly risk score for each of the plurality of asset using machine learning model that analyzes asset event data related to activity in the computing environment.);
wherein obtaining the security score comprises: obtaining an inference as output from an inference model trained to generate security scores, the inference comprising the security score. (Chen, Para 53-55, discloses that the anomaly detection machine learning model is trained on behavioral attributes, and outputs a model anomaly risk score.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender/Sanchez’s system to incorporate the teaching of Chen’s technique of obtaining security scores by using machine learning model using past measurements. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender/Sanchez’s system to address a structured false positive, misidentification of any asset affected by such structured false positive as an anomaly asset may be avoided during subsequent model application. (Chen, Page 2)
Claims 7 is rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Pender (US 20170330191 A1) further in view of Liu (WO 2023071766 A1) further in view of Misra (US 20210051521 A1).
Regarding Claim 7, Anh/Verma/Ramaswamy/Pender teach the method of claim 3,
Anh/Verma/Ramaswamy/Pender does not explicitly teach; However, Liu teaches:
instructions for retaining a first portion of the data points that meet the data security criteria (Liu, Para 93, discloses retaining the candidate model that meets the preset rules.);
instructions for discarding a second portion of the data points that do not meet the data security criteria (Liu, Para 93, discloses deleting the candidate model that does not meets the preset rules.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender’s system to incorporate the teaching of Liu’s technique of determining the data to retain and discard as per the preset rule. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender’s system to improve accuracy of obtaining the compressed model automatically which in turns reduces manual participation and reduces labor costs. (Liu, Page 2)
Liu does not explicitly teach; However, Misra teaches:
instructions for discarding a third portion of the data points that meet the data security criteria and are similar to other data points previously marked for retention within a similarity threshold (Misra, Para 130, discloses analyzing and discarding data which are irrelevant
or redundant to reduce the data load on the requesting network).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender’s system to incorporate the teaching of Misra’s technique of determining the data to discard to avoid redundancy. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender’s system to improve intelligent data retention and processing in mobile networks without overwhelming network nodes with computational burden. (Misra, Page 2)
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Pender (US 20170330191 A1) further in view of Liu (WO 2023071766 A1) further in view of Misra (US 20210051521 A1) further in view of Fields (US 20220036465 A1).
Regarding Claim 8, Anh/Verma/Ramaswamy/Pender/Liu/Misra teach the method of claim 7,
Anh/Verma/Ramaswamy/Pender/Liu/Misra does not explicitly teach; However, Fields teaches:
wherein the policy further comprises: instructions for performing a test for ascertaining whether the policy has aged out (Fields, Para 151, discloses the server determines whether the policy is expired or not by examining the policy status.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Ramaswamy/Pender/Liu/Misra’s system to incorporate the teaching of Misra’s technique of determining if the policy is out dated. One would be motivated to perform such modification on Anh/Verma/Ramaswamy/Pender/Liu/Misra’s system for tracking policy and title data allows for such data to be protected from tampering (e.g., due to the many distributed copies and the need for quorum amongst nodes). (Fields, Page 1)
Fields does not explicitly teach; However, Ramaswamy teaches:
Wherein the policy comprises data retention policy; (Ramaswamy, para 34, discloses the retention policy administrator 144 defining a particular retention rule/policy.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Verma/Pender/Liu/Misra’s system to incorporate the teaching of Ramaswamy’s technique of implementing retention rule/policy in their system. One would be motivated to perform such modification on Anh/Verma/Pender/Liu/Misra’s system to improve retention management across multiple platforms by implementing unified, intelligent, and cross platform data retention policy system. (Ramaswamy, Page 1, 2)
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Dsouza (Dsouza, R., & lukmal. (2022, March 28). How
to manage IoT device certificate rotation using AWS IoT. https://aws.amazon.com/blogs/iot/how-to-manage-iot-device-certificate-rotation-using-aws-iot/.)
Regarding Claim 9, Anh/Verma/Ramaswamy teach the method of claim 1,
Anh/Verma/Ramaswamy does not explicitly teach; However, Dsouza teaches:
obtaining an acknowledgement from the data processing system that indicates that the policy has aged out (Dsouza, page 3, section 1, discloses that the AWS IoT defender audit system performs automated checks for expiring device certificates policy if a device certificate is expiring within 30 days or has expired);
initiating a policy regeneration process in response to the acknowledgement to obtain an updated
policy (Dsouza, page 5, section 1, discloses that Lambda function automatically initiates a certificate rotation process (policy regeneration) and generates a new certificate signing request (CSR) when the system detects that a certificate is aging out);
and deploying the updated policy to the data processing system and the network core (Dsouza, page 5, section 2, 3, discloses that once the new certificate (updated policy) is created, it is deployed to the device across the fleet via MQTT);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Ramaswamy/Verma’s system to incorporate the teaching of Dsouza’s technique of implementing retention rule/policy regeneration and deployment. One would be motivated to perform such modification on Anh/Ramaswamy/Verma’s system to improve security and ensures automatic renewal of policies to minimize any potential access disruption due to manual rotation. (Dsouza, Page 1, 2)
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Bahl (US 20080282347 A1).
Regarding Claim 11, Anh/Verma/Ramaswamy teach the method of claim 1,
Anh teaches:
Wherein the information is curated telemetry data (Anh, para 124, discloses system 730A collects packet level telemetry data based on D-based criteria or rules from PSG device.);
Anh/Verma/Ramaswamy does not explicitly teach; However, Bahl teaches:
wherein the information is obtained prior to a loss of a root of trust between the data processing system and the network core (Bahl, para 66, discloses that if at step 630, the Network Risk Management Service (NRMS) 210 determines that further information is required, it can wait until such information is received at step 640 before determining that the host is infected at step 650.);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Ramaswamy/Verma’s system to incorporate the teaching of Bahl’s technique of obtaining information before the host is infected. One would be motivated to perform such modification on Anh/Ramaswamy/Verma’s system to avoid long period of exposure by enabling near-instant reaction to threats and allow rapid updates from various data sources. (Bahl, Page 1)
Regarding Claim 12, Anh/Verma/Ramaswamy/Bahl teach the method of claim 11,
Anh teaches:
Wherein the specific system is the network core (Anh, para 116, 119 discloses the telemetry collection, log storage, analysis, updating, and retention control are performed by 730 A based on filtering rules and predefined criteria);
Anh/Verma/Ramaswamy does not explicitly teach; However, Bahl teaches:
wherein the validation of the data processing system by the specific system re-establishes the root of trust between the data processing system and the specific system. (Bahl, para 67, discloses reauthentication or reconfiguring the host which inherently implies once the host is reauthenticated, NRMS determines the host is trustworthy.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Ramaswamy/Verma’s system to incorporate the teaching of Bahl’s technique of reauthenticating the host which acts as a validation. One would be motivated to perform such modification on Anh/Ramaswamy/Verma’s system to avoid long period of exposure by enabling near-instant reaction to threats and allow rapid updates from various data sources. (Bahl, Page 1)
Claims 21 is rejected under 35 U.S.C. 103 as being unpatentable over Anh (US 20240031392 A1) in view of Verma (US 10334439 B2) in view of Ramaswamy (US 20180276223 A1) in view of Bahl (US 20080282347 A1) in view of Lockett (US 20140380425 A1).
Regarding Claim 21, Anh/Verma/Ramaswamy teach the method of claim 1,
Anh teaches:
Wherein the specific system is the network core (Anh, para 116, 119 discloses the telemetry collection, log storage, analysis, updating, and retention control are performed by 730 A based on filtering rules and predefined criteria);
Anh/Verma/Ramaswamy does not explicitly teach; However, Bahl teaches:
wherein the validation of the data processing system is performed by the specific system to re-establish a root of trust between the network and the data processing system, (Bahl, para 67 discloses reauthentication or reconfiguring the host which inherently implies once the host is reauthenticated, NRMS determines the host is trustworthy)
without actual involvement of the user and further without involvement of other ones of the data processing systems within the distributed environment that share a same one of the root of trust as the data processing system (Bahl, para 67 discloses remedial actions like reauthentication or reconfiguring the appropriate hosts can be policy driven. While Bahl mentions about subscribers which are firewall, email server (para 47), and no other client devices.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Ramaswamy/Verma’s system to incorporate the teaching of Bahl’s technique of reauthenticating the host which acts as a validation. One would be motivated to perform such modification on Anh/Ramaswamy/Verma’s system to avoid long period of exposure by enabling near-instant reaction to threats and allow rapid updates from various data sources.
Anh/Verma/Ramaswamy/Bahl does not explicitly teach; However, Lockett teaches:
that was originally manually established by a user associated with the specific system and the data processing system (Lockett, para 232 discloses that user controls provide signals trusted as having been generated in response to input by an authorized user. Lockett further discloses that the use of biometric authentication techniques, including fingerprint, face, iris and voice recognition, to establish user authentication);
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified Anh/Ramaswamy/Verma/Bahl’s system to incorporate the teaching of Lockett’s technique of user establishing authentication via the use of biometric techniques like fingerprint, face, iris and voice recognition. One would be motivated to perform such modification on Anh/Ramaswamy/Verma/Bahl’s system to provide a predictable and well-understood mechanism for initially establishing trust, while still allowing the system to subsequently validate and reestablish trust automatically without user intervention thereby improving security, usability and system robustness.
Conclusion
THIS ACTION IS MADE FINAL. 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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/AMIT KHADKA/Examiner, Art Unit 2432
/Jeffrey Nickerson/Supervisory Patent Examiner, Art Unit 2432