Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. EXAMINER’S NOTE: The claims have been reviewed and considered under the new guidance pursuant to the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG 2019) issued January 7, 2019.
3. This communication is in response to Applicant’s amendment filed on 28 April 2026. Claims 1, 5, 9-11, 15, 18-20 have been amended. Claims 1-20 remain pending.
Response to Arguments
4. Applicant’s arguments, see pages 6-11, filed on 28 April 2026, with respect to the 103 rejection in view of Olden et al. has been fully considered, but are moot in view of the new grounds of rejection. In light of the newly amended claim limitations, a new ground of rejection is hereby presented in view of Brandt et al. (Pub No. 2021/0067548).
5. In light of the previous double patenting rejection, the Applicant has filed a terminal disclaimer to overcome the rejection, the terminal disclaimer was approved on 28 April 2026, therefore, the rejection has been withdrawn.
6. In light of the previous 101 rejection, the Applicant amended claim 20 by incorporating “comprising circuitry”, therefore, the rejection has been withdrawn.
7. In light of the Applicant’s arguments for claims 1, 11, and 20, the Applicant traverses that the prior art, Olden et al. fail to disclose, suggest, or teach the claimed limitation “predicting, based on historical data, that the user is to request access to one or more second resources with at least a threshold probability” because Olden et al. features merely validate current session security based on historical data with at least a threshold probability which will cause the access server to generate an authorization response to a predicted request for associating an authorization level with the user requesting access to particular resources.
The Examiner respectfully disagrees and asserts that Olden et al. discloses a method and system for processing authorization requests across unified access management systems (UAM systems).
Olden discloses predicting, based on historical data, that the user is to request access to one or more second resources with at least a threshold probability wherein the cookie is stored in memory on the browser computer, preventing malicious users from reading the cookie from disk. Second, the cookie contains IP address-specific information that is checked to see whether the information is coming from the IP address for which it was created, preventing a malicious user from stealing the cookie and using it from another computer. Third, the cookie has time out settings for inactivity that renders it unusable after a determined period of inactivity. This prevents a malicious user from using a vacant computer to gain access to a trusted user's protected resources. Fourth, the cookie has a maximum lifetime setting that forces a re-authentication if the time threshold is exceeded. The time out settings, both for inactivity and maximum lifetime, are set on a per Web server basis. Based on this historical data, protects the passwords by encrypting communication and allows the web applications to be secured by requiring user information to be transferred back to them to enable seamless sign-on as disclosed in Column 24, lines 29-63.
Claim Rejections - 35 USC § 103
8. 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.
9. 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.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olden et al. (US Patent No. 6,460,141) in view of Brandt et al. (Pub No. 2021/0067548).
Referring to the rejection of claim 1, Olden et al. discloses a method comprising:
obtaining, from an access server, an authorization response to a request to evaluate an authorization level associated with a user accessing one or more first resources; (See Olden et al., Col. 8, lines 15-33, i.e., a user is granted or denied access privilege to a resource depending on the policy level as defined with three resultants – allow, deny, or require. Allow permits the user to access the resources without any further rule processing, deny forbids the user access without any further rule processing, and require is satisfied if accessibility is determined, if not satisfied, deny the user access)
predicting, based on historical data, that the user is to request access to one or more second resources with at least a threshold probability; (See Olden et al., Col. 24, lines 29-63, i.e., the cookie is stored in memory on the browser computer, preventing malicious users from reading the cookie from disk. Second, the cookie contains IP address-specific information that is checked to see whether the information is coming from the IP address for which it was created, preventing a malicious user from stealing the cookie and using it from another computer. Third, the cookie has time out settings for inactivity that renders it unusable after a determined period of inactivity. This prevents a malicious user from using a vacant computer to gain access to a trusted user's protected resources. Fourth, the cookie has a maximum lifetime setting that forces a re-authentication if the time threshold is exceeded. The time out settings, both for inactivity and maximum lifetime, are set on a per Web server basis. Based on this historical data, protects the passwords by encrypting communication and allows the web applications to be secured by requiring user information to be transferred back to them to enable seamless sign-on)
Olden et al. fail to explicitly disclose and causing the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources.
Brandt et al. discloses a technique and system for detecting malicious activity within a network.
Brandt et al. discloses and causing the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources. (See Brandt et al., para. 36-41, 50, 53-56, i.e., Once the factors associated with the attribute are identified, the predictive modeling service may request those factors from the unified compute engine. The unified compute engine may include a set of logic capable of returning model factor data, such as aggregation, summaries, or other basic mathematical operations to support the inputs required of a model. The unified compute engine may transform raw data into a form that can be ingested into a model. The unified compute engine may then retrieve the factors from the database. In one embodiment, the retrieved factors may be precomputed factors. The precomputed factors may be computed previously or offline. These precomputed factors may be computed any time prior to being retrieved (e.g., 1 day ago, 6 hours ago, etc.). The communications subsystem may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, etc.). The communications subsystem may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores that may be in communication with one or more streaming data source computers coupled to network-based authentication system. After the one or more learning statistical models are determined, the rule engine may request model scores for each of the models. The request may be made to the predictive modeling service. The predictive modeling service may determine what factors are required for each of the learning statistical models. Once predictive modeling service identifies factors, predictive modeling service may request factor values based on factors from unified compute engine. The model scores may be returned to the predictive modeling service. The predictive modeling service may share the model scores the rule engine. Based on the model scores, the rule engine may render a decision on whether the network transaction is malicious and/or whether to authorize the network transaction. When a model score is below a threshold, the network-based authentication system may determine that the network transaction is not malicious and may authorize the network transaction. If a model score is at or above the threshold, network-based authentication system may not authorize the network transaction)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine Olden et al.’s security and access management system provides unified access management to address the specific problems facing the deployment of security for the Web and non-Web environment modified with Brandt et al.’s technique and system for detecting malicious activity within a network.
Motivation for such an implementation would enable a predictive modeling service for requesting precomputed factors based on previous network transactions processed by the network-based authentication system. (See Brandt et al., para. 53)
Referring to the rejection of claims 2 and 12, (Olden et al. modified by Brandt et al.) discloses wherein the historical data comprises statistical information associated with the user accessing the one or more second resources in association with one or more previous user sessions. (See Olden et al., Col. 26, lines 30-36 and Col. 28, lines 6-17, i.e., the log file rotates itself automatically. In other words, when the log file reaches a pre-defined size, which is preferably configurable, the log file is time stamped, and a new log file is created. The administrator can elect to implement a process for archiving these log files, as they typically grow relatively fast because they represent all protected intranet/extranet usage. The management server in turn directs FireWall-1 modules to terminate previous sessions or deny access to specific hosts which have been identified by SAMP clients as generating suspicious activity on the network or server system. The specific actions taken by the firewall might include terminating a current session in progress or blocking new session attempts that match the criteria over a specified time period in the future)
Referring to the rejection of claims 3 and 13, (Olden et al. modified by Brandt et al.) discloses wherein the statistical information is indicative of one or more correlations between the user accessing the one or more second resources and accessing the one or more first resources. (See Olden et al., Col. 28, lines 18-35, i.e., The SAMP client application is capable of identifying suspicious activity on the network or a specific host. Examples of such activity for correlation between user and one or more resources include: a client making repeated connection attempts to privileged services on a specific host (e.g., scanning); a client attempting to issue illegal commands or repeatedly failing to complete a login to a server system for which access by the client would generally be considered permissible (e.g., a user accessing an Internet-accessible Web server, but attempting to send illegal CGI commands through a form); or any other criteria set which if met, qualifies the activity as an inferred security threat and not allowing connections to pass through the firewall unless they are already allowed by the explicitly defined management policies)
Referring to the rejection of claims 4 and 14, (Olden et al. modified by Brandt et al.) discloses wherein the request is responsive to the user requesting access to the one or more first resources. (See Olden et al., Col. 9, lines 27-34, Col. 25, lines 43-67 and Col. 26, lines 1-6, i.e., when a user request to access resources occur, there are thirteen types of events that are in response during the authentication/authorization process. The security and access management system records the following user events - 1. Invalid user 2. Invalid password 3. Inactive account 4. Expired account 5. User denied based on user level explicit entitlement 6. User denied based on group level explicit entitlement 7. User denied based on realm level explicit entitlement 8. User denied based on smart rule 9. User denied because no entitlement existed 10. User allowed based on user level explicit entitlement 11. User allowed based on group level explicit entitlement 12. User allowed based on realm level explicit entitlement 13. User allowed based on smart rule. During the request, the Web server grants access to the user to different application functions)
Referring to the rejection of claims 5 and 15, (Olden et al. modified by Brandt et al.) discloses further comprising: receiving, from the access server, the authorization response and the pre-processed authorization response; and granting, responsive to the receiving, access to at least one of: the one or more first resources, or the one or more second resources. (See Brandt et al., para. 39 and 69-78, i.e., After the real-time and/or precomputed factor values have been retrieved or determined, the unified compute engine may then transmit both the precomputed factor values and the real-time factor values to the predictive modeling service. The predictive modeling service may then transmit the real-time factor values and/or the precomputed factor values to the model execution engine. The model execution engine may execute the one or more learning statistical models based on the real-time and/or precomputed factor values. For example, a first learning statistical model may be utilized to determine if a network transaction is authorized based on source location A. At step 505, the network-based authentication system may receive a network transaction. At step 510, the network-based authentication system may identify a first attribute of the network transaction. At step 515, the network-based authentication system may select a first learning statistical model and a second learning statistical model. After selecting the first learning statistical model and the second learning statistical model, at step 520, the network-based authentication system may calculate a first score for the network transaction using the first learning statistical model. At step 525, the network-based authentication system may calculate a second score for the network transaction using the second learning statistical model. At step 530, the first score may be compared to a first threshold to determine whether the first score exceeds the first threshold. In another example, a first score exceeding a first threshold may be determined based on a plurality of attributes of the network transaction and validity periods associated with the plurality of attributes)
The rationale for combining Olden et al. in view of Brandt et al. is the same as claim 1.
Referring to the rejection of claims 6 and 16, (Olden et al. modified by Brandt et al.) discloses wherein at least one of the one or more first resources or the one or more second resources comprise at least one of: a gaming application, a video streaming application, an audio streaming application, or a database application. (See Olden et al., Col. 8, lines 34-38, i.e., the resource definition architecture comprises an application architecture which groups protected resources into web-based applications (i.e., database applications) comprised of Uniform Resource Identifiers (URIs)
Referring to the rejection of claims 7 and 17, (Olden et al. modified by Brandt et al.) discloses wherein the one or more first resources are hosted using a distributed computing infrastructure. (See Olden et al., Col. 5, lines 10-21 and Col. 6, lines 45-48, i.e., one or more resources are hosted using distributed computing infrastructure (i.e., distributed authorized servers and a security and access management system provides a highly flexible and scalable data model for defining both accessibility of resources and applications)
Referring to the rejection of claim 8, (Olden et al. modified by Brandt et al.) discloses wherein the access server comprises a unified access management (UAM) server. (See Olden et al., Col. 30, lines 33-49, i.e., a unified access management server is disclosed)
Referring to the rejection of claims 9 and 18, (Olden et al. modified by Brandt et al.) discloses wherein at least one of the request or the pre-processed request causes the access server to download one or more policies associated with an authorization of the user to access at least one of: the one or more first resources, or the one or more second resources. (See Brandt et al., para. 52-56, i.e., Rule engine 102 may determine a machine learning model. The machine learning model may include one or more learning statistical models. The one or more learning statistical models may be the same as models. Rule engine may determine one or more learning statistical models that are applicable to the network transaction. After the one or more learning statistical models are determined, the rule engine may request model scores for each of the models. The request may be made to the predictive modeling service. The predictive modeling service may determine what factors are required for each of the learning statistical models. Once predictive modeling service identifies factors, predictive modeling service may request factor values based on factors from unified compute engine. Factor values may include real-time factors and/or precomputed factors. Real-time factors may be factors associated with a network transaction that occur or are identified at the time or within a period of time close to when the network transaction is received by the network-based authentication system. Database may return precomputed factor values and transaction data to unified compute engine. Using the transaction data, the unified compute engine may compute or determine real-time factor values. Predictive modeling service may receive the real-time factor values and precomputed factor values from the unified compute engine. Predictive modeling service may request model execution engine to execute the one or more learning statistical models using the real-time factor values and the precomputed factor values. Model execution engine may return a model score for each of the one or more learning statistical models after execution. The model scores may be returned to the predictive modeling service. The predictive modeling service may share the model scores the rule engine. Based on the model scores, the rule engine may render a decision on whether the network transaction is malicious and/or whether to authorize the network transaction. The rule engine may render or determine decision. One or more network transactions may need to invoke user assistance in order to process. For example, if a network transaction is significant (e.g., large monetary value associated with the network transaction) or sensitive in nature, it may need a user such as a system administrator to aid in processing the network transaction. In such an example, a system administrator's authorization may be needed to authenticate a network transaction that is above a threshold size or certain sensitivity level)
The rationale for combining Olden et al. in view of Brandt et al. is the same as claim 1.
Referring to the rejection of claims 10 and 19, (Olden et al. modified by Brandt et al.) discloses further comprising: receiving, responsive to an additional request, the predicted authorization response from the access server, wherein the additional request is responsive to the user requesting access to the one or more second resources. (See Brandt et al., para. 36-41, 50, 53-56, i.e., Once the factors associated with the attribute are identified, the predictive modeling service may request those factors from the unified compute engine. The unified compute engine may include a set of logic capable of returning model factor data, such as aggregation, summaries, or other basic mathematical operations to support the inputs required of a model. The unified compute engine may transform raw data into a form that can be ingested into a model. The unified compute engine may then retrieve the factors from the database. In one embodiment, the retrieved factors may be precomputed factors. The precomputed factors may be computed previously or offline. These precomputed factors may be computed any time prior to being retrieved (e.g., 1 day ago, 6 hours ago, etc.). The communications subsystem may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, etc.). The communications subsystem may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores that may be in communication with one or more streaming data source computers coupled to network-based authentication system. After the one or more learning statistical models are determined, the rule engine may request model scores for each of the models. The request may be made to the predictive modeling service. The predictive modeling service may determine what factors are required for each of the learning statistical models. Once predictive modeling service identifies factors, predictive modeling service may request factor values based on factors from unified compute engine. The model scores may be returned to the predictive modeling service. The predictive modeling service may share the model scores the rule engine. Based on the model scores, the rule engine may render a decision on whether the network transaction is malicious and/or whether to authorize the network transaction. When a model score is below a threshold, the network-based authentication system may determine that the network transaction is not malicious and may authorize the network transaction. If a model score is at or above the threshold, network-based authentication system may not authorize the network transaction)
The rationale for combining Olden et al. in view of Brandt et al. is the same as claim 1.
Referring to the rejection of claim 11, (Olden et al. modified by Brandt et al.) discloses a system comprising: (See Olden et al., Fig. 1, a security and access management system, item 10 is disclosed)
a memory device; (See Olden et al., Fig. 1, a security and access management system, item 10 comprises an entitlements database, item 32)
and one or more processing units, communicatively coupled to the memory device, to: (See Olden et al., Fig. 1, a security and access management system comprises a computer disclosed as the processor coupled to the memory)
obtain, from an access server, an authorization response to a request to evaluate an authorization level associated with a user accessing one or more first resources; (See Olden et al., Col. 8, lines 15-33, i.e., a user is granted or denied access privilege to a resource depending on the policy level as defined with three resultants – allow, deny, or require. Allow permits the user to access the resources without any further rule processing, deny forbids the user access without any further rule processing, and require is satisfied if accessibility is determined, if not satisfied, deny the user access)
predict, based on historical data, that the user is to request access to one or more second resources with at least a threshold probability; (See Olden et al., Col. 24, lines 29-63, i.e., the cookie is stored in memory on the browser computer, preventing malicious users from reading the cookie from disk. Second, the cookie contains IP address-specific information that is checked to see whether the information is coming from the IP address for which it was created, preventing a malicious user from stealing the cookie and using it from another computer. Third, the cookie has time out settings for inactivity that renders it unusable after a determined period of inactivity. This prevents a malicious user from using a vacant computer to gain access to a trusted user's protected resources. Fourth, the cookie has a maximum lifetime setting that forces a re-authentication if the time threshold is exceeded. The time out settings, both for inactivity and maximum lifetime, are set on a per Web server basis. Based on this historical data, protects the passwords by encrypting communication and allows the web applications to be secured by requiring user information to be transferred back to them to enable seamless sign-on)
Olden et al. fail to explicitly disclose and cause the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources.
Brandt et al. discloses a technique and system for detecting malicious activity within a network.
Brandt et al. discloses and cause the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources. (See Brandt et al., para. 36-41, 50, 53-56, i.e., Once the factors associated with the attribute are identified, the predictive modeling service may request those factors from the unified compute engine. The unified compute engine may include a set of logic capable of returning model factor data, such as aggregation, summaries, or other basic mathematical operations to support the inputs required of a model. The unified compute engine may transform raw data into a form that can be ingested into a model. The unified compute engine may then retrieve the factors from the database. In one embodiment, the retrieved factors may be precomputed factors. The precomputed factors may be computed previously or offline. These precomputed factors may be computed any time prior to being retrieved (e.g., 1 day ago, 6 hours ago, etc.). The communications subsystem may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, etc.). The communications subsystem may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores that may be in communication with one or more streaming data source computers coupled to network-based authentication system. After the one or more learning statistical models are determined, the rule engine may request model scores for each of the models. The request may be made to the predictive modeling service. The predictive modeling service may determine what factors are required for each of the learning statistical models. Once predictive modeling service identifies factors, predictive modeling service may request factor values based on factors from unified compute engine. The model scores may be returned to the predictive modeling service. The predictive modeling service may share the model scores the rule engine. Based on the model scores, the rule engine may render a decision on whether the network transaction is malicious and/or whether to authorize the network transaction. When a model score is below a threshold, the network-based authentication system may determine that the network transaction is not malicious and may authorize the network transaction. If a model score is at or above the threshold, network-based authentication system may not authorize the network transaction)
The rationale for combining Olden et al. in view of Brandt et al. is the same as claim 1.
Referring to the rejection of claim 20, (Olden et al. modified by Brandt et al.) discloses a processor, comprising circuitry, to: (See Olden et al., Fig. 1, a security and access management system comprises a computer operated by a user disclosed as the processor)
obtain, from an access server, an authorization response to a request to evaluate an authorization level associated with a user accessing one or more first resources; ; (See Olden et al., Col. 8, lines 15-33, i.e., a user is granted or denied access privilege to a resource depending on the policy level as defined with three resultants – allow, deny, or require. Allow permits the user to access the resources without any further rule processing, deny forbids the user access without any further rule processing, and require is satisfied if accessibility is determined, if not satisfied, deny the user access)
predict, based on historical data, that the user is to request access to one or more second resources with at least a threshold probability; (See Olden et al., Col. 24, lines 29-63, i.e., the cookie is stored in memory on the browser computer, preventing malicious users from reading the cookie from disk. Second, the cookie contains IP address-specific information that is checked to see whether the information is coming from the IP address for which it was created, preventing a malicious user from stealing the cookie and using it from another computer. Third, the cookie has time out settings for inactivity that renders it unusable after a determined period of inactivity. This prevents a malicious user from using a vacant computer to gain access to a trusted user's protected resources. Fourth, the cookie has a maximum lifetime setting that forces a re-authentication if the time threshold is exceeded. The time out settings, both for inactivity and maximum lifetime, are set on a per Web server basis. Based on this historical data, protects the passwords by encrypting communication and allows the web applications to be secured by requiring user information to be transferred back to them to enable seamless sign-on)
Olden et al. fail to explicitly disclose and cause the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources.
Brandt et al. discloses a technique and system for detecting malicious activity within a network.
Brandt et al. discloses and cause the access server to generate a pre-processed authorization response to a predicted request to evaluate an authorization level associated with the user accessing the one or more second resources. (See Brandt et al., para. 36-41, 50, 53-56, i.e., Once the factors associated with the attribute are identified, the predictive modeling service may request those factors from the unified compute engine. The unified compute engine may include a set of logic capable of returning model factor data, such as aggregation, summaries, or other basic mathematical operations to support the inputs required of a model. The unified compute engine may transform raw data into a form that can be ingested into a model. The unified compute engine may then retrieve the factors from the database. In one embodiment, the retrieved factors may be precomputed factors. The precomputed factors may be computed previously or offline. These precomputed factors may be computed any time prior to being retrieved (e.g., 1 day ago, 6 hours ago, etc.). The communications subsystem may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, etc.). The communications subsystem may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores that may be in communication with one or more streaming data source computers coupled to network-based authentication system. After the one or more learning statistical models are determined, the rule engine may request model scores for each of the models. The request may be made to the predictive modeling service. The predictive modeling service may determine what factors are required for each of the learning statistical models. Once predictive modeling service identifies factors, predictive modeling service may request factor values based on factors from unified compute engine. The model scores may be returned to the predictive modeling service. The predictive modeling service may share the model scores the rule engine. Based on the model scores, the rule engine may render a decision on whether the network transaction is malicious and/or whether to authorize the network transaction. When a model score is below a threshold, the network-based authentication system may determine that the network transaction is not malicious and may authorize the network transaction. If a model score is at or above the threshold, network-based authentication system may not authorize the network transaction)
The rationale for combining Olden et al. in view of Brandt et al. is the same as claim 1.
Conclusion
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COURTNEY D FIELDS whose telephone number is (571)272-3871. The examiner can normally be reached IFP M-F 8am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SHEWAYE GELAGAY can be reached at (571)272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COURTNEY D FIELDS/Examiner, Art Unit 2436 July 16, 2026
/SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436
