DETAILED ACTION
This Action is in response to the RCE Amendment for Application Number 17647312 received on 2/02/2026.
Claims 1-19 are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/20/2026 has been entered.
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 .
Claim Objections
Claim 1 and 19 are objected to because of the following informalities:
Claim 1 recites the limitation, “metric data indicating performance of the one or more objects in real time”, which appears to include a minor typographical error. For examination purposes, the limitation will be interpreted to recite, “the metric data indicating performance of the one or more objects in real time”.
Claim 19 recites the limitation, “event of interst”, which appears to include grammatical typo.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation, “wherein the event of interest is generated when the first metric data of the first virtual machine and the second virtual machine are both performing outside nominal thresholds”, which is found indefinite because it is not clear how “first metric data” can “perform outside nominal thresholds”.
Claims 10 and 19 both recite the limitation, “wherein the event of interest is generated when the first metric data of the first virtual machine and the second metric data of the second virtual machine are both performing outside nominal thresholds”, which is found indefinite because it is not clear how “first metric data” or “second metric data” can “perform outside nominal thresholds”.
Claims 2-9 and 11-18 are rejected for the same reasons above by virtue of their dependencies to claims 1 and 10.
For examination purposes, the respective limitation of claims 1, 10 and 19 will be interpreted to recite, “wherein the event of interest is generated when the first metric data of the first virtual machine and the second metric data of the second virtual machine indicate that the first virtual machine and the second virtual machine are both performing outside nominal thresholds”.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 10-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 9557879) in view of Harutyunyan et al. (US 20210218619).
Regarding claim 1, Wang disclosed a system comprising:
one or more processors (Wang, col. 22, lines 48-67, “processor”);
memory containing instructions configured to control the one or more processors (Wang, col. 22, lines 48-67, “software module can reside in…memory”); to:
receive network data related to objects of an enterprise system, the network data being received from a plurality of enterprise monitoring systems, each object being a digital device, a virtual machine, virtual device, or application (col. 1, lines 45-50, Wang disclosed, “receiving monitoring data obtained from a plurality of monitored resources in a computing environment. The method can further include transforming the monitoring data into a topology model having a plurality of interconnected topology objects. The topology objects can represent the monitored resources.”; col. 4, line 66 through col. 5, line 12, the monitored resources include “devices”, “virtual machines”, “applications” etc. Wang lists many other types; col. 5, lines 13-26; Wang disclosed the monitoring data provided by “agents”);
analyze the received network data to identify metric data related to one or more objects of the enterprise system, the metric data including object identifiers associated with the one or more objects, the metric data indicating performance of the one or more objects in real time (Wang, col. 5, lines 39-55, Wang disclosed the monitoring data includes “performance data” of the monitored resources; col. 5, lines 55-60, “process the monitoring data”; col. 6, lines 25-35, “information from a physical environment can be gathered from one or more monitored resources in the physical environment layer 210 and transformed through the data collection layer 220 into a real-time topology model (the topology model layer 230) that is capable of dynamically representing the complex interconnected nature of the computing environment”; Such transforming therefore includes analyzing the performance data; col. 5, lines 20-30, “each of the monitored resources 102 can be considered an object, and data collected about each object can be stored in the topology model”; See Figure 5, 512 or Figure 6, 602 or Figure 7, 712-716, or Figure 9A-G all of which show dependency views between hosts having object identifiers; See col. 7, line 56 through col. 8, line 10, Wang disclosed the dependency detector discovering and modeling infrastructure and transactional dependencies, and can “identify, from this collected data, resources 102 that are communicating with each other” and “the dependency detector 142 can create a dependency between the communicating resources 102 as a directed edge in the dependency graph”; That is, the collected data includes object identifiers that are utilized in creating the graph depicting the dependencies between the resources);
receive, from a user interface, a selection of an object identifier of an enterprise system, the object identifier identifying a first virtual machine of the objects of the enterprise network (Fig. 3B, 352, “Receive a user selection of an object in a topology model”; col. 4, line 66 through col. 5, line 12, the monitored resources include “devices”, “virtual machines”; col. 1, lines 48-55, The object represent the monitored resources; col. 11, line 16-25, “The process 350 begins at block 352, where the dashboard user interface module 150 receives a user selection of an object in a topology model. The user selection can be provided in response to first outputting one or more topology objects for display to the user. In another embodiment, a menu of available types of topology objects can first be output for display to the user (see, e.g., FIG. 4), from which the user can make the selection of a topology object or type of topology object.” Selection of an object on the dashboard amounts to claimed selection of an object identifier representing the resource);
identify an object subset that includes the object identifier, the object subset including at least a second virtual machine of the objects of the enterprise system that are in communication with the first virtual machine (Wang, Fig. 3B, 354, “Identify the object in a dependency graph”, The identifying of the object in a dependency graph amounts to identifying a subset of objects that have a discovered dependency; Fig. 3B, 356, “Identify dependencies of the object by transitively searching the graph for a next node” ; col. 11, lines 25-30, “Subsequently, the custom filter module 146 can apply one or more filters, including custom filters, to obtain dependencies related to the selected object”; See col. 7, line 55 through col. 8, line 9, Wang disclosed the dependencies including “model infrastructure and transactional dependencies” including relationships between the objects by communication; col. 8, lines 15-37, Wang disclosed the dependency relationship includes a relationship to processes; See also col. 2, lines 19-25, “dependencies between first ones of the topology objects based at least in part on observed interactions between second ones of the topology objects and the interconnections between the topology objects represented in the topology model; See also col. 9, lines 32-45, “monitored resources 102 or objects can be further grouped together into services. For instance, services can include logical groupings of hosts and/or other objects together based on functionality”; col. 4, line 66 through col. 5, line 12, the monitored resources include “devices”, “virtual machines”; col. 1, lines 48-55, The object represent the monitored resources);
determine one or more metric data of a particular object in the object subset that are performing outside nominal thresholds (Wang, col. 5, lines 50-55, “This performance data can also include alarms or alerts that indicate whether certain monitored resource 102 characteristics are outside of established performance criteria.”);
perform analysis of the determined one or more metric data to generate an event of interest associated with the particular object of the object subset (Wang, col. 5, lines 53-55, “The computing management system 110 can include one or more physical or virtual servers that process the monitoring data. A topology engine 130 of the computing management system 110 can transform the monitoring data into the topology model. The topology model can include a plurality of interrelated topology objects, where each topology object can represent a monitored resource 102.”; See also col. 5, line 56- col. 6, line 15 for more info regarding creation of the topology model involving nodes/objects having a relationship; col. 6, lines 26-35, “The computing management system 110 data architecture 200, as illustrated in FIG. 2, shows how information from a physical environment can be gathered from one or more monitored resources in the physical environment layer 210 and transformed through the data collection layer 220 into a real-time topology model (the topology model layer 230) that is capable of dynamically representing the complex interconnected nature of the computing environment”; col. 21, lines 34-40, “For instance, in one embodiment a filtered dependency graph can be provided to a performance monitoring process that analyzes the filtered dependency graph”; Wang, col. 21, lines 29-55, Wang disclosed analyzing the dependency graph and “send[ing] an alarm to a user if a dependency in the filtered dependency graph has changed (e.g., without permission). The custom filter module 146 can provide data representing a comparison between a previous and current dependency graph view”),
provide an event alert, to the user interface, based on the event of interest associated with the particular object of the object subset (Wang, col. 21, lines 29-55, Wang disclosed analyzing the dependency graph and “send[ing] an alarm to a user if a dependency in the filtered dependency graph has changed (e.g., without permission). The custom filter module 146 can provide data representing a comparison between a previous and current dependency graph view”).
Wang did not explicitly disclose wherein the one or more metric data includes a first metric of the first virtual machine in the object subset and a second metric data of the second virtual machine of the object subset, the first metric being different from the second metric; wherein the event of interest is generated when the first metric data of the first virtual machine and the second metric data of the second virtual machine indicate that the first virtual machine and the second virtual machine are both performing outside nominal thresholds. Wang also did not disclosed identifying a list of display metrics based on the metric data associated with the event of interest and metric data associated with the one or more objects of the object subset; and providing the list of display metrics.
In an analogous art, Harutyunyan disclosed receiving, from a user interface, a selection of an object identifier of an enterprise system, the object identifier identifying a first virtual machine of the objects of the enterprise network (Harutyunyan, [0004], Harutyunyan disclosed objects in a data center represent virtual machines; [0110], "The user selects objects of an object topology to compute correlations with the selected metric using a drop-down menu 1904"; );
identifying an object subset that includes the object identifier (Harutyunyan, [0110], In response to the object selection above, Harutyunyan disclosed "The object topology associated with the selected metric is displayed in window 1906 with the object associated with the selected metric identified by a circle 1908"),
the object subset including at least a second virtual machine of the objects of the enterprise system that are in communication with the first virtual machine (Harutyunyan, [0004], Harutyunyan disclosed objects in a data center represent virtual machines; [0085], "the object topology may be further divided based on whether the objects are related. For example, the first level of VMs may be divided into two levels if VMs 1506-1509 comprise modules of a first distributed application and VMs 1510 and 1511 comprise modules of a second distributed application"; Determining of an object topology of VMs of a distributed application amount to identifying an object subset of VMs in communication);
determining one or more metric data of a particular object in the object subset that are performing outside nominal thresholds, wherein the one or more metric data includes a first metric of the first virtual machine in the object subset and a second metric data of the second virtual machine of the object subset, the first metric being different from the second metric (Harutyunyan, [0087] "Methods and systems described herein compute correlations between metrics of an object exhibiting unexpected abnormal behavior and other metrics of objects in different levels of an object topology of a data center"; [0097], "the selected metric v.sub.s may have been identified because recently generated metric values violated a threshold for the metric, indicating a performance problem with the object O.sub.A.");
performing analysis of the determined one or more metric data to generate an event of interest associated with the particular object of the object subset, wherein the event of interest is generated when the first metric data of the first virtual machine and the second metric data of the second virtual machine indicate that the first virtual machine and the second virtual machine are both performing outside nominal thresholds (Harutyunyan, [0103], Harutyunyan disclosed analysis of the metric data involving computing correlation coefficients of a selected metric and metrics of objects in the object topology, computed over a recent time interval, and compared to a correlation threshold to determine which metrics are correlated with the selected metric; [0106]-[0109], Harutyunyan disclosed generating an event of interest by determining a ranked order of correlated metrics reduced to a list of unexpected metrics (changed) by discarding correlated metrics that are correlated with the selected metric over the recent time interval and are correlated with the selected metric over historical time intervals when the objects in the object topology exhibited normal behavior. The unexpected metrics are metrics that have not historically been correlated with the selected metric and may be useful in troubleshooting a performance problem);
identifying a list of display metrics based on metric data associated with the event of interest and metric data associated with one or more objects of the object subset ([0106]-[0109], "a ranked order of correlated metrics reduced to a list of unexpected metrics"); and
providing the list of display metrics and an event alert, to the user interface, based on the event of interest associated with the particular object of the object subset (Harutyunyan, [0110], "The user may then view the selected metric and the highest ranked unexpected metrics that are correlated with the selected metric using the scroll bar 1916"; The user is provided with an alert and may select the metric and view the correlated metrics).
One of ordinary skill in the art would have been motivated to combine the teachings of Wang with Harutyunyan as they are both related to monitoring computing resource performance, and as such they are within similar environments.
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the teachings of Wang with Harutyunyan in order to provide the users of Wang with additional user interface features allowing for the display of the metrics disclosed by their teachings, in a highly organized manner, thereby facilitating administrators and application owners in troubleshooting problems, and giving them an opportunity to timely correct such problems (Harutyunyan, [0003]).
Claim 10 recites a method with limitations that are substantially similar to the limitations of claim 1. Wang and Harutyunyan disclosed such a method as shown by the above mappings. Claim 19 recites a computer program product comprising a non-transitory computer readable storage medium having a program code embodied therewith, the program code executable by a computing system to cause the computing system to perform limitations that are substantially similar to the limitations of claim 1. Wang and Harutyunyan disclosed a medium performing the teachings of the invention (Wang, col. 22, lines 48-65). As such, claims 10 and 19 are rejected under the same rationale applied above.
Regarding claims 2 and 11, Wang and Harutyunyan disclosed the system of claim 1, wherein metric data includes other performance information received from the enterprise monitoring system (Wang, col. 5, lines 12-38, and lines 55-67, agents of the monitoring system provide various monitoring data; See also col. 1, lines 15-30 the intended environment includes companies and their infrastructure, i.e. virtual infrastructure; See also Fig. 2, 210). See motivation to combine above.
Regarding claims 3 and 12, Wang and Harutyunyan disclosed the system of claim 1, wherein the metric data includes analysis of other performance metric data received from the enterprise monitoring system (Wang, col. 5, lines 39-55, Wang disclosed the monitoring data includes “performance data” of the monitored resources; col. 5, lines 55-60, “process the monitoring data”; col. 6, lines 25-35, “information from a physical environment can be gathered from one or more monitored resources in the physical environment layer 210 and transformed through the data collection layer 220 into a real-time topology model (the topology model layer 230) that is capable of dynamically representing the complex interconnected nature of the computing environment”; Such transforming therefore includes analyzing the performance data received from the enterprise monitoring system).
Regarding claims 4 and 13, Wang and Harutyunyan disclosed the system of claim 1, wherein the performing outside of nominal thresholds comprises changes in metric parameters over time are outside of a nominal threshold (Wang, col. 5, lines 50-55, “This performance data can also include alarms or alerts that indicate whether certain monitored resource 102 characteristics are outside of established performance criteria.” col. 21, lines 29-55, Wang disclosed analyzing the dependency graph and “send[ing] an alarm to a user if a dependency in the filtered dependency graph has changed (e.g., without permission). The custom filter module 146 can provide data representing a comparison between a previous and current dependency graph view”; col. 5, lines 39-50, Wang specifies metrics of such characteristics, such as “virtual machines per physical host, virtual machine memory allocations, processor load, memory load, remaining free storage, network bandwidth, network latency, or any of a variety of other parameters”; Wang’s monitoring of resources/objects in real-time to produce alerts that indicate when a change of monitored resource characteristics results in monitored resource 102 characteristics being outside of established performance criteria, i.e. changes over time; See also col. 21, lines 35-55 of Wang, disclosing changes over time)
Regarding claims 5 and 14, Wang and Harutyunyan disclosed the system of claim 1, wherein the alert to the user interface provides a result of the analysis is performing outside of nominal threshold, provide at the result of an analysis of metrics data regarding the first object (Wang, col. 5, lines 39-55, Wang disclosed the monitoring data includes “performance data” of the monitored resources; col. 5, lines 55-60, “process the monitoring data”; col. 6, lines 25-35, “information from a physical environment can be gathered from one or more monitored resources in the physical environment layer 210 and transformed through the data collection layer 220 into a real-time topology model (the topology model layer 230) that is capable of dynamically representing the complex interconnected nature of the computing environment”; Such transforming therefore includes analyzing the performance data received from the enterprise monitoring system; col. 5, lines 50-55, “This performance data can also include alarms or alerts that indicate whether certain monitored resource 102 characteristics are outside of established performance criteria.” col. 21, lines 29-55, Wang disclosed analyzing the dependency graph and “send[ing] an alarm to a user if a dependency in the filtered dependency graph has changed (e.g., without permission;)”).
Regarding claims 6 and 15, Wang and Harutyunyan disclosed the system of claim 1, wherein the metrics data performing outside of nominal thresholds may be two or more performance metrics for the same object of the enterprise system (Wang, col. 5, lines 50-55, “This performance data can also include alarms or alerts that indicate whether certain monitored resource 102 characteristics are outside of established performance criteria”, and therefore includes “two or more” as claimed.).
Claim(s) 7-9, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 9557879) in view of Harutyunyan et al. (US 20210218619) and further in view of Hsiao et al. (US 11106442).
Regarding claims 7 and 16, Wang and Harutyunyan disclosed the system of claim 1, but did not explicitly disclose wherein at least one of the nominal thresholds changes dynamically.
In an analogous art, Hsiao disclosed wherein at least one of the nominal thresholds changes dynamically (Hsiao, col. 73, lines 14-29, and line 64 through col. 74, lines 26, Hsaio disclosed a user interface including a threshold value field to which, “The threshold field can comprises a representation of a slider bar. User manipulation of the representation of the slider bar can permit the user to change the threshold value and can cause dynamic adjustment of the status indicator for the representation of each entity responsive to the changed threshold value; col. 74, lines 43-47, “Example processes and user interfaces for dynamically selecting thresholds and metrics for real-time or near real-time status update of the monitored entities are described in further detail with respect to FIGS. 71-77”).
One of ordinary skill in the art would have been motivated to combine the teachings of Hsiao with the combined teachings of Wang and Harutyunyan as they all relate to the monitoring of resources in a networking environment. Furthermore, Hsiao explicitly suggests the teachings applied with respect to entity relationships represented as directed graphs, just as service dependency relationships (col. 162, lines 56-64), and therefore explicitly suggest such teachings implemented with similar systems as Wang and Harutyunyan.
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the teachings of Hsiao within the combined teachings of Wang and Harutyunyan in order to provide processing of large amounts of monitoring data in an intelligent manner and effectively presenting results of such processing to users (Hsiao, col. 1, lines 30-38) thereby increasing customer desirability of use of the system.
Regarding claims 8 and 17, Wang and Harutyunyan disclosed the system of claim 1, but did not explicitly disclose wherein at least one of the nominal thresholds is received from the user interface.
In an analogous art, Hsiao disclosed wherein at least one of the nominal thresholds is received from the user interface (Hsiao, col. 73, lines 14-29, and line 64 through col. 74, lines 26, Hsaio disclosed a user interface including a threshold value field to which, “The threshold field can comprises a representation of a slider bar. User manipulation of the representation of the slider bar can permit the user to change the threshold value and can cause dynamic adjustment of the status indicator for the representation of each entity responsive to the changed threshold value; col. 74, lines 43-47, “Example processes and user interfaces for dynamically selecting thresholds and metrics for real-time or near real-time status update of the monitored entities are described in further detail with respect to FIGS. 71-77”).
One of ordinary skill in the art would have been motivated to combine the teachings of Hsiao with the combined teachings of Wang and Harutyunyan as they all relate to the monitoring of resources in a networking environment. Furthermore, Hsiao explicitly suggests the teachings applied with respect to entity relationships represented as directed graphs, just as service dependency relationships (col. 162, lines 56-64), and therefore explicitly suggest such teachings implemented with similar systems as Wang and Harutyunyan.
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the teachings of Hsiao within the combined teachings of Wang and Harutyunyan in order to provide processing of large amounts of monitoring data in an intelligent manner and effectively presenting results of such processing to users (Hsiao, col. 1, lines 30-38) thereby increasing customer desirability of use of the system.
Regarding claims 9 and 18, Wang Harutyunyan disclosed the system of claim 1, but did not explicitly disclose wherein the instructions are further configured to control the one or more processors to, if performance metrics from the network data for all of the objects in the object subset are performing within nominal thresholds, provide an indication that the objects are performing nominally.
Hsiao disclosed wherein the instructions are further configured to control the one or more processors to, if performance metrics data from the network data for all of the objects in the object subset are performing within nominal thresholds, provide an indication that the objects are performing nominally (Hsiao, col. 72, lines 45-50, Hsiao disclosed the EMS can process metrics with respect to the threshold and display the tiles in distinctive colors, for example, such as red for entities that meet the threshold and as green for entities that do not meet the threshold).
One of ordinary skill in the art would have been motivated to combine the teachings of Hsiao with the combined teachings of Wang and Harutyunyan as they all relate to the monitoring of resources in a networking environment. Furthermore, Hsiao explicitly suggests the teachings applied with respect to entity relationships represented as directed graphs, just as service dependency relationships (col. 162, lines 56-64), and therefore explicitly suggest such teachings implemented with similar systems as Wang and Harutyunyan.
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the teachings of Hsiao within the combined teachings of Wang and Harutyunyan in order to provide processing of large amounts of monitoring data in an intelligent manner and effectively presenting results of such processing to users (Hsiao, col. 1, lines 30-38) thereby increasing customer desirability of use of the system.
Response to Arguments
The previous 35 USC 112(a) rejections have been withdrawn in view of the amendments made in Applicant’s response, filed 1/20/2026.
Applicant does not appear to present arguments with respect to the claim amendments made in the Applicant’s response, filed 1/20/2026. Applicant notes that the Examiner previously indicated claims 1-19 allowable with respect to the Office Action filed 11/18/2025. It is noted that claims 1-19, as amended in Applicant’s response filed 1/20/2026, change the scope of the invention, rendering the previous indication of allowable subject matter moot.
It is the Examiner’s position that Applicant has not yet submitted claims drawn to limitations, which define the operation and apparatus of Applicant’s disclosed invention in manner, which distinguishes over the prior art.
Failure for Applicant to significantly narrow definition/scope of the claims and supply arguments commensurate in scope with the claims implies the Applicant intends broad interpretation be given to the claims. The Examiner has interpreted the claims with scope parallel to the Applicant in the response and reiterates the need for the Applicant to more clearly and distinctly define the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dagan (US 9459942) disclosed correlation of metrics monitored from a virtual environment across a plurality of devices for irregularities exceeding a threshold (Dagan, col. 2, lines 25-45).
Burnett et al. (US 11429627) disclosed a user interface depicting a representation of various metrics and interdependencies including automatic changes (col. 58, lines 10-25, col. 61, line 42 through col. 62, line 5).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY B DENNISON whose telephone number is (571)272-3910. The examiner can normally be reached M-F 8:30-5:50.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hadi Armouche can be reached at 571-270-3618. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JERRY B DENNISON/Primary Examiner, Art Unit 2409